128 results on '"Shameer K"'
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2. Foraging Behaviour
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Fellowes, Mark D. E., van Alphen, Jacques J. M., Shameer, K. S., Hardy, Ian C. W., Wajnberg, Eric, Jervis, Mark A., Hardy, Ian C.W., editor, and Wajnberg, Eric, editor
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- 2023
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Catalog
3. The Life-Cycle
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Jervis, Mark A., Copland, Michael J. W., Shameer, K. S., Harvey, Jeffrey A., Hardy, Ian C.W., editor, and Wajnberg, Eric, editor
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- 2023
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4. A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation.
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Shameer K. Mohammed, Supriya M. Hariharan, and Suraj Kamal
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- 2018
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5. The Political Budget Cycle: Evidence from Indian Municipal Corporation Elections
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Shameer, K. Muhammed and Durai, S. Raja Sethu
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The political budget cycle (PBC) theory deals with increased spending or decreased revenue collection, or a combination of both, on the verge of an election by the incumbent government to retain office. Empirical verification of the PBC theory at the subnational local government level is scarce in the literature. Subnational local governments are more prominent in population and budget for a country like India. This study takes into account 34 municipal corporations in India to examine the PBC theory and find strong evidence of politically motivated cycles on the budget expenditure and revenue front. Notably, the more visible expenditure on welfare schemes shows increased spending during an election period. On the revenue side, this study also finds evidence in support of the PBC theory. Indian municipal corporations are creating ‘welfare’ and ‘infrastructure’ cycles during the election period to gain political profit and are validating the ‘visibility’ and ‘targetability’ hypotheses described in the literature. This study is the first attempt to trace the presence of the PBC at the subnational local government level in India. more...
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- 2024
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6. Direct and indirect influences of intercrops on the coconut defoliator Opisina arenosella
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Shameer, K. S., Nasser, M., Mohan, Chandrika, and Hardy, Ian C. W.
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- 2017
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7. Glucosylceramide synthase modulation ameliorates murine renal pathologies and promotes macrophage effector function in vitro
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Agnes Cheong, Florin Craciun, Hervé Husson, Joseph Gans, Javier Escobedo, Yi-Chien Chang, Lilu Guo, Mariana Goncalves, Nathan Kaplan, Laurie A. Smith, Sarah Moreno, Joseph Boulanger, Shiguang Liu, Jacqueline Saleh, Mindy Zhang, Anna S. Blazier, Weiliang Qiu, Andrew Macklin, Tejaswi Iyyanki, Clément Chatelain, Shameer Khader, Thomas A. Natoli, Oxana Ibraghimov-Beskrovnaya, Dimitry Ofengeim, and Jonathan D. Proto more...
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Biology (General) ,QH301-705.5 - Abstract
Abstract While significant advances have been made in understanding renal pathophysiology, less is known about the role of glycosphingolipid (GSL) metabolism in driving organ dysfunction. Here, we used a small molecule inhibitor of glucosylceramide synthase to modulate GSL levels in three mouse models of distinct renal pathologies: Alport syndrome (Col4a3 KO), polycystic kidney disease (Nek8 jck ), and steroid-resistant nephrotic syndrome (Nphs2 cKO). At the tissue level, we identified a core immune-enriched transcriptional signature that was shared across models and enriched in human polycystic kidney disease. Single nuclei analysis identified robust transcriptional changes across multiple kidney cell types, including epithelial and immune lineages. To further explore the role of GSL modulation in macrophage biology, we performed in vitro studies with homeostatic and inflammatory bone marrow-derived macrophages. Cumulatively, this study provides a comprehensive overview of renal dysfunction and the effect of GSL modulation on kidney-derived cells in the setting of renal dysfunction. more...
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- 2024
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8. Design, Synthesis, Antioxidant and Anticancer Activity of Novel Schiff’s Bases of 2-Amino Benzothiazole
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Saipriya Saipriya D, Arun Prakash, Suvarna G Kini, Varadaraj Bhatt G, K Sreedhara Ranganath Pai, Subhankar Biswas, and Mohammed Shameer K
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chemistry.chemical_classification ,Antioxidant ,biology ,medicine.medical_treatment ,Protein Data Bank (RCSB PDB) ,02 engineering and technology ,021001 nanoscience & nanotechnology ,biology.organism_classification ,Aldehyde ,Combinatorial chemistry ,HeLa ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,chemistry ,Benzothiazole ,Docking (molecular) ,030220 oncology & carcinogenesis ,medicine ,Proton NMR ,Molecule ,General Pharmacology, Toxicology and Pharmaceutics ,0210 nano-technology - Abstract
Introduction: Around 5,00,000 women are affected by cervical cancer and nearly half of them end up losing the battle of life with this deadly disease. So, there is an urgent need for the synthesis and development of new, small, synthetic molecules to tackle this challenge. Schiff’s bases are derivatives of azomethine group (-CH=N-) and are highly reactive. Here a series of novel Schiff’s bases were synthesized by single step process of condensing substituted 2-amino benzothiazole with different benzaldehydes. Objectives: To design, synthesize novel Schiff’s bases of 2-amino benzothiazole and evaluate its anti-oxidant as well as anti-cancer activity. Methods: A total of 18 compounds were synthesized by single step process of condensing substituted 2-amino benzothiazole with different substituted benzaldehydes. These were characterized by FTIR, 1H NMR, and Mass spectroscopy. The synthesized compounds were tested in-vitro for both antioxidant and antiproliferative activity. In-silico docking studies were performed on the crystal structure of the complex of caspase-3 with a nicotinic acid aldehyde inhibitor with PDB IDs 1RE1, 1RHM and 3DEH to study the interaction of the compounds with the receptor. Results: Majority of the derivatives displayed moderate to significant antiproliferative activity on HeLa cell line. Interestingly, the compound SP16 showed excellent activity with an IC50 value of 2.517μg/ml in comparison to the reference compound Cisplatin (17.2μg/ml). Compound SP7 and SP 15 showed favourable in silico interactions.Conclusion: A series of 18 novel Schiff’s bases of 2-amino benzothiazoles compounds were designed, synthesized and evaluated for their biological activities. The compound SP16 showed excellent activity with an IC50 value of 2.517μg/ml in comparison to the reference compound Cisplatin and Compound SP7 and SP 15 showed favourable in silico interactions. more...
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- 2018
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9. A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation
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Supriya M. Hariharan, Suraj Kamal, and Shameer K. Mohammed
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Article Subject ,Computer science ,Feature extraction ,02 engineering and technology ,01 natural sciences ,Sonar ,Automatic target recognition ,Robustness (computer science) ,0103 physical sciences ,lcsh:Technology (General) ,0202 electrical engineering, electronic engineering, information engineering ,Fading ,Electrical and Electronic Engineering ,Hidden Markov model ,010301 acoustics ,Instrumentation ,Rayleigh fading ,business.industry ,020206 networking & telecommunications ,Pattern recognition ,ComputingMethodologies_PATTERNRECOGNITION ,Control and Systems Engineering ,lcsh:T1-995 ,Artificial intelligence ,business ,Classifier (UML) - Abstract
Underwater acoustic target classifiers are found to have many applications in military and security areas where a higher degree of prediction accuracy is needed that makes classifier efficiency and reliability an interesting subject. Classifiers are often trained with known acoustic target specimens with their characteristic feature set and tested with measurements obtained from the sonar that is deployed in the surveillance or observation zone. The selection of source-specific deterministic features in automatic target recognition (ATR) system is very significant, since it determines the reliability, efficiency, and success rate of the classifier. The robustness of the gammatone cepstral coefficients (GTCC) in combination with the statistical Euclidean distance, artificial neural network (ANN), and hidden Markov model (HMM) classifiers has been investigated, and its performance is compared with that of other feature extraction schemes. The classifier performance has been analyzed in Rayleigh fading conditions, based on which the performance is enhanced by incorporating an autoregressive (AR) Rayleigh fading channel compensation. The performance of the classifier in different operating conditions is investigated, with underwater target signals consisting of the real field data collected during expedition, and the results are presented in this paper. more...
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- 2018
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10. A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation
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Mohammed, Shameer K., primary, Hariharan, Supriya M., additional, and Kamal, Suraj, additional
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- 2018
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11. Deep learning architectures for underwater target recognition
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Shameer K. Mohammed, P. R. Saseendran Pillai, M. H. Supriya, and Suraj Kamal
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Feature engineering ,Engineering ,Discriminative model ,business.industry ,Deep learning ,Feature extraction ,Pattern recognition ,Artificial intelligence ,Invariant (mathematics) ,Underwater ,business ,Sonar ,Feature learning - Abstract
Passive sonar target recognition is a challenging task due to the complex milieu of the ocean. Most of the state of the art target recognition systems depend on hand engineered feature extraction schemes in order to effectively represent the target signatures, based on expert knowledge. Due to the whimsical nature of the sources and medium, such feature engineering methods often fail to yield invariant features from the observations. In this paper, a deep unsupervised feature learning approach capable of capturing invariant features from the sensory signal stream through multi layered hierarchical abstraction has been adopted. These abstractions learned by the higher layers are mostly invariant and can be used as the discriminative features for the purpose of classification. more...
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- 2013
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12. Direct and indirect influences of intercrops on the coconut defoliator Opisina arenosella.
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Shameer, K. S., Nasser, M., Mohan, Chandrika, and Hardy, Ian C. W.
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CATCH crops , *COCONUT palm diseases & pests , *INSECT-host relationships , *PLANT species , *INSECT-plant relationships , *PLANTATIONS - Abstract
Coconut palm ( Cocos nucifera) infestation by Opisina arenosella (Lepidoptera: Oecophoridae) in the Indian subcontinent may occur in November to May each year in the same or adjoining areas of plantations. Parasitoids of O. arenosella may also be consistently present at these times. During other periods, pests and/or parasitoids could be maintained on intercrops that are commonly grown throughout the year. Field surveys of 54 intercrop species in Kerala, India, found that O. arenosella attacks banana, but not others, while laboratory screening showed that O. arenosella can mature on jack fruit, cashew and oil palm. Larvae of 20 lepidopteran species found on intercrops were screened for use by Goniozus nephantidis (Hymenoptera: Bethylidae), a larval parasitoid of O. arenosella, which oviposited on two species but its offspring failed to mature. Thirteen intercrop herbivore species were screened for use by Brachymeria nosatoi (Hymenoptera: Chalcididae), a pupal parasitoid of O. arenosella, which completed development on the pyralids Herculia nigrivita, Syllepte derogata and Psara basalis. Further, connectance trophic webs were compiled using prior field records of coconut, 33 species of intercrops, 58 species of lepidopteran herbivores and 29 species of primary parasitoids. Both laboratory and literature evidence suggests that populations of O. arenosella are unlikely to be maintained by feeding on intercrops or strongly influenced by direct competition with other lepidopterans but are likely to be affected by sharing parasitoids. Intercrop herbivores have clear potential for maintaining parasitoids of O. arenosella, and we recommend thirteen plant species as intercrops that should aid in conservation biocontrol. [ABSTRACT FROM AUTHOR] more...
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- 2018
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13. Food webs and date palm agro-ecological community characteristics.
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Shameer, K. S., Almandhari, T., and Hardy, I.
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DATE palm , *SHELF-life dating of food , *FOOD chains , *ONLINE dating , *BIOTIC communities - Abstract
Understanding the inter-organism interactions in ecological communities allows assessment, and even prediction, of how communities respond to natural and anthropomorphic impacts. The trophic interrelationships of invertebrates within a community often form an extensive feeding web composed of several trophic levels. Food webs, or trophic webs, can be constructed directly from empirical field studies or, alternatively, by using literature records to map all the trophic interrelationships in natural and agro-ecosystems. For terrestrial communities, this includes the plants, herbivores and the complex of natural enemies associated with these herbivores. The food webs that were recently constructed for coconut palm and date palm agroecosystems infer large-scale community ecology consequences of shared and nonshared natural enemies, such as the relative importance of direct and apparent competition. The plant-insect community characteristics of date palm agro-ecosystem can be explained with emphasis on the prospects of better pest management strategies against Lesser Date Moth, Batrachedra amydraula Meyrick. [ABSTRACT FROM AUTHOR] more...
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- 2023
14. Postdischarge-to-30-Day Mortality Among Patients Receiving MitraClip: A Systematic Review and Meta-Analysis
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Beni R. Verma, MD, Shashank Shekhar, MD, Toshiaki Isogai, MD, Raghuram Chava, MD, Pejman Raeisi-Giglou, DO, Agam Bansal, MD, Shameer Khubber, MD, Bryce Montane, MD, Prashansha Vaidya, MD, Simrat Kaur, MD, Manpreet Kaur, MD, Rhonda Miyasaka, MD, Serge C. Harb, MD, Amar Krishnaswamy, MD, and Samir R. Kapadia, MD more...
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Heart failure ,Mitral valve disease ,Mortality ,Percutaneous valve therapy ,Structural heart disease intervention ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Background: MitraClip (MC) implantation is the recommended treatment for severe symptomatic mitral regurgitation in patients not responding to medical therapy and at prohibitive surgical risk. It is important to quantify immediate mortality during postdischarge-to-30-day period so as to improve the procedural outcomes. Hence, we aim to identify the incidence of postdischarge-to-30-day mortality and its associated predictors using the technique of meta-analysis. Methods: We searched Medline, Embase, and Cochrane CENTRAL databases from inception until July 3, 2019 for studies reporting mortality prior to discharge, at 30 days and 1 year after MC implantation. The primary outcome was postdischarge-to-30-day all-cause mortality. Results: Of 2394 references, 15 studies enrolling 7498 patients were included. Random effects analysis showed that all-cause cumulative inpatient, 30-day, and 1-year mortality was 2.40% (2.08, 2.77; I2 = 0%), 4.31% (3.64, 5.09, I2 = 41.9%), and 20.71% (18.32; 23.33, I2 = 81.5%), respectively. The postdischarge-to-30-day mortality was 1.70% (95% confidence interval: 1.0, 2.70; I2 = 84%). A total of 71.50% of deaths (95% confidence interval: 36.80-91.50, I2 = 63%) in the postdischarge-to-30-day period were due to cardiac etiology. On meta-regression, pre-MC left ventricular ejection fraction (p = 0.003), Log.Euroscore (p = 0.047), Society of Thoracic Surgeons Predicted Risk of Mortality (p < 0.001), and prolonged ventilation >48 hours (p < 0.001) were found to be its significant predictors. Conclusions: Our meta-analysis reports an additional mortality of ∼2% immediately after MC implantation during the postdischarge-to-30-day period. Majority of deaths occurred due to cardiac causes. Pre-MC left ventricular ejection fraction, Log.Euroscore, Society of Thoracic Surgeons Predicted Risk of Mortality score, and prolonged ventilation were found to be its significant predictors. Further studies are needed to better understand the causes of this early mortality to maximize benefits of this important therapy. more...
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- 2022
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15. A Qualitative Study to Explore the Determinants of Risky Sexual Behaviors and Pregnancy among Female Adolescents in Sabah, Malaysia
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Idayu Badilla Idris, Shameer Khan Bin Sulaiman, Rozita Hod, Hamed Khazaei, and Nik Nairan Abdullah
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Gynecology and obstetrics ,RG1-991 - Abstract
This investigation was performed in Kota Kinabalu, Sabah state, where the highest number of pregnancies is recorded. The purpose of this study was to determine variables associated with hazardous sexual activity and adolescent pregnancy in Sabah, Malaysia. The findings indicate that familial variables, peer interactions, self-esteem, psychiatric concerns, economic considerations, and sex knowledge all play a significant role in hazardous sexual conduct and adolescent pregnancy in Sabah, Malaysia. Information obtained from this study will help the Malaysian government and other officials to design and establish proper interventions that will help alleviate the challenge of high prevalence of teenage pregnancy. It is suggested that sex education be included in the high school curriculum, along with physical and health education in Sabah, Malaysia. more...
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- 2022
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16. Deep learning architectures for underwater target recognition
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Kamal, Suraj, primary, Mohammed, Shameer K., additional, Pillai, P. R. Saseendran, additional, and Supriya, M. H., additional
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- 2013
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17. 3DSwap: curated knowledgebase of proteins involved in 3D domain swapping
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Shameer, K., primary, Shingate, P. N., additional, Manjunath, S. C. P., additional, Karthika, M., additional, Pugalenthi, G., additional, and Sowdhamini, R., additional
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- 2011
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18. STIFDB—Arabidopsis Stress Responsive Transcription Factor DataBase
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Shameer, K., primary, Ambika, S., additional, Varghese, Susan Mary, additional, Karaba, N., additional, Udayakumar, M., additional, and Sowdhamini, R., additional
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- 2009
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19. HARMONY: a server for the assessment of protein structures
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Pugalenthi, G., primary, Shameer, K., additional, Srinivasan, N., additional, and Sowdhamini, R., additional
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- 2006
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20. Coronary artery aneurysms: outcomes following medical, percutaneous interventional and surgical management
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Douglas Johnston, Beni Rai Verma, Shashank Shekhar, Manpreet Kaur, Muhammad Shahzeb Khan, Chandramohan Meenakshisundaram, Samir Kapadia, Kinjal Banerjee, Safi Khan, Ankur Kalra, Shameer Khubber, Rajdeep Chana, Kamal Dhaliwal, Mohomed Gad, Muhummad Zia Khan, Yasser Sammour, Rayji Tsutsui, Rishi Puri, Faisal G Bakaeen, Conrad Simpfendorfer, Stephen Ellis, and Gosta Pettersson more...
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Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Background Coronary artery aneurysms (CAAs) are increasingly diagnosed on coronary angiography; however, controversies persist regarding their optimal management. In the present study, we analysed the long-term outcomes of patients with CAAs following three different management strategies.Methods We performed a retrospective review of patient records with documented CAA diagnosis between 2000 and 2005. Patients were divided into three groups: medical management versus percutaneous coronary intervention (PCI) versus coronary artery bypass grafting (CABG). We analysed the rate of major cardiovascular and cerebrovascular events (MACCEs) over a period of 10 years.Results We identified 458 patients with CAAs (mean age 78±10.5 years, 74.5% men) who received medical therapy (N=230) or underwent PCI (N=52) or CABG (N=176). The incidence of CAAs was 0.7% of the total catheterisation reports. The left anterior descending was the most common coronary artery involved (38%). The median follow-up time was 62 months. The total number of MACCE during follow-up was 155 (33.8%); 91 (39.6%) in the medical management group vs 46 (26.1%) in the CABG group vs 18 (34.6%) in the PCI group (p=0.02). Kaplan-Meier survival analysis showed that CABG was associated with better MACCE-free survival (p log-rank=0.03) than medical management. These results were confirmed on univariate Cox regression, but not multivariate regression (OR 0.773 (0.526 to 1.136); p=0.19). Both Kaplan-Meier survival and regression analyses showed that dual antiplatelet therapy (DAPT) and anticoagulation were not associated with significant improvement in MACCE rates.Conclusion Our analysis showed similar long-term MACCE risks in patients with CAA undergoing medical, percutaneous and surgical management. Further, DAPT and anticoagulation were not associated with significant benefits in terms of MACCE rates. These results should be interpreted with caution considering the small size and potential for selection bias and should be confirmed in large, randomised trials. more...
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- 2021
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21. A Sequence Data Mining Protocol to Identify Best Representative Sequence for Protein Domain Families.
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Gowri, V.S., Shameer, K., Reddy, C.C.S., Shingate, P., and Sowdhamini, R.
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- 2010
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22. 663 Correlation between early endpoints and overall survival in non-small-cell lung cancer: a trial-level meta-analysis
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Faisal Khan, Shameer Khader, Youyi Zhang, Daniel Jackson, Kirsty Rhodes, Imran Khan Anwer Neelufer, Sreenath Nampally, Andrzej Prokop, Emmette Hutchison, Jiabu Ye, Feng Liu, Antony Sabin, James Weatherall, Cristina Duran, Renee Iacona, and Pralay Mukhopadhyay more...
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2020
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23. A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease
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Shameer K, Dow G, Benjamin Glicksberg, Kw, Johnson, Ze Y, Ms, Tomlinson, Readhead B, Jt, Dudley, and Ij, Kullo
24. PeptideMine - A webserver for the design of peptides for protein-peptide binding studies derived from protein-protein interactomes
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Gopal Balasubramanian, Veeranna Shivamurthy, Madan Lalima L, Shameer Khader, and Sowdhamini Ramanathan
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Signal transduction events often involve transient, yet specific, interactions between structurally conserved protein domains and polypeptide sequences in target proteins. The identification and validation of these associating domains is crucial to understand signal transduction pathways that modulate different cellular or developmental processes. Bioinformatics strategies to extract and integrate information from diverse sources have been shown to facilitate the experimental design to understand complex biological events. These methods, primarily based on information from high-throughput experiments, have also led to the identification of new connections thus providing hypothetical models for cellular events. Such models, in turn, provide a framework for directing experimental efforts for validating the predicted molecular rationale for complex cellular processes. In this context, it is envisaged that the rational design of peptides for protein-peptide binding studies could substantially facilitate the experimental strategies to evaluate a predicted interaction. This rational design procedure involves the integration of protein-protein interaction data, gene ontology, physico-chemical calculations, domain-domain interaction data and information on functional sites or critical residues. Results Here we describe an integrated approach called "PeptideMine" for the identification of peptides based on specific functional patterns present in the sequence of an interacting protein. This approach based on sequence searches in the interacting sequence space has been developed into a webserver, which can be used for the identification and analysis of peptides, peptide homologues or functional patterns from the interacting sequence space of a protein. To further facilitate experimental validation, the PeptideMine webserver also provides a list of physico-chemical parameters corresponding to the peptide to determine the feasibility of using the peptide for in vitro biochemical or biophysical studies. Conclusions The strategy described here involves the integration of data and tools to identify potential interacting partners for a protein and design criteria for peptides based on desired biochemical properties. Alongside the search for interacting protein sequences using three different search programs, the server also provides the biochemical characteristics of candidate peptides to prune peptide sequences based on features that are most suited for a given experiment. The PeptideMine server is available at the URL: http://caps.ncbs.res.in/peptidemine more...
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- 2010
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25. 3PFDB - A database of Best Representative PSSM Profiles (BRPs) of Protein Families generated using a novel data mining approach
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Shameer Khader, Nagarajan Paramasivam, Gaurav Kumar, and Sowdhamini Ramanathan
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Analysis ,QA299.6-433 - Abstract
Abstract Background Protein families could be related to each other at broad levels that group them as superfamilies. These relationships are harder to detect at the sequence level due to high evolutionary divergence. Sequence searches are strongly directed and influenced by the best representatives of families that are viewed as starting points. PSSMs are useful approximations and mathematical representations of protein alignments, with wide array of applications in bioinformatics approaches like remote homology detection, protein family analysis, detection of new members and evolutionary modelling. Computational intensive searches have been performed using the neural network based sensitive sequence search method called FASSM to identify the Best Representative PSSMs for families reported in Pfam database version 22. Results We designed a novel data mining approach for the assessment of individual sequences from a protein family to identify a single Best Representative PSSM profile (BRP) per protein family. Using the approach, a database of protein family-specific best representative PSSM profiles called 3PFDB has been developed. PSSM profiles in 3PFDB are curated using performance of individual sequence as a reference in a rigorous scoring and coverage analysis approach using FASSM. We have assessed the suitability of 10, 85,588 sequences derived from seed or full alignments reported in Pfam database (Version 22). Coverage analysis using FASSM method is used as the filtering step to identify the best representative sequence, starting from full length or domain sequences to generate the final profile for a given family. 3PFDB is a collection of best representative PSSM profiles of 8,524 protein families from Pfam database. Conclusion Availability of an approach to identify BRPs and a curated database of best representative PSI-BLAST derived PSSMs for 91.4% of current Pfam family will be a useful resource for the community to perform detailed and specific analysis using family-specific, best-representative PSSM profiles. 3PFDB can be accessed using the URL: http://caps.ncbs.res.in/3pfdb more...
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- 2009
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26. PURE: A webserver for the prediction of domains in unassigned regions in proteins
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Offmann Bernard O, Shameer Khader, Reddy Chilamakuri CS, and Sowdhamini Ramanathan
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Protein domains are the structural and functional units of proteins. The ability to parse proteins into different domains is important for effective classification, understanding of protein structure, function, and evolution and is hence biologically relevant. Several computational methods are available to identify domains in the sequence. Domain finding algorithms often employ stringent thresholds to recognize sequence domains. Identification of additional domains can be tedious involving intense computation and manual intervention but can lead to better understanding of overall biological function. In this context, the problem of identifying new domains in the unassigned regions of a protein sequence assumes a crucial importance. Results We had earlier demonstrated that accumulation of domain information of sequence homologues can substantially aid prediction of new domains. In this paper, we propose a computationally intensive, multi-step bioinformatics protocol as a web server named as PURE (Prediction of Unassigned REgions in proteins) for the detailed examination of stretches of unassigned regions in proteins. Query sequence is processed using different automated filtering steps based on length, presence of coiled-coil regions, transmembrane regions, homologous sequences and percentage of secondary structure content. Later, the filtered sequence segments and their sequence homologues are fed to PSI-BLAST, cd-hit and Hmmpfam. Data from the various programs are integrated and information regarding the probable domains predicted from the sequence is reported. Conclusion We have implemented PURE protocol as a web server for rapid and comprehensive analysis of unassigned regions in the proteins. This server integrates data from different programs and provides information about the domains encoded in the unassigned regions. more...
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- 2008
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27. Poster: HORIBALFRE: Higher Order Residue Interactions Based ALgorithm for Fold REcognition.
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Sundaramurthy, P., Sreenivasan, R., Shameer, K., Gakkhar, S., and Sowdhamini, R.
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- 2011
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28. Host-parasitoid trophic webs in complex agricultural systems.
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Shameer KS and Hardy IC
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- Animals, Plants parasitology, Pest Control, Biological, Herbivory, Food Chain, Insecta physiology, Host-Parasite Interactions, Agriculture
- Abstract
The composition and dynamics of ecological communities are complex because of the presence of large numbers of organisms, belonging to many different species, each with their own evolutionary history, and their numerous interactions. The construction and analysis of trophic webs summarize interactions across trophic levels and link community structure to properties such as ecosystem services. We focus on agroecological communities, which may be simpler than natural communities but nonetheless present considerable challenges to describe and understand. We review the characteristics and study of communities comprised of plants, phytophagous insects, and insect parasitoids with particular regard to the maintenance of sustainable agroecological communities and ecosystem services, especially biological pest control. We are constrained to largely overlook other members of these communities, such as hyperparasitoids, predators, parasites, and microbes. We draw chiefly on recent literature while acknowledging the importance of many advances made during the immediately preceding decades. Trophic web construction and analysis can greatly improve the understanding of the role and impact of herbivores and natural enemies in agroecological communities and the various species interactions, such as apparent competition, which assists biocontrol strategies. The study of trophic webs also helps in predicting community ecology consequences of externally driven changes to agroecosystems., Competing Interests: Declaration of Competing Interest K.S.S. and I.C.W.H. have no inappropriately influencing interests to declare. We apologize for not being able to mention more of the earlier literature and other excellent contributions to this research field., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.) more...
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- 2024
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29. Genetic analyses of inflammatory polyneuropathy and chronic inflammatory demyelinating polyradiculoneuropathy identified candidate genes.
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Du Z, Lessard S, Iyyanki T, Chao M, Hammond T, Ofengeim D, Klinger K, de Rinaldis E, Shameer K, and Chatelain C
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- Humans, Female, Male, Polymorphism, Single Nucleotide, Case-Control Studies, Mendelian Randomization Analysis, Polyradiculoneuropathy, Chronic Inflammatory Demyelinating genetics, Genome-Wide Association Study, Genetic Predisposition to Disease
- Abstract
Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare, immune-mediated disorder in which an aberrant immune response causes demyelination and axonal damage of the peripheral nerves. Genetic contribution to CIDP is unclear and no genome-wide association study (GWAS) has been reported so far. In this study, we aimed to identify CIDP-related risk loci, genes, and pathways. We first focused on CIDP, and 516 CIDP cases and 403,545 controls were included in the GWAS analysis. We also investigated genetic risk for inflammatory polyneuropathy (IP), in which we performed a GWAS study using FinnGen data and combined the results with GWAS from the UK Biobank using a fixed-effect meta-analysis. A total of 1,261 IP cases and 823,730 controls were included in the analysis. Stratified analyses by gender were performed. Mendelian randomization (MR), colocalization, and transcriptome-wide association study (TWAS) analyses were performed to identify associated genes. Gene-set analyses were conducted to identify associated pathways. We identified one genome-wide significant locus at 20q13.33 for CIDP risk among women, the top variant located at the intron region of gene CDH4. Sex-combined MR, colocalization, and TWAS analyses identified three candidate pathogenic genes for CIDP and five genes for IP. MAGMA gene-set analyses identified a total of 18 pathways related to IP or CIDP. Sex-stratified analyses identified three genes for IP among males and two genes for IP among females. Our study identified suggestive risk genes and pathways for CIDP and IP. Functional analyses should be conducted to further confirm these associations., Competing Interests: Declaration of interests All authors were employees of Sanofi US Services at the time of study and hold shares and/or stock options in the company., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.) more...
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- 2024
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30. Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health.
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Roy R, Marakkar S, Vayalil MP, Shahanaz A, Anil AP, Kunnathpeedikayil S, Rawal I, Shetty K, Shameer Z, Sathees S, Prasannakumar AP, Mathew OK, Subramanian L, Shameer K, and Yadav KK
- Subjects
- Humans, Nutrigenomics, Diet, Artificial Intelligence, Food-Drug Interactions, Big Data
- Abstract
The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.) more...
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- 2022
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31. Breakthroughs and Applications of Organ-on-a-Chip Technology.
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Koyilot MC, Natarajan P, Hunt CR, Sivarajkumar S, Roy R, Joglekar S, Pandita S, Tong CW, Marakkar S, Subramanian L, Yadav SS, Cherian AV, Pandita TK, Shameer K, and Yadav KK
- Subjects
- Biocompatible Materials, Microfluidics, Tissue Engineering, Artificial Intelligence, Lab-On-A-Chip Devices
- Abstract
Organ-on-a-chip (OOAC) is an emerging technology based on microfluid platforms and in vitro cell culture that has a promising future in the healthcare industry. The numerous advantages of OOAC over conventional systems make it highly popular. The chip is an innovative combination of novel technologies, including lab-on-a-chip, microfluidics, biomaterials, and tissue engineering. This paper begins by analyzing the need for the development of OOAC followed by a brief introduction to the technology. Later sections discuss and review the various types of OOACs and the fabrication materials used. The implementation of artificial intelligence in the system makes it more advanced, thereby helping to provide a more accurate diagnosis as well as convenient data management. We introduce selected OOAC projects, including applications to organ/disease modelling, pharmacology, personalized medicine, and dentistry. Finally, we point out certain challenges that need to be surmounted in order to further develop and upgrade the current systems. more...
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- 2022
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32. StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit.
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Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, Freeman A, Patel S, Yu W, Huhn M, Sheikh AS, Tan K, Sellman BR, Cohen T, Mangion J, Khan FM, Gusev Y, and Shameer K
- Abstract
Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer., Competing Interests: Authors CL, AP, VG, RNH, EP, AF, SP, WY, MH, A-SS, KT, BS, TC, JM, FMK, and KS are or were employed by AstraZeneca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lee, Lin, Prokop, Gopalakrishnan, Hanna, Papa, Freeman, Patel, Yu, Huhn, Sheikh, Tan, Sellman, Cohen, Mangion, Khan, Gusev and Shameer.) more...
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- 2022
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33. OSPred Tool: A Digital Health Aid for Rapid Predictive Analysis of Correlations Between Early End Points and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.
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Shameer K, Zhang Y, Prokop A, Nampally S, N IKA, Weatherall J, Iacona RB, and Khan FM
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- Clinical Trials, Phase III as Topic, Endpoint Determination, Humans, Progression-Free Survival, Proportional Hazards Models, Antineoplastic Agents therapeutic use, Carcinoma, Non-Small-Cell Lung drug therapy, Lung Neoplasms diagnosis, Lung Neoplasms drug therapy
- Abstract
Purpose: Overall survival (OS) is the gold standard end point for establishing clinical benefits in phase III oncology trials. However, these trials are associated with low success rates, largely driven by failure to meet the primary end point. Surrogate end points such as progression-free survival (PFS) are increasingly being used as indicators of biologic drug activity and to inform early go/no-go decisions in oncology drug development. We developed OSPred, a digital health aid that combines actual clinical data and machine intelligence approaches to visualize correlation trends between early (PFS-based) and late (OS) end points and provide support for shared decision making in the drug development pipeline., Methods: OSPred is based on a trial-level data set of 81 reports (35 anticancer drugs with various mechanisms of action; 156 observations) in non-small-cell lung cancer (NSCLC). OSPred was developed using R Shiny, with packages ggplot2, metafor, boot, dplyr, and mvtnorm, to analyze and visualize correlation results and predict OS hazard ratio (HR OS) on the basis of user-inputted PFS-based data, namely, HR PFS, or the odds ratio of PFS at 4 (OR PFS4) or 6 (OR PFS6) months., Results: The three main features of the tool are as follows: prediction of HR OS on the basis of user-inputted early end point values; visualization of comparisons of the user's investigational drug with other drugs in the NSCLC setting, including by specific MoA; and creation of a probability density chart, providing point prediction and CIs for HR OS. A working version of the tool for download is linked., Conclusion: The OSPred tool offers interactive visualization of clinical trial end point correlations with reference to a large pool of historical NSCLC studies. Its focused capability has the potential to digitally transform and accelerate data-driven decision making as part of the drug development process., Competing Interests: Shameer KhaderEmployment: AstraZenecaPatents, Royalties, Other Intellectual Property: Coauthor of an AI patent for Automation for Clinical Event Adjudication Andrzej ProkopEmployment: AstraZeneca Pharma PolandHonoraria: AstraZeneca Sreenath NampallyEmployment: AstraZeneca/MedImmune, BayerStock and Other Ownership Interests: AstraZeneca Jim WeatherallEmployment: AstraZenecaStock and Other Ownership Interests: AstraZeneca Renee Bailey IaconaEmployment: AstraZenecaStock and Other Ownership Interests: AstraZeneca Faisal M. KhanEmployment: AstraZeneca, Novo NordiskStock and Other Ownership Interests: AstraZeneca, Novo NordiskPatents, Royalties, Other Intellectual Property: Patent applied through AstraZenecaTravel, Accommodations, Expenses: AstraZeneca, Novo NordiskNo other potential conflicts of interest were reported. more...
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- 2022
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34. Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients.
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Vunikili R, Glicksberg BS, Johnson KW, Dudley JT, Subramanian L, and Shameer K
- Abstract
Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We've also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse. Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License., Competing Interests: LS reports being a co-founder of Entrupy Inc, Velai Inc and Gaius Networks Inc and has consulted with the World Bank and the Governance Lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Vunikili, Glicksberg, Johnson, Dudley, Subramanian and Shameer.) more...
- Published
- 2021
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35. High Precision Mammography Lesion Identification From Imprecise Medical Annotations.
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An U, Bhardwaj A, Shameer K, and Subramanian L
- Abstract
Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underlying lesions. The expense of collecting high quality pixel-level data in the field of medical science makes this even more difficult. To surmount this fundamental challenge, we propose LatentCADx, a deep learning segmentation model capable of precisely annotating cancer lesions underlying hand-drawn annotations, which we procedurally obtain using joint classification training and a strict segmentation penalty. We demonstrate the capability of LatentCADx on a publicly available dataset of 2,620 Mammogram case files, where LatentCADx obtains classification ROC of 0.97, AP of 0.87, and segmentation AP of 0.75 (IOU = 0.5), giving comparable or better performance than other models. Qualitative and precision evaluation of LatentCADx annotations on validation samples reveals that LatentCADx increases the specificity of segmentations beyond that of existing models trained on hand-drawn annotations, with pixel level specificity reaching a staggering value of 0.90. It also obtains sharp boundary around lesions unlike other methods, reducing the confused pixels in the output by more than 60 % ., Competing Interests: KS was employed by Northwell Health and KS was employed by AstraZeneca. LS reports being a co-founder of Entrupy Inc, Velai Inc and Gaius Networks Inc and has consulted with the World Bank and the Governance Lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 An, Bhardwaj, Shameer and Subramanian.) more...
- Published
- 2021
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36. Correction to: The role of machine learning in clinical research: transforming the future of evidence generation.
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Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, Freitag DF, Benoit J, Hughes MC, Khan F, Slater P, Shameer K, Roe M, Hutchison E, Kollins SH, Broedl U, Meng Z, Wong JL, Curtis L, Huang E, and Ghassemi M more...
- Published
- 2021
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37. The role of machine learning in clinical research: transforming the future of evidence generation.
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Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, Freitag DF, Benoit J, Hughes MC, Khan F, Slater P, Shameer K, Roe M, Hutchison E, Kollins SH, Broedl U, Meng Z, Wong JL, Curtis L, Huang E, and Ghassemi M more...
- Subjects
- Humans, United States, United States Food and Drug Administration, Artificial Intelligence, Machine Learning
- Abstract
Background: Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum., Results: Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas., Conclusions: ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence., (© 2021. The Author(s).) more...
- Published
- 2021
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38. Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis.
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Shameer K, Zhang Y, Jackson D, Rhodes K, Neelufer IKA, Nampally S, Prokop A, Hutchison E, Ye J, Malkov VA, Liu F, Sabin A, Weatherall J, Duran C, Iacona RB, Khan FM, and Mukhopadhyay P
- Abstract
Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy., Competing Interests: KR, EH, FL, AS, JW, RI, and FK are full-time employees of AstraZeneca and own AstraZeneca stock. SK, YZ, DJ, SN, AP, JY, and CD are full-time employees of AstraZeneca. PM was a full-time employee of AstraZeneca at the time that the study was conducted and owns AstraZeneca stock. IK and VM were full-time employees of AstraZeneca at the time that the study was conducted. The authors declare that this study received funding from AstraZeneca. The funder had the following involvement with the study: Study design and concept, collection, analysis and interpretation of the data, review and approval of the final draft and approval to submit for publication., (Copyright © 2021 Shameer, Zhang, Jackson, Rhodes, Neelufer, Nampally, Prokop, Hutchison, Ye, Malkov, Liu, Sabin, Weatherall, Duran, Iacona, Khan and Mukhopadhyay.) more...
- Published
- 2021
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39. SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.
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Prokop A, Zhang Y, Mukhopadhyay P, Khan F, and Shameer K
- Subjects
- Humans, Carcinoma, Non-Small-Cell Lung drug therapy, Lung Neoplasms drug therapy
- Abstract
Combination therapies are an emerging drug development strategy in cancer, particularly in the immunooncology (IO) space. Many combination studies do not meet their safety objectives due to serious adverse events (SAEs). Prediction of SAEs based on evidence from single and combination studies would be highly beneficial. To address the emerging challenge of optimizing the safety and efficacy of combination studies, we have assembled a novel oncology clinical trial data set with 329 trials, 685 arms (279 unique treatment arms), including 200 combinations, 79 mono arms, and 59 curated adverse event categories in the setting of non-small cell lung cancer (NSCLC). We integrated the database with an analytical framework: SAEgnal. Using SAEgnal, we have investigated the difference in the risk of 39 adverse event types between combination and monotherapy arms across a subset of 34 combination trials. We observed different risk profiles between combination and monotherapies; interestingly, while the risk of elevated AST/ALT is lower in combination arms (in 1/8 trials, p-value < 0.05), it is higher for bleeding (7/8 trials, p-value < 0.05). We envisage that the SAEgnal framework would enable rapid predictive analytics of SAEs in oncology and accelerate drug development in oncology., (©2021 AMIA - All rights reserved.) more...
- Published
- 2021
40. MicroRNA-195 controls MICU1 expression and tumor growth in ovarian cancer.
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Rao G, Dwivedi SKD, Zhang Y, Dey A, Shameer K, Karthik R, Srikantan S, Hossen MN, Wren JD, Madesh M, Dudley JT, Bhattacharya R, and Mukherjee P
- Subjects
- Calcium-Binding Proteins genetics, Calcium-Binding Proteins metabolism, Cell Line, Tumor, Cell Proliferation genetics, Female, Gene Expression Regulation, Neoplastic, Glycolysis genetics, Humans, Mitochondrial Membrane Transport Proteins metabolism, Cation Transport Proteins genetics, Cation Transport Proteins metabolism, MicroRNAs genetics, MicroRNAs metabolism, Ovarian Neoplasms genetics
- Abstract
MICU1 is a mitochondrial inner membrane protein that inhibits mitochondrial calcium entry; elevated MICU1 expression is characteristic of many cancers, including ovarian cancer. MICU1 induces both glycolysis and chemoresistance and is associated with poor clinical outcomes. However, there are currently no available interventions to normalize aberrant MICU1 expression. Here, we demonstrate that microRNA-195-5p (miR-195) directly targets the 3' UTR of the MICU1 mRNA and represses MICU1 expression. Additionally, miR-195 is under-expressed in ovarian cancer cell lines, and restoring miR-195 expression reestablishes native MICU1 levels and the associated phenotypes. Stable expression of miR-195 in a human xenograft model of ovarian cancer significantly reduces tumor growth, increases tumor doubling times, and enhances overall survival. In conclusion, miR-195 controls MICU1 levels in ovarian cancer and could be exploited to normalize aberrant MICU1 expression, thus reversing both glycolysis and chemoresistance and consequently improving patient outcomes., (© 2020 The Authors.) more...
- Published
- 2020
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41. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.
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Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, and Arnaout R more...
- Subjects
- Delivery of Health Care, Humans, Machine Learning, Predictive Value of Tests, United States, Cardiology, Checklist
- Abstract
Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.) more...
- Published
- 2020
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42. Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses.
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Liu AC, Patel K, Vunikili RD, Johnson KW, Abdu F, Belman SK, Glicksberg BS, Tandale P, Fontanez R, Mathew OK, Kasarskis A, Mukherjee P, Subramanian L, Dudley JT, and Shameer K
- Subjects
- Humans, Prognosis, Risk Factors, Sepsis pathology, Precision Medicine, Sepsis diagnosis, Sepsis therapy
- Abstract
Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.) more...
- Published
- 2020
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43. KRCC1: A potential therapeutic target in ovarian cancer.
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Dwivedi SKD, Shameer K, Dey A, Mustafi SB, Xiong X, Bhattacharya U, Neizer-Ashun F, Rao G, Wang Y, Ivan C, Yang D, Dudley JT, Xu C, Wren JD, Mukherjee P, and Bhattacharya R
- Subjects
- Cell Line, Tumor, Female, Histone Deacetylase 1 genetics, Histone Deacetylase 1 metabolism, Histone Deacetylase 2 genetics, Histone Deacetylase 2 metabolism, Histones genetics, Histones metabolism, Humans, Phosphorylation, Risk Factors, DNA Damage, Intracellular Signaling Peptides and Proteins genetics, Intracellular Signaling Peptides and Proteins metabolism, Neoplasm Proteins genetics, Neoplasm Proteins metabolism, Ovarian Neoplasms genetics, Ovarian Neoplasms metabolism, Ovarian Neoplasms pathology, Ovarian Neoplasms therapy, Transcription, Genetic
- Abstract
Using a systems biology approach to prioritize potential points of intervention in ovarian cancer, we identified the lysine rich coiled-coil 1 (KRCC1), as a potential target. High-grade serous ovarian cancer patient tumors and cells express significantly higher levels of KRCC1 which correlates with poor overall survival and chemoresistance. We demonstrate that KRCC1 is predominantly present in the chromatin-bound nuclear fraction, interacts with HDAC1, HDAC2, and with the serine-threonine phosphatase PP1CC. Silencing KRCC1 inhibits cellular plasticity, invasive properties, and potentiates apoptosis resulting in reduced tumor growth. These phenotypes are associated with increased acetylation of histones and with increased phosphorylation of H2AX and CHK1, suggesting the modulation of transcription and DNA damage that may be mediated by the action of HDAC and PP1CC, respectively. Hence, we address an urgent need to develop new targets in cancer., (© 2019 Federation of American Societies for Experimental Biology.) more...
- Published
- 2020
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44. Correction to: Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits.
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Glicksberg BS, Amadori L, Akers NK, Sukhavasi K, Franzén O, Li L, Belbin GM, Ayers KL, Shameer K, Badgeley MA, Johnson KW, Readhead B, Darrow BJ, Kenny EE, Betsholtz C, Ermel R, Skogsberg J, Ruusalepp A, Schadt EE, Dudley JT, Ren H, Kovacic JC, Giannarelli C, Li SD, Björkegren JLM, and Chen R more...
- Abstract
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- 2019
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45. Decoding systems biology of plant stress for sustainable agriculture development and optimized food production.
- Author
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Shameer K, Naika MBN, Shafi KM, and Sowdhamini R
- Subjects
- Agriculture, Food Supply, Gene Expression Regulation, Plant genetics, Genetic Engineering, Genome, Plant, High-Throughput Nucleotide Sequencing, Humans, Metabolic Networks and Pathways genetics, Plants genetics, Systems Biology, Models, Biological, Plants metabolism, Stress, Physiological
- Abstract
Plants are essential facilitators of human life on planet earth. Plants play a critical functional role in mediating the quality of air, availability of food and the sustainability of agricultural resources. However, plants are in constant interaction with its environment and often hampered by various types of stresses like biotic and abiotic ones. Biotic stress is a significant reason for crop-loss and causes yield loss in the range of 31-42%, post-harvest loss due to biotic stress is in the range of 6-20%, and abiotic stress causes 6-20% of the crop damage. Recognizing the molecular factors driving plant stress-related events, and developing molecular strategies to aid plants to tolerate, resist or adapt to biotic and abiotic stress are critical for sustainable agriculture practice. In this review, we discuss how recent advances in bioinformatics, plant genomics, and data science could help to improve our understanding of plant stress biology and improve the scale of global food production. We present various areas of scientific and technological advances, such as increased availability of genomics data through whole genome sequencing that require attention. We also discuss emerging techniques including CRISPR-Cas9 based genome engineering systems to develop plant varieties that can handle combinatorial stress signals. Growing trend of converging multiple omics technologies and availability of accurate, multi-scale models of plant stress through the study of orthologs and synteny studies, would improve our knowledge of how plants perceive, respond, and manage stress to thrive as resilient crop species and thus help to reduce global food crisis., (Copyright © 2018 Elsevier Ltd. All rights reserved.) more...
- Published
- 2019
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46. Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits.
- Author
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Glicksberg BS, Amadori L, Akers NK, Sukhavasi K, Franzén O, Li L, Belbin GM, Ayers KL, Shameer K, Badgeley MA, Johnson KW, Readhead B, Darrow BJ, Kenny EE, Betsholtz C, Ermel R, Skogsberg J, Ruusalepp A, Schadt EE, Dudley JT, Ren H, Kovacic JC, Giannarelli C, Li SD, Björkegren JLM, and Chen R more...
- Subjects
- Cardiovascular Diseases blood, Cholesterol blood, Genotype, Humans, Triglycerides blood, Cardiovascular Diseases genetics, Genomics, Mutation
- Abstract
Background: Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene., Results: We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight., Conclusion: In sum, by integrating genetic and electronic medical record data, and leveraging one of the world's largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation. more...
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- 2019
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47. Genome-wide analysis indicates association between heterozygote advantage and healthy aging in humans.
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Xu K, Kosoy R, Shameer K, Kumar S, Liu L, Readhead B, Belbin GM, Lee HC, Chen R, and Dudley JT
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- Alleles, Female, Gene Frequency, Genetic Variation, Humans, Male, Polymorphism, Single Nucleotide, Genome, Human, Genome-Wide Association Study methods, Genomics methods, Healthy Aging genetics, Heterozygote
- Abstract
Background: Genetic diversity is known to confer survival advantage in many species across the tree of life. Here, we hypothesize that such pattern applies to humans as well and could be a result of higher fitness in individuals with higher genomic heterozygosity., Results: We use healthy aging as a proxy for better health and fitness, and observe greater heterozygosity in healthy-aged individuals. Specifically, we find that only common genetic variants show significantly higher excess of heterozygosity in the healthy-aged cohort. Lack of difference in heterozygosity for low-frequency variants or disease-associated variants excludes the possibility of compensation for deleterious recessive alleles as a mechanism. In addition, coding SNPs with the highest excess of heterozygosity in the healthy-aged cohort are enriched in genes involved in extracellular matrix and glycoproteins, a group of genes known to be under long-term balancing selection. We also find that individual heterozygosity rate is a significant predictor of electronic health record (EHR)-based estimates of 10-year survival probability in men but not in women, accounting for several factors including age and ethnicity., Conclusions: Our results demonstrate that the genomic heterozygosity is associated with human healthspan, and that the relationship between higher heterozygosity and healthy aging could be explained by heterozygote advantage. Further characterization of this relationship will have important implications in aging-associated disease risk prediction. more...
- Published
- 2019
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48. A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging.
- Author
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Johnson KW, Glicksberg BS, Shameer K, Vengrenyuk Y, Krittanawong C, Russak AJ, Sharma SK, Narula JN, Dudley JT, and Kini AS
- Subjects
- Aged, Aged, 80 and over, Algorithms, Biomarkers, Computational Biology methods, Female, Gene Ontology, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors administration & dosage, Male, Middle Aged, Plaque, Atherosclerotic drug therapy, Prognosis, ROC Curve, Tomography, Optical Coherence, Treatment Outcome, Gene Expression Profiling methods, Models, Biological, Plaque, Atherosclerotic diagnosis, Plaque, Atherosclerotic etiology, Transcriptome
- Abstract
Background: Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy., Methods: FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8-10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers., Findings: Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism., Interpretation: In this pilot study, transcriptomic models could predict if FCT increased following 8-10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.) more...
- Published
- 2019
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49. CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.
- Author
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Badgeley MA, Liu M, Glicksberg BS, Shervey M, Zech J, Shameer K, Lehar J, Oermann EK, McConnell MV, Snyder TM, and Dudley JT
- Subjects
- Deep Learning, Humans, Machine Learning, Neural Networks, Computer, Algorithms, Software
- Abstract
Motivation: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems., Results: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement., Availability and Implementation: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license., Supplementary Information: Supplementary material is available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.) more...
- Published
- 2019
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50. Correction: Population Distribution Analyses Reveal a Hierarchy of Molecular Players Underlying Parallel Endocytic Pathways.
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Gupta GD, Dey G, Mg S, Ramalingam B, Shameer K, Thottacherry JJ, Kalappurakkal JM, Howes MT, Chandran R, Das A, Menon S, Parton RG, Sowdhamini R, Thattai M, and Mayor S
- Abstract
[This corrects the article DOI: 10.1371/journal.pone.0100554.].
- Published
- 2018
- Full Text
- View/download PDF
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