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4. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

Author

Mason, Mike, Lapuente-Santana, Óscar, Halkola, Anni S., Wang, Wenyu, Mall, Raghvendra, Xiao, Xu, Kaufman, Jacob, Fu, Jingxin, Pfeil, Jacob, Banerjee, Jineta, Chung, Verena, Chang, Han, Chasalow, Scott D., Lin, Hung Ying, Chai, Rongrong, Yu, Thomas, Finotello, Francesca, Mirtti, Tuomas, Mäyränpää, Mikko I., Bao, Jie, Verschuren, Emmy W., Ahmed, Eiman I., Ceccarelli, Michele, Miller, Lance D., Monaco, Gianni, Hendrickx, Wouter R. L., Sherif, Shimaa, Yang, Lin, Tang, Ming, Gu, Shengqing Stan, Zhang, Wubing, Zhang, Yi, Zeng, Zexian, Das Sahu, Avinash, Liu, Yang, Yang, Wenxian, Bedognetti, Davide, Tang, Jing, Eduati, Federica, Laajala, Teemu D., Geese, William J., Guinney, Justin, Szustakowski, Joseph D., Vincent, Benjamin G., and Carbone, David P.

Published
2024
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5. An integrated tumor, immune and microbiome atlas of colon cancer.

Author

Roelands, Jessica, Kuppen, Peter, Ahmed, Eiman, Mall, Raghvendra, Masoodi, Tariq, Singh, Parul, Monaco, Gianni, Raynaud, Christophe, de Miranda, Noel, Ferraro, Luigi, Carneiro-Lobo, Tatiana, Syed, Najeeb, Rawat, Arun, Awad, Amany, Decock, Julie, Mifsud, William, Miller, Lance, Sherif, Shimaa, Mohamed, Mahmoud, Rinchai, Darawan, Van den Eynde, Marc, Sayaman, Rosalyn, Ziv, Elad, Bertucci, Francois, Petkar, Mahir, Lorenz, Stephan, Mathew, Lisa, Wang, Kun, Murugesan, Selvasankar, Chaussabel, Damien, Vahrmeijer, Alexander, Wang, Ena, Ceccarelli, Anna, Fakhro, Khalid, Zoppoli, Gabriele, Ballestrero, Alberto, Tollenaar, Rob, Marincola, Francesco, Galon, Jérôme, Khodor, Souhaila, Ceccarelli, Michele, Hendrickx, Wouter, and Bedognetti, Davide

Subjects
Humans, Cohort Studies, Biomarkers, Tumor, Colonic Neoplasms, Transcriptome, Tumor Microenvironment
Abstract

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

Published
2023

6. Permutation-Invariant Subgraph Discovery

Author

Mall, Raghvendra, Parambath, Shameem A., Yufei, Han, Yu, Ting, and Chawla, Sanjay

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause. For example, suppose there are two gene regulatory networks X and Y from a diseased and normal tissue respectively. Then, the PSPI problem can be used to detect if there has been a structural change between the two networks which can serve as a signature of the disease. Besides the new problem formulation, we propose an ADMM algorithm (STEPD) to solve a relaxed version of the PSPI problem. An extensive case study on comparative gene regulatory networks (GRNs) is used to demonstrate that STEPD is able to accurately infer structured perturbations and thus provides a tool for computational biologists to identify novel prognostic signatures. A spectral analysis confirms that STEPD can recover small clique-like perturbations making it a useful tool for detecting permutation-invariant changes in graphs., Comment: 8 pages, 4 Figures, 2 Tables

Published
2021

11. Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry

Author

Roelands, Jessica, Mall, Raghvendra, Almeer, Hossam, Thomas, Remy, Mohamed, Mahmoud G, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Ziv, Elad, Sayaman, Rosalyn W, Kuppen, Peter JK, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Human Genome, Clinical Research, Cancer, Breast Cancer, Good Health and Well Being, Clinical sciences, Oncology and carcinogenesis, Epidemiology
Abstract

Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E - 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.

Published
2021

17. Kernel Spectral Clustering and applications

Author

Langone, Rocco, Mall, Raghvendra, Alzate, Carlos, and Suykens, Johan A. K.

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered., Comment: chapter contribution to the book "Unsupervised Learning Algorithms"

Published
2015

19. Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

Author

Mall, Raghvendra, Langone, Rocco, and Suykens, Johan A. K.

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show a major advantage our proposed approach i.e. the ability to locate good quality clusters at both the coarser and finer levels of hierarchy using internal cluster quality metrics on 7 real-life networks., Comment: PLOS ONE, Vol 9, Issue 6, June 2014

Published
2014
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21. Politics on YouTube: Detecting Online Group Polarization Based on News Videos' Comments.

Author

Mall, Raghvendra, Nagpal, Mridul, Salminen, Joni, Almerekhi, Hind, Soon-gyo Jung, and Jansen, Bernard J.

Subjects
*SOCIAL media, *MACHINE learning, *ORGANIZATION management, *INFORMATION retrieval
Abstract

Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments' toxicity approximates either an "M" or "U" shaped distribution--that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities. [ABSTRACT FROM AUTHOR]

Published
2024
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26. MATE-Pred: Multimodal Attention-based TCR-Epitope interaction Predictor

Author

Goffinet, Etienne, Mall, Raghvendra, Singh, Ankita, Kaushik, Rahul, Castiglione, Filippo, Goffinet, Etienne, Mall, Raghvendra, Singh, Ankita, Kaushik, Rahul, and Castiglione, Filippo

Abstract

An accurate binding affinity prediction between T-cell receptors and epitopes contributes decisively to develop successful immunotherapy strategies. Some state-of-the-art computational methods implement deep learning techniques by integrating evolutionary features to convert the amino acid residues of cell receptors and epitope sequences into numerical values, while some other methods employ pre-trained language models to summarize the embedding vectors at the amino acid residue level to obtain sequence-wise representations. Here, we propose a highly reliable novel method, MATE-Pred, that performs multi-modal attention-based prediction of T-cell receptors and epitopes binding affinity. The MATE-Pred is compared and benchmarked with other deep learning models that leverage multi-modal representations of T-cell receptors and epitopes. In the proposed method, the textual representation of proteins is embedded with a pre-trained bi-directional encoder model and combined with two additional modalities: a) a comprehensive set of selected physicochemical properties; b) predicted contact maps that estimate the 3D distances between amino acid residues in the sequences. The MATE-Pred demonstrates the potential of multi-modal model in achieving state-of-the-art performance (+8.4\% MCC, +5.5\% AUC compared to baselines) and efficiently capturing contextual, physicochemical, and structural information from amino acid residues. The performance of MATE-Pred projects its potential application in various drug discovery regimes., Comment: Patent pending: U.S. Provisional Application No. 63/603,952

Published
2023

27. A metabolic function of FGFR3-TACC3 gene fusions in cancer

Author

Frattini, Vronique, Pagnotta, Stefano M., Tala, Fan, Jerry J., Russo, Marco V., Lee, Sang Bae, Garofano, Luciano, Zhang, Jing, Shi, Peiguo, Lewis, Genevieve, Sanson, Heloise, Frederick, Vanessa, Castano, Angelica M., Cerulo, Luigi, Rolland, Delphine C. M., Mall, Raghvendra, Mokhtari, Karima, Elenitoba-Johnson, Kojo S. J., Sanson, Marc, Huang, Xi, Ceccarelli, Michele, Lasorella, Anna, and Iavarone, Antonio

Subjects
Metabolism -- Genetic aspects -- Health aspects, Fibroblast growth factor receptors -- Health aspects -- Genetic aspects, Cancer -- Genetic aspects, Gene fusion -- Health aspects -- Genetic aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Vronique Frattini [1]; Stefano M. Pagnotta [1, 2]; Tala [1]; Jerry J. Fan [3, 4]; Marco V. Russo [1]; Sang Bae Lee [1]; Luciano Garofano [1, 2, 5]; Jing [...]

Published
2018
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28. Additional file 1 of An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Author

Mir, Fayaz Ahmad, Mall, Raghvendra, Ullah, Ehsan, Iskandarani, Ahmad, Cyprian, Farhan, Samra, Tareq A., Alkasem, Meis, Abdalhakam, Ibrahem, Farooq, Faisal, Taheri, Shahrad, and Abou-Samra, Abdul-Badi

Abstract

Additional file 1: Figure S1. Flowchart highlighting different number of MiRNAs, PCGs, Metabolites analyzed in our dataset and steps taken to identify key MiRNAs, PCGs, Metabolites involved in significantly enriched metabolic pathways between OBM vs OBO. Figure S2. Correlation between clinical traits and enriched metabolic pathways. Here each block represents correlation between the enrichment profile of a metabolic pathway and a clinical characteristic based on their values across the 39 participant profiles. Here we only considered those pathways which were significantly enriched and those clinical traits which were highlighted in Fig. 3A. Figure S3. Gene expression of 25 identified genes (differentially expressed in the samples) across multiple tissue types in the GTEx portal. ( https://gtexportal.org/ ). The expression of these genes is higher across multiple tissue types and not biased towards blood tissue from which samples were collected.

Published
2023
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35. Characteristic MicroRNAs Linked to Dysregulated Metabolic Pathways in Qatari Adult Subjects With Obesity and Metabolic Syndrome

Author

Mir, Fayaz Ahmad, primary, Mall, Raghvendra, additional, Iskandarani, Ahmad, additional, Ullah, Ehsan, additional, Samra, Tareq A., additional, Cyprian, Farhan, additional, Parray, Aijaz, additional, Alkasem, Meis, additional, Abdalhakam, Ibrahem, additional, Farooq, Faisal, additional, and Abou-Samra, Abdul-Badi, additional

Published
2022
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36. Benchmark on a large cohort for sleep-wake classification with machine learning techniques

Author

Palotti, Joao, Mall, Raghvendra, Aupetit, Michael, Rueschman, Michael, Singh, Meghna, Sathyanarayana, Aarti, Taheri, Shahrad, and Fernandez-Luque, Luis

Subjects
Quality of life, Epidemiology, Machine learning, Health care, Health services and systems, lcsh:R858-859.7, lcsh:Computer applications to medicine. Medical informatics, Article, Fatigue
Abstract

Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F1 score of the machine learning algorithms, was also superior to the device’s native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.Other Information Published in: npj Digital Medicine License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41746-019-0126-9

Published
2022
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37. Additional file 9 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Author

Sherif, Shimaa, Mall, Raghvendra, Almeer, Hossam, Naik, Adviti, Al Homaid, Abdulaziz, Thomas, Remy, Roelands, Jessica, Narayanan, Sathiya, Mohamed, Mahmoud Gasim, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Abstract

Additional file 9: Correlation of 3 ir-lncRNA signature with immune subpopulations across tumor types. Pearson correlation heatmap between immune cell subpopulation enrichment scores and 3 ir-lncRNA scores.

Published
2022
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38. Additional file 6 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Author

Sherif, Shimaa, Mall, Raghvendra, Almeer, Hossam, Naik, Adviti, Al Homaid, Abdulaziz, Thomas, Remy, Roelands, Jessica, Narayanan, Sathiya, Mohamed, Mahmoud Gasim, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Abstract

Additional file 6: Prognostic value of ICR classifier across solid cancers. Forest plot showing HRs for death (overall survival) and corresponding 95%-confidence interval for the continuous ICR score and number of patients for each TCGA cancer cohort and RAQA breast cancer cohort. Significant positive HRs are visualized in blue and significant negative HRs are visualized in red. ICR enabled (HR < 1, p-value < 0.05) cancer types are indicated with orange asterisks and ICR disabled (HR > 1, p-value < 0.05) cancers are indicated with purple asterisks.

Published
2022
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39. Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry

Author

Roelands, Jessica, Mall, Raghvendra, Almeer, Hossam, Thomas, Remy, Mohamed, Mahmoud G., Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Ziv, Elad, Sayaman, Rosalyn W., Kuppen, Peter J. K., Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Subjects
Human Genome, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Clinical sciences, Article, Prognostic markers, Good Health and Well Being, Breast cancer, Pharmacology and pharmaceutical sciences, Clinical Research, Breast Cancer, Genetics, Cancer genomics, Tumour immunology, RC254-282, Oncogenesis, Cancer
Abstract

Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E − 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.Other Information Published in: npj Breast Cancer License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41523-021-00215-x

Published
2022
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40. Additional file 8 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Author

Sherif, Shimaa, Mall, Raghvendra, Almeer, Hossam, Naik, Adviti, Al Homaid, Abdulaziz, Thomas, Remy, Roelands, Jessica, Narayanan, Sathiya, Mohamed, Mahmoud Gasim, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Abstract

Additional file 8: Survival curves of 3 ir-lncRNA signature. Overall survival Kaplan-Meier curves in which the 3 ir-lncRNAs signature did not show any significant prognostic value. Dichotomization cutoff of ‘high’ (red) and ‘low’ (cyan) subgroups was based on the optimal cut-off point as determined by a 5-fold cross-validation analysis. Censor points are indicated by vertical lines. P-values were determined by logrank test.

Published
2022
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41. Additional file 1 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Author

Sherif, Shimaa, Mall, Raghvendra, Almeer, Hossam, Naik, Adviti, Al Homaid, Abdulaziz, Thomas, Remy, Roelands, Jessica, Narayanan, Sathiya, Mohamed, Mahmoud Gasim, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Abstract

Additional file 1: Mapping of differentially expressed ir-lncRNAs to protein coding genes. (A) Diagram representation of the random walk with restart global propagation network algorithm. (B) Walkscore distribution of protein-coding genes in TCGA-BRCA, with cutoff set at walkscore ≥ 0.01 to generate a ranked list of protein-coding genes in proximity of differentially expressed ir-lncRNAs.

Published
2022
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44. Data-Driven Drug Repurposing for COVID-19

Author

Ullah Ehsan, Elbasir Abdurrahman, Meer Hossam Al, Mall Raghvendra, and Chawla Sanjay

Subjects
Drug, Coronavirus disease 2019 (COVID-19), business.industry, media_common.quotation_subject, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Lopinavir, Computational biology, Clinical trial, Drug repositioning, medicine, Ritonavir, Protein activity, business, medicine.drug, media_common
Abstract

Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can berepurposed for COVID-19.Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.

Published
2020
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48. Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response

Author

Roelands, Jessica, primary, Hendrickx, Wouter, additional, Zoppoli, Gabriele, additional, Mall, Raghvendra, additional, Saad, Mohamad, additional, Halliwill, Kyle, additional, Curigliano, Giuseppe, additional, Rinchai, Darawan, additional, Decock, Julie, additional, Delogu, Lucia G, additional, Turan, Tolga, additional, Samayoa, Josue, additional, Chouchane, Lotfi, additional, Ballestrero, Alberto, additional, Wang, Ena, additional, Finetti, Pascal, additional, Bertucci, Francois, additional, Miller, Lance D, additional, Galon, Jerome, additional, Marincola, Francesco M, additional, Kuppen, Peter J K, additional, Ceccarelli, Michele, additional, and Bedognetti, Davide, additional

Published
2020
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49. Assessment of network module identification across complex diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Natoli, Ted, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Marbach, Daniel, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Mercer, Johnathan, Micarelli, Elisa, Monaco, Alfonso, Narayan, Rajiv, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., DREAM Module Identification Challenge Consortium, Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Sáez Rodríguez, Julio, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Subramanian, Aravind, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Slonim, Donna K., Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Zhang, Jitao D., Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Stolovitzky, Gustavo, Vivek, Jayaswal, von Mering, Christian, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Kutalik, Zoltán, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Lage, Kasper, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, Zuccon, Guido, Crawford, Jake, Cowen, Lenore J., Bergmann, Sven, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Tomasoni, Mattia, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Fang, Tao, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, De Domenico, Manlio, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Lamparter, David, Del Sol, Antonio, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Lin, Junyuan, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hescott, Benjamin, Hu, Xiaozhe, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan T., Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Le, Thuc Duy, Li, Jiuyong, Liu, Lin, Zhang, W, Marbach, D, The DREAM Module Identification Challenge Consortium, Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Spinelli, Lionel, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Theories and Approaches of Genomic Complexity (TAGC), DREAM Module Identification Challenge Consortium, Aicheler, F., Amoroso, N., Arenas, A., Azhagesan, K., Baker, A., Banf, M., Batzoglou, S., Baudot, A., Bellotti, R., Bergmann, S., Boroevich, K.A., Brun, C., Cai, S., Caldera, M., Calderone, A., Cesareni, G., Chen, W., Chichester, C., Choobdar, S., Cowen, L., Crawford, J., Cui, H., Dao, P., De Domenico, M., Dhroso, A., Didier, G., Divine, M., Del Sol, A., Fang, T., Feng, X., Flores-Canales, J.C., Fortunato, S., Gitter, A., Gorska, A., Guan, Y., Guénoche, A., Gómez, S., Hamza, H., Hartmann, A., He, S., Heijs, A., Heinrich, J., Hescott, B., Hu, X., Hu, Y., Huang, X., Hughitt, V.K., Jeon, M., Jeub, L., Johnson, N.T., Joo, K., Joung, I., Jung, S., Kalko, S.G., Kamola, P.J., Kang, J., Kaveelerdpotjana, B., Kim, M., Kim, Y.A., Kohlbacher, O., Korkin, D., Krzysztof, K., Kunji, K., Kutalik, Z., Lage, K., Lamparter, D., Lang-Brown, S., Le, T.D., Lee, J., Lee, S., Li, D., Li, J., Lin, J., Liu, L., Loizou, A., Luo, Z., Lysenko, A., Ma, T., Mall, R., Marbach, D., Mattia, T., Medvedovic, M., Menche, J., Mercer, J., Micarelli, E., Monaco, A., Müller, F., Narayan, R., Narykov, O., Natoli, T., Norman, T., Park, S., Perfetto, L., Perrin, D., Pirrò, S., Przytycka, T.M., Qian, X., Raman, K., Ramazzotti, D., Ramsahai, E., Ravindran, B., Rennert, P., Saez-Rodriguez, J., Schärfe, C., Sharan, R., Shi, N., Shin, W., Shu, H., Sinha, H., Slonim, D.K., Spinelli, L., Srinivasan, S., Subramanian, A., Suver, C., Szklarczyk, D., Tangaro, S., Thiagarajan, S., Tichit, L., Tiede, T., Tripathi, B., Tsherniak, A., Tsunoda, T., Türei, D., Ullah, E., Vahedi, G., Valdeolivas, A., Vivek, J., von Mering, C., Waagmeester, A., Wang, B., Wang, Y., Weir, B.A., White, S., Winkler, S., Xu, K., Xu, T., Yan, C., Yang, L., Yu, K., Yu, X., Zaffaroni, G., Zaslavskiy, M., Zeng, T., Zhang, J.D., Zhang, L., Zhang, W., Zhang, X., Zhang, J., Zhou, X., Zhou, J., Zhu, H., Zhu, J., and Zuccon, G.

Subjects
Identification methods, Cellular signalling networks, Computer science, Population genetics, [SDV]Life Sciences [q-bio], Quantitative Trait Loci, Gene regulatory network, DREAM challenge, network, modules, predictions, Genome-wide association study, Computational biology, Biochemistry, Models, Biological, Polymorphism, Single Nucleotide, Gene regulatory networks, Functional clustering, 03 medical and health sciences, Human disease, Humans, Disease, ddc:610, Protein Interaction Maps, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Network module, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Network topology, Gene Expression Profiling, Computational Biology, Cell Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Gene expression profiling, [SDV] Life Sciences [q-bio], Molecular network, Phenotype, Protein network, Network Module Identification, Analysis, Algorithms, Biotechnology, Genome-Wide Association Study
Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology., In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

Published
2019
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50. Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, Subramanian, Aravind, Zhang, Jitao D., Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K., Saez-Rodriguez, Julio, Cowen, Lenore J., Bergmann, Sven, Marbach, Daniel, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, Domenico, Manlio De, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Sol, Antonio del, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan, Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Micarelli, Elisa, Monaco, Alfonso, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Vivek, Jayaswal, Mering, Christian von, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, and Zuccon, Guido

Subjects
Identification methods, Molecular network, Computer science, Association (object-oriented programming), Key (cryptography), Open community, Genome-wide association study, Identification (biology), Computational biology, Disease
Abstract

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

Published
2018
Full Text
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