Search

Hepatitis B virus (HBV) is a cause of hepatocellular carcinoma (HCC). Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways were affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation and has implications in early diagnosis and treatment.To elucidate HBV oncogenesis in HCC and identify potential therapeutic targets.We employed our Search, Tag, Analyze, Resource platform to conduct a meta-analysis of public data from National Center for Biotechnology Information's Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples and analyzed results with ingenuity pathway analysis.Our analysis revealed liver X receptors/retinoid X receptor (RXR) activation and farnesoid X receptor/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene rab-like protein 6 (RABL6). The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. We conducted survival analysis that demonstrated increased mortality with higher RABL6 expression. Additionally, homeobox A10 (HOXA10) was a top upstream regulator and was strongly upregulated in our analysis. HOXA10 has recently been demonstrated to contribute to HCC pathogenesisThis meta-analysis describes possible roles of RABL6 and HOXA10 in the pathogenesis of HBV-related HCC. RABL6 and HOXA10 represent potential therapeutic targets and warrant further investigation.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating “smart data” which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

Objective.Osteoporosis is a growing healthcare burden. By identifying osteoporosis-promoting genetic variations, we can spotlight targets for new pharmacologic therapies that will improve patient outcomes. In this metaanalysis, we analyzed mesenchymal stem cell (MSC) biomarkers in patients with osteoporosis.Methods.We employed our Search Tag Analyze Resource for the Gene Expression Omnibus (STARGEO) platform to conduct a metaanalysis to define osteoporosis pathogenesis. We compared 15 osteoporotic and 14 healthy control MSC samples. We then analyzed the genetic signature in Ingenuity Pathway Analysis.Results.The top canonical pathways identified that were statistically significant included the serine peptidase inhibitor kazal type 1 pancreatic cancer pathway, calcium signaling, pancreatic adenocarcinoma signaling, axonal guidance signaling, and glutamate receptor signaling. Upstream regulators involved in this disease process included ESR1, dexamethasone, CTNNβ1, CREB1, and ERBB2.Conclusion.Although there has been extensive research looking at the genetic basis for inflammatory arthritis, very little literature currently exists that has identified genetic pathways contributing to osteoporosis. Our study has identified several important genes involved in osteoporosis pathogenesis including ESR1, CTNNβ1, CREB1, and ERBB2. ESR1 has been shown to have numerous polymorphisms, which may play a prominent role in osteoporosis. The Wnt pathway, which includes the CTNNβ1 gene identified in our study, plays a prominent role in bone mass regulation. Wnt pathway polymorphisms can increase susceptibility to osteoporosis. Our analysis also suggests a potential mechanism for ERBB2 in osteoporosis through Semaphorin 4D (SEMA4D). Our metaanalysis identifies several genes and pathways that can be targeted to develop new anabolic drugs for osteoporosis treatment.

The study of precision medicine that measures the effects of social, cultural, and environmental influences on health is essential to improve health outcomes. Race is a social concept used historically to divide, track, control populations, and reinforce social hierarchies. Beyond genetics, race is also a surrogate for other socioeconomic factors affecting patient outcomes. Our data analytics study aims to analyze the Electronic Medical Record (EMR) to study patients of different races in diagnosing and treating Coronary Artery Disease (CAD). We found no race discrepancies at the University of California San Francisco Medical Centers. This study opens several new hypotheses for further research in this crucial field.

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

Background: Cardiovascular Disease (CVD) and Coronary Artery Disease (CAD) in particular, is one of the leading causes of death, morbidity, and mortality in the United States. Notably, women continue to have worse outcomes than men. The causes of these discrepancies have yet to be fully elucidated. The main objective of this study is to detect gender discrepancies in outcome using data analytics to risk stratify ~ 32,000 patients with CAD of the total 960,129 patients treated at UCSF Medical Center during an eight years. As an implementation of clinical care, this study’s long-term goal is to improve precision diagnosis and ultimately management of CAD for both early detection and identification of patients at risk for rapid progression of the disease.Methods: We designed and implemented a multidimensional framework to trace patients from admission through treatment as a path of events. The time between events for a similar set of paths was calculated. Then the average waiting time for each step of the treatment was calculated for men and women. Finally, we applied statistical analysis to determine differences in time between diagnosis and treatment steps for men and women.Discussions: There were statistically significant gender-based differences in the common path of diagnosis and treatment of patients with CAD. The average time for women from the first visit to diagnostic Cardiac Catheterization was more than 2 months than for men (358.77 vs. 291.83 days). By contrast, the average time from diagnostic Cardiac Catheterization to treatment Cardiac Catheterization and Coronary Artery Bypass Grafting (CABG) was not significant. Women with CAD requiring revascularization have a significantly longer interval between their first physician encounter indicative of CVD and their first diagnostic cardiac catheterization compared to men. Avoiding the delay in diagnosis and treatment will provide a better outcome for patients at risk.

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

A crucial factor in Big Data is to take advantage of available data and use that for discovery, hypothesis generation, and testing the hypothesis. When it comes to chronic diseases, worldwide, women continue to have poorer outcomes than men. The causes of these discrepancies have yet to be fully elucidated. Personalized medicine has transformed the way we identify and manage health-related issues. The big difference between traditional assessment of a situation and personalized medicine is the reliance of the multidimensional data (e.g., demographic, genetics) to make decisions about a treatment for individual patients. Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve health care quality in general. Deep learning models could be used in a wide variety of predictive problems and settings in health care. In this chapter, we describe the role of big data analytics, machine learning, deep learning, and personalized medicine to improve women’s health. Then we describe a summary of use cases from our research group at UCSF School of Medicine and Mayo Clinic Healthcare Delivery and Informatics that shows big-scale advanced data analytics to improve women’s health in dermatology, cardiology, and breast cancer.

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.

While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help to identify critically ill patients, provide proper treatment and prevent mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in IRAN between 3 March 2020 and 8 April 2020. Patients with COVID-19 were followed up to check their health condition after two months. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon Rank-Sum Test. 319 patients (mean age 45.48±18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein (CRP), fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating and age were the most important symptoms of COVID-19 infection. Traveling in past three months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not have any relationship with COVID-19. To the best of our knowledge, some of the effective features on the mortality due to COVID-19 are investigated for the first time in this research. This study may prove helpful in early prediction and risk reduction of mortality in patients infected with COVID-19. This article is protected by copyright. All rights reserved.

Background Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help to identify critically ill patients, provide proper treatment and prevent mortality. Methods We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in IRAN between 3 March 2020 and 8 April 2020. Patients with COVID- 19 were followed up to check their health condition after two months. The categorical data between groups were analyzed by Fisher’s exact test and continuous data by Wilcoxon Rank-Sum Test. Findings 319 patients (mean age 45.48±18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein (CRP), fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating and age were the most important symptoms of COVID-19 infection. Traveling in past three months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not have any relationship with COVID-19. Research in context Evidence before this study We searched Google scholar, PUBMED and Scopus for articles that investigated the recent epidemic of COVID-19, especially those that investigate effective risk factors. We found that there is not enough research in this field, especially the risk factor that is effective in finding the rate of mortality of this disease. Added value of this study We determined some of the most important effective risk factors on prediction, clinical outcome and mortality rate of COVID-19 infection. To the best of our knowledge, some of these risk factors are investigated in this work for the first time. Our findings could provide good insight into the early prediction of the disease, its clinical outcomes, and suggest a cost-effective method for mortality prediction. Implication of all the available evidence COVID-19 can transmit human-to-human and lead to severe symptoms and high mortality. Early prediction of this disease and the risk of mortality can help the physicians to better manage this worldwide health problem. Interpretation Finding clinical symptoms for early diagnosis of COVID-19 is a critical part of prevention. These symptoms can help in the assessment of disease progression. To the best of our knowledge, some of the effective features on the mortality due to COVID-19 are investigated for the first time in this research. Funding None

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection. Electronic supplementary material The online version of this article (10.1007/s10278-018-0154-z) contains supplementary material, which is available to authorized users.

Hepatitis C is a leading cause of chronic liver disease and hepatocellular carcinoma (HCC). Understanding the evolution and biology of HCC among HCV patients may lead to novel therapeutic avenues and risk stratification.Using meta-analysis platform STARGEO, we performed two separate meta-analyses as follows: 357 HCV-related HCC tumor samples with 220 adjacent non-tumor samples and 92 HCV-related cirrhotic liver samples with 53 healthy liver samples as a control. Then, we analyzed the signature in Ingenuity Pathway Analysis.HCV cirrhosis analysis demonstrated LPS/IL-1 mediated inhibition of RXR function, LXR/RXR activation, sirtuin signaling, IL-10 signaling and hepatic fibrosis/stellate cell activation as top canonical pathways. IL1β, TNF, and TGF-β1 were top upstream regulators. Cellular morphologic and signaling changes were noted through the up-regulation of RGS1/2, WNT receptor FZD7, the TGF-β1-induced gap junction gene GJA1, and the zinc finger transcription factor repressor SNAI2. Apoptosis was inhibited through the down-regulation of OMA1. Metabolic dysfunction was noted through the down-regulation of SCLY and CBS. HCV-related HCC analysis showed FXR/RXR and LXR/RXR signaling, LPS/IL1-mediated inhibition of RXR activation, and melatonin degradation as top canonical pathways.Our results suggest that the genetic changes in the setting of chronic HCV infection predispose patients to developing HCC.

Background Nasopharyngeal carcinoma (NPC) is a cancer of epithelial origin with a high incidence in certain populations. While NPC has a high remission rate with concomitant chemoradiation, recurrences are frequent, and the downstream morbidity of treatment is significant. Thus, it is imperative to find alternative therapies. Methods We employed a Search Tag Analyze Resource (STARGEO) platform to conduct a meta-analysis using the National Center for Biotechnology’s (NCBI) Gene Expression Omnibus (GEO) to define NPC pathogenesis. We identified 111 tumor samples and 43 healthy nasopharyngeal epithelium samples from NPC public patient data. We analyzed associated signatures in Ingenuity Pathway Analysis (IPA), restricting genes that showed statistical significance (p Results Our meta-analysis identified activation of lipopolysaccharide (LPS)-induced tissue injury in NPC tissue. Additionally, interleukin-1 (IL-1) and SB203580 were the top upstream regulators. Tumorigenesis-related genes such as homeobox A10 (HOXA10) and prostaglandin-endoperoxide synthase 2 (PTGS2 or COX-2) as well as those associated with extracellular matrix degradation, such as matrix metalloproteinases 1 and 3 (MMP-1, MMP-3) were also upregulated. Decreased expression of genes that encode proteins associated with maintaining healthy nasal respiratory epithelium structural integrity, including sentan-cilia apical structure protein (SNTN) and lactotransferrin (LTF) was documented. Importantly, we found that etanercept inhibits targets upregulated in NPC and LPS induction, such as MMP-1, PTGS2, and possibly MMP-3. Conclusions Our analysis illustrates that nasal epithelial barrier dysregulation and maladaptive immune responses are key components of NPC pathogenesis along with LPS-induced tissue damage.

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse. The Authors.

Background: The pathogenesis of sickle cell disease (SCD) and its complications have been well characterized down to the molecular level. However, there remains a relative dearth of disease modifying therapies that reduce the frequency and number of vas-occlusive crises, hospitalizations, and deaths. Recent advancements in utilizing hydroxyurea and L-glutamine, which both impact unique disease pathways, should pave way for the identification of other molecular pathways as ideal drug targets. In this regard, our meta-analysis serves to identify key genes and associated pathways that are differentially expressed in SC patients. Methods: We employed our STARGEO platform to tag samples from the NCBI Gene Expression Omnibus and performed meta-analysis to compare SC and healthy control transcriptomes. For the meta-analysis, we tagged 285 peripheral blood samples from SC patients and 86 samples from healthy subjects as a control. We then analyzed the signature in Ingenuity Pathway Analysis to elucidate top disease functions from our analysis. Results: From our meta-analysis, we identified iron homeostasis signaling, NRF2-mediated oxidative stress response, cell senescence, and pyrimidine interconversion/biosynthesis as top canonical pathways that were upregulated in the peripheral blood samples from SC patients. Top upstream regulators included membrane associated protein and transporter ABCB6, non-coding RNY3, and erythroid maturation transcription factors GATA1, KLF1, and HIPK2 (with predicted activation). The most upregulated genes included inflammatory modulators RNF182 and IFI27, the latter of which has been shown to inhibit vascular endothelial growth and repair. Several membrane-associated protein coding genes such as GYPA, RAP1GAP, and PAQR9 were also upregulated in the SC samples. RAP1GAP is known to modulate neutrophil cell adhesion and homing while PAQR9 has roles in regulating protein quality control: a role also seen in similarly upregulated YOD1, a deubiquitinating enzyme involved in trafficking of misfolded proteins. Expectedly, also upregulated were HBBP1 and SOX6, which regulate globin genes and have been shown to silence γ-globin expression. Lastly, SLC6A19, the neutral amino acid transporter mutated in Hartnup disease, was also upregulated. Of the downregulated genes, WASF3, a member of the Wiskott-Aldrich syndrome protein family, has been linked to poor survival in many malignancies, including AML and CMML, but has not previously been linked to SCD pathogenesis. ENKUR was also downregulated and has been annotated as a tethering protein to cation channels as well as linked to pathways involving vascular leakage. SIGLEC10, which binds to vascular adhesion proteins, is a key suppressor of inflammatory responses to damage; it's downregulation along with ELAPOR1, a transmembrane protein involved in cellular response to stress, was also observed. Finally, based off the focus genes in our analysis we identified several networks with most being involved in amino acid metabolism, cellular assembly, function, and maintenance, hematological disease, and organismal injury. The top pathway is illustrated in Figure 1. Conclusions: Our study illustrates differentially expressed gene activity in SCD consistent with known pathophysiology such as immune response, endothelial damage and adherence, heme metabolism, and globin regulation. We also showed evidence of genes not previously studied in SCD, which may have novel roles such as those part of the ubiquitin-proteasome system like YOD1 and RNF182. Additionally, while some genes in our analysis like EKLF and GAT1 have been shown to enhance δ-globin expression, paving way for possible drug therapies for B-hemoglobinopathies, others like IFI27, PAQR9, RAP1GAP, ENKUR, SIGLEC10, WASF3, and SOX9 have yet to be studied as mediators of disease pathogenesis in SCD. A target to SOX9, a known suppressor of γ-globin, or ABCB6, a known modulator of erythroid cell shape and hydration, have particularly promising potential as disease modifying therapies. Finally, HIPK2, HBBP1, and SLC6A19 have previously been shown to have intriguing effects on hydroxyurea dosing and responsivity in SC patients and may also be candidate target molecules to enhance existing therapies. These data identify potential candidate pathways for mechanistic studies seeking to confirm a causative role in the pathogenesis of sickle cell disease. Disclosures No relevant conflicts of interest to declare.

Cardiovascular disease (CVD) is the major cause of death in the United State and other countries

We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports., Measurement(s)coronary artery diseaseTechnology Type(s)digital curationFactor Type(s)year • diseaseSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9825680

333 Background: Hepatitis B is a cause of hepatocellular carcinoma (HCC) globally, irrespective of viral loads. Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation, and has implications in early diagnosis and treatment. Methods: We employed our Search, Tag, Analyze, Resource (STARGEO) platform to conduct a meta-analysis of public data from NCBI's Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples. We present the results from the meta-analysis in Ingenuity Pathway Analysis. Results:Our analysis revealed LXR/RXR activation, LPS-IL-1 mediation inhibition of RXR, and FXR/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene RABL6. The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. Additionally, HOXA10 was a top upstream regulator and was strongly upregulated in our analysis. While described in other cancer, HOXA10 has not been well documented in HBV-related HCC and our causal analysis suggests it mediates pathogenesis through downregulation of tumor suppressors and its protective effect from oxidative stress. Conclusions: This meta-analysis describes possible role of viral regulatory proteins such as RABL6 and HOXA10 in the pathogenesis of HBV -related HCC. Further prospective studies are needed to confirm our findings.

Aims Dermatomyositis (DM) is a progressive, idiopathic inflammatory myopathy with poorly understood pathogenesis. A hallmark of DM is an increased risk for developing breast, ovarian, and lung cancer. Since autoantibodies against anti-TIF-1-γ, a member of the tripartite motif (TRIM) proteins, has a strong association with malignancy, we examined expression of the TRIM gene family to identify pathways that may be contributing to DM pathogenesis. Materials and methods We employed the Search Tag Analyze Resource for GEO platform to search the NCBI Gene Expression Omnibus to elucidate TRIM family gene expression as well as oncogenic drivers in DM pathology. We conducted meta-analysis of the data from human skin (60 DM vs 34 healthy) and muscle (71 DM vs 22 healthy). Key findings We identified genes involved in innate immunity, antigen presentation, metabolism, and other cellular processes as facilitators of DM disease activity and confirmed previous observations regarding the presence of a robust interferon signature. Moreover, analysis of DM muscle samples revealed upregulation of TRIM14, TRIM22, TRIM25, TRIM27, and TRIM38. Likewise, analysis of DM skin samples showed upregulation of TRIM5, TRIM6, TRIM 14, TRIM21, TRIM34, and TRIM38 and downregulation of TRIM73. Additionally, we noted upregulation of oncogenes IGLC1, IFI44, POSTN, MYC, NPM1, and IDO1 and related this change to interferon signaling. While the clinical data associated with genetic data that was analyzed did not contain clinical data regarding malignancy in these cohorts, the observed genetic changes may be associated with homeostatic and signaling changes that relate to the increased risk in malignancy in DM. Significance Our results implicate previously unknown genes as potential drivers of DM pathology and suggest certain TRIM family members may have disease-specific roles with potential diagnostic and therapeutic implications.

A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful.

In biomedicine, high-quality metadata are crucial for finding experimental datasets, for understanding how experiments were performed, and for reproducing those experiments. Despite the recent focus on metadata, the quality of metadata available in public repositories continues to be extremely poor. A key difficulty is that the typical metadata acquisition process is time-consuming and error prone, with weak or nonexistent support for linking metadata to ontologies. There is a pressing need for methods and tools to speed up the metadata acquisition process and to increase the quality of metadata that are entered. In this paper, we describe a methodology and set of associated tools that we developed to address this challenge. A core component of this approach is a value recommendation framework that uses analysis of previously entered metadata and ontology-based metadata specifications to help users rapidly and accurately enter their metadata. We performed an initial evaluation of this approach using metadata from a public metadata repository.