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Leveraging Big Data and Predictive Knowledge to Fight Disease

Leveraging Big Data and Predictive Knowledge to Fight Disease

Tuesday, July 28, 2015

The New York Academy of Sciences

Presented By

Presented by the Biochemical Pharmacology Discussion Group and the New York Academy of Sciences


Drug development is entering an era of precision medicine that is centered on the analysis of massive amounts of data. The ability to integrate, interrogate, model and interpret biological, chemical, pharmacological, genomic and clinical data holistically is key to making more effective and truly personalized medicines to fight disease. Researchers are using innovative technologies and computational techniques to develop predictive knowledge for the identification of promising new treatments, new therapeutic uses for existing molecules, patients who are good candidates for particular clinical trials or treatment protocols, and population signals of adverse drug reactions. This symposium explores the many uses of big data and predictive knowledge to guide drug development and clinical trials.

This event will also be broadcast as a webinar.

Please note: Transmission of presentations via the webinar is subject to individual consent by the speakers. Therefore, we cannot guarantee that every speaker's presentation will be broadcast in full via the webinar. To access all speakers' presentations in full, we invite you to attend the live event in New York City when possible.

Registration and Webinar Pricing

Student / Postdoc Member$15
Nonmember (Academia)$65
Nonmember (Corporate)$85
Nonmember (Non-profit)$65
Nonmember (Student / Postdoc / Resident / Fellow)$45

The Biochemical Pharmacology Discussion Group is proudly supported by

  • Boehringer Ingelheim
  • Pfizer

American Chemical Society


* Presentation titles and times are subject to change.

July 28, 2015

8:30 AM

Registration and Continental Breakfast

9:00 AM

Introductory Remarks
Sonya Dougal, PhD, New York Academy of Sciences
Walter Jessen, PhD, Covance Inc.

9:15 AM

Big Data for Drug Discovery and Drug Safety
Nicholas Tatonetti, PhD, Columbia University Medical Center

9:45 AM

Harnessing the Power of Healthcare Data with Advanced Data Analytics to Fight Disease
Diane Wuest, PhD, GNS Healthcare

10:15 AM

Artificial Intelligence Strategies for the Analysis of Biomedical Big Data
Jason H. Moore, PhD, MS, University of Pennsylvania

10:45 AM

Networking Coffee Break

11:15 AM

Use of Electronic Health Records for Surveillance and Predictive Analytics
Michael Matheny, MD, MS, MPH, Vanderbilt University

11:45 AM

Networking Lunch Break and Poster Session

1:15 PM

Data-Driven Study Population Definition for Clinical and Translational Research
Chunhua Weng, PhD, Columbia University

1:45 PM

Keynote Address
The Future of Developing Drugs: Employing Artificial Intelligence and Biology
Niven R. Narain, MD, Berg Pharma

2:30 PM

Networking Coffee Break

3:00 PM

Drug Repositioning in the Era of Precision Medicine
Craig P. Webb, PhD, NuMedii, Inc.

3:30 PM

Use of "Big Data" in the Development of Crizotinib (Xalkori) for ALK+ Metastatic Non-Small Cell Lung Cancer (NSCLC)
Marc D. Chioda, PharmD, Pfizer Inc.
Leonard James, MD, PhD, Pfizer Inc.

4:15 PM

Predicting Posttraumatic Stress from Multi-modular Data
Sisi Ma, PhD, New York University School of Medicine

4:30 PM

Understanding Multicellular Function and Disease with Human Tissue-Specific Networks
Arjun Krishnan, PhD, Princeton University

4:55 PM

Closing Remarks
Robert Martone, St. Jude Children's Research Hospital

5:00 PM

Networking Reception

6:00 PM




Walter Jessen, PhD

Covance Inc.

Robert Martone

St. Jude Children's Research Hospital

Sonya Dougal, PhD

The New York Academy of Sciences

Keynote Speaker

Niven R. Narain, MD

Berg Pharma

Niven R. Narain is Co-Founder, President & CTO of Berg, a Boston-based biopharma company housing fully integrated discovery, clinical, analytics, and diagnostics divisions.  Narain is keenly focused on making the healthcare industry more efficient by employing the flagship Interrogative Biology® platform he created which leverages leading-edge biological and clinical insight from patients.  The platform merges biology with technology to truly represent a true Precision Medicine approach to understating patient populations with use of artificial intelligence to derive actionable drug targets, biomarkers, and health analytic information.  Niven has overseen the development of a robust pipeline at Berg led by BPM 31510, an anticancer technology he discovered that targets the cancer metabolism being developed for solid tumors and skin cancer. In addition to multiple pre-IND assets in diabetes and CNS diseases, Narain collaborated with the US Department of Defense to develop novel biomarkers for the diagnoses and prognosis of prostate cancer currently in CLIA-based clinical trials for product launch.  He is inventor of the Interrogative Biology™ platform that has produced and guided clinical development of lead molecules in cancer and diabetes. His technologies and scientific expertise is the subject of key collaborations within the US Department of Defense, NASA, Walter Reed National Military Medical Center, NIH/NCI, in addition to leading academic medical centers such as Harvard Medical School, MD Anderson Cancer Center, Weill Cornell Medical College, among others. Narain has over 400 issued and pending US and international patents, covering novel biological platform technologies and multiple disease indications. Narain was previously Director of Cutaneous Oncology & Therapeutics Research at the Miller School of Medicine and serves as Sr. Biopharma Advisor to Ocean Tomo in Chicago and serves on the Steering Committee for NASA on the Gene Lab/Mars Initiative. A graduate of St. John’s University, NY in Biochemistry/Philosophy, Narain received his PhD training in cancer biology and clinical dermatology research at the Miller School of Medicine.


Marc D. Chioda, PharmD

Pfizer Inc,

Marc Chioda is an Associate Medical Director on the Lung Cancer team at Pfizer Oncology. He is the US Medical Affairs Lead for crizotinib (Xalkori) and also supports pipeline compounds in development such as PF- 06463922; Pfizer’s next-generation ALK/ROS inhibitor. With over a decade of pharmaceutical industry experience, Marc has worked on a variety of teams spanning pre-clinical research, clinical research and health economics/outcomes research. He has practiced in hospital, community and specialty pharmacy settings. Marc is a co-inventor on five pharmaceutical patents and joined Pfizer in 2013 after serving as adjunct clinical faculty at Rutgers University where he also earned his Doctor of Pharmacy (PharmD) degree.

Leonard James, MD, PhD

Pfizer Inc.

Lee James is a senior director in clinical development at Pfizer and is the Global Clinical Lead for PF-06463922, Pfizer’s next generation ALK/ROS1 inhibitor.  Prior to this role, he worked with multiple teams in clinical development and medical affairs involving both Xalkori (crizotinib) and Sutent (sunitinib).

Lee received his Bachelor of Science from Cornell University, and both MD and a PhD in Molecular and Cellular Biology from the University of Washington in Seattle. His PhD thesis on Myc target genes took place at the Fred Hutchinson Cancer Research Center.  He completed his residency in Internal Medicine at the University of Chicago Hospitals and his post-graduate training through the Oncology/Hematology fellowship program at Memorial Sloan Kettering Cancer Center, with a focus on lung cancer.  Prior to Pfizer, he was a Medical Oncologist in private practice, where he led the clinical implementation of a new EMR system and served as PI on industry-sponsored clinical trials.

Iya Khalil, PhD

GNS Healthcare

Iya Khalil is responsible for initiating and developing GNS Healthcare’s partnerships with pharma, biotech, and academia. In addition, she oversees the execution of projects in these areas.

She has extensive experience in creating and applying computational approaches that leverage large-scale genomic, clinical, and molecular data for healthcare innovation. Prior to joining GNS Healthcare, she worked at Cornell University, the University of Washington, and Abbott Labs.

A frequent speaker at industry events and conferences, Iya is an inventor on a number of pending patents and has published multiple articles on in silico technologies applied to drug discovery and development. She is also a co-founder and board member of the New Libya Foundation, a non-profit organization dedicated to nurturing the development of civil society organizations in Libya. Iya holds a BS in physics from the University of Washington and a Ph.D. in physics from Cornell University.

Arjun Krishnan, PhD

Princeton University

Arjun Krishnan is a Senior Researcher at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He has a B.Tech in Biotechnology and a PhD in Genetics, Bioinformatics & Computational Biology. Arjun’s research interests lie in understanding various aspects of multicellular biology: (1) tissue/cell-type specific gene expression, function and interaction, (2) functional and evolutionary relationships between cell-types/tissues, and (3) role of tissue-specificity in disease manifestation and drug response. One of the principal approaches he takes is to integrate large-scale functional genomics data to build computational models of gene interactions in specific biological contexts. These models are designed to capture scarce expert biomedical knowledge and make systematic genome-wide predictions of gene function, disease-gene association and effects of genetic perturbation.

Sisi Ma, PhD

New York University School of Medicine

Dr. Sisi Ma is a research scientist at the Center for Health Informatics and Bioinformatics, New York University Langone Medical Center. Dr. Ma’s primary research interest is the application of statistical modeling, machine learning, and causal analysis methods in the field of biology and medicine. Specifically, her approaches include (1) devising and implementing new causal discovery methods that are specifically tailored to the characteristics of biomedical data, (2) benchmarking novel and existing causal discovery and predictive modeling methods in order to evaluate their efficacy on biomedical data, (3) designing analytical experiments to discover critical contributing factors to pathologies and diseases from multimodality high dimensional high volume data to aid the development of diagnostic technologies and identification of potential treatment targets. Dr. Ma received her PhD in Psychology in 2014, her MS in Computer Science in 2013 from Rutgers University, and her BSc in medical research in 2008 from Peking University Health Science Center.

Michael Matheny, MD, MS, MPH

Vanderbilt University

Michael E. Matheny, MD, MS, MPH, is Director of the Vanderbilt Center for Population Health Informatics, Associate Director of the TVHS Veterans Affairs Biomedical Informatics Fellowship, and Assistant Professor of Bioinformatics, Medicine, and Biostatistics at Vanderbilt University. He received an MD from the University of Kentucky, an MS in Biomedical Informatics from Massachusetts Institute of Technology, an MPH from Harvard University, and is a Fellow of American College of Physicians with board certifications in Internal Medicine and Clinical Informatics. He has expertise in developing and adapting methods for post-marketing medical device surveillance as well as the development and evaluation of NLP tools, predictive analytics, and automated surveillance applications within large observational cohorts. He currently has funding from Veterans Affairs HSR&D, PCORI, NHGRI, FDA, and Astra Zeneca.

Jason H. Moore, PhD, MS

University of Pennsylvania

Jason Moore has an MS in Applied Statistics and a PhD in Human Genetics from the University of Michigan. We was then an Assistant and Associate Professor of Molecular Physiology and Biophysics at Vanderbilt University where held an Ingram Chair in Cancer Research and served as Director of the Advanced Computing Center for Research and Education. He then moved to the Geisel School of Medicine at Dartmouth where he was the Frank Lane Research Scholar in Computational Genetics and later the Third Century Professor of Genetics and founding Director of the Institute for Quantitative Biomedical Sciences. Dr. Moore is now the Edward Rose Professor of Informatics and Chief of the Division of Informatics in the Department of Biostatistics and Epidemiology at the Perelman School of Medicine of the University of Pennsylvania. He serves as founding Director of the Penn Institute for Biomedical Informatics and Senior Associate Dean for Informatics. Dr. Moore has been recognized for his work in human genetics and translational bioinformatics as an elected fellow of the American Association for the Advancement of Science and as a Kavli Fellow of the National Academy of Sciences.

Nicholas Tatonetti, PhD

Columbia University Medical Center

Dr. Nicholas Tatonetti is assistant professor of biomedical informatics in the Departments of Biomedical Informatics, Systems Biology, and Medicine and is Director of Clinical Informatics at the Herbert Irving Comprehensive Cancer Center at Columbia University. He received his PhD from Stanford University where he focused on the development of novel statistical and computational methods for observational data mining. He applied these methods to drug safety surveillance where he discovered and validated new drug effects and interactions. His lab at Columbia is focused on expanding upon his previous work in detecting, explaining, and validating drug effects and drug interactions from large-scale observational data. Widely published in both clinical and bioinformatics, Dr. Tatonetti is passionate about the integration of hospital data (stored in the electronic health records) and high-dimensional biological data (captured using next-generation sequencing, high-throughput screening, and other "omics" technologies). Dr. Tatonetti has been featured by the New York Times, Genome Web, and Science Careers. His work has been picked up by the mainstream and scientific media and generated hundreds of news articles.

Craig P. Webb, PhD

NuMedii, Inc.

Dr. Webb is the Chief Scientific Officer at NuMedii, where he has primary responsibility for leading the company’s technology development, and translating new drug indication hypotheses into the clinic. Prior to this role, he spent 13 years at the Van Andel Research Institute (VARI) in Michigan, where he directed the community’s efforts in translational science and precision oncology. Under his leadership, VARI formed four new companies in the areas of translational informatics (TransMed Systems and Intervention Insights), molecular diagnostics (The Center for Molecular Medicine) and clinical trial operations (ClinXus). He has a BSc in Biochemistry, a PhD in cell biology, and a post-doctoral fellowship in molecular oncology. He has published more than 60 peer reviewed publications and book chapters and has presented over 80 invited lectures on precision medicine and drug repositioning.

Chunhua Weng, PhD

Columbia University

Dr. Chunhua Weng is an Associate Professor of Biomedical Informatics and co-Director for the Biomedical Informatics Core of the CTSA at Columbia University. Before arriving at Columbia, she obtained an undergraduate degree in computer science from Nankai University, P. R. China, a master’s degree in Information and Computer Science from University of California at Irvine, and a Ph.D. in Biomedical and Health Informatics from University of Washington at Seattle.  Dr. Weng’s current primary research interests are (1) designing and applying text knowledge engineering methods to improve the computability of clinical research designs and to support knowledge management and reuse for clinical research; (2) designing data-driven methods to improve the efficiency and patient-centeredness of clinical research; and (3) design socio-technical solutions to integrate patient care and clinical research workflows towards the creation of a rapid learning health system. 

Diane Wuest, PhD

GNS Healthcare


Use of “Big Data” in the Development of Crizotinib (Xalkori) for ALK+ Metastatic Non-Small Cell Lung Cancer
Marc D. Chioda, PharmD and Leonard P. James, MD, PhD
Pfizer Oncology, New York, New York, United States

Lung cancer is the leading cause of cancer-related mortality worldwide, accounting for approximately 27% of all cancer deaths in the United States during 2014. Non-small cell lung cancer (NSCLC) comprises approximately 84% of lung cancer cases and the recent discovery of relevant biomarkers has reshaped our approach to clinical research and development of new medicines for patients with NSCLC. This presentation will discuss the clinical development program of Crizotinib (Xalkori) for the treatment of patients with metastatic non-small cell lung cancer (NSCLC) whose tumors are anaplastic lymphoma kinase (ALK)-positive. This case-study in drug-development underscores the importance of genetic profiling in this disease and highlights how an increased understanding of genetic drivers can be adapted to clinical trial designs to select molecularly defined cohorts of patients. Insights from the development of Crizotinib as a first-in-class ALK-inhibitor are now being applied to the development of next-generation ALK inhibitors such as PF-06463922.

Harnessing the Power of Healthcare Data with Advanced Data Analytics to Fight Disease
Iya Khalil, PhD, GNS Helathcare, Cambridge, Massachusetts, United States

We are living in the era of big data in healthcare, with unprecedented ability to collect data at multiple levels (molecular/'omic', phenotypic, and real world data from EMRs, mobile devices, patient reported outcomes, etc.) and at scale. This has the potential to transform our fundamental understanding of disease and our ability to match the right interventions to the right patients. Key to leveraging this data and uncovering which treatments and interventions specifically improve a patient’s health, are powerful analytic approaches. Utilizing causal mathematics and machine learning to create in silico disease networks directly from data has been a successful approach to identify predictive and causal mechanistic associations. Simulations of resultant models unlock the knowledge within complex data, enabling personalized, actionable predictions and precision targeting of interventions.

Artificial Intelligence Strategies for the Analysis of Biomedical Big Data
Jason H. Moore, PhD, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States

A central goal of artificial intelligence (AI) is to develop algorithms and software that can solve complex problems as a human would. AI is poised to have a significant impact on making discoveries in biomedical big data given the availability of powerful algorithms, visualization methods, and high-performance computing. We introduce here the Exploratory Modeling for Extracting Relationships using Genetic and Evolutionary Navigation Techniques (EMERGENT) algorithm as an AI approach for the large-scale genetic analysis of common human diseases. EMERGENT builds models of genetic variation from lists of mathematical functions using computational evolution. A key feature of the system is the ability to utilize pre-processed expert knowledge giving it the ability to explore model space much as a human would. We applied EMERGENT to the genetic analysis of glaucoma in 1272 subjects with the disease and 1057 healthy controls. A total of 657,366 single-nucleotide polymorphisms (SNPs) from across the human genome were measured in these subjects and available for analysis. EMERGENT revealed a best model consisting of six SNPs that map to at least six different genes. Two of these genes have previously been associated with glaucoma. The others represent new hypotheses. All of the SNPs are involved in non-additive gene-gene interactions. Further, the six genes are all directly or indirectly related through biological interactions to the vascular endothelial growth factor (VEGF) gene that is an actively investigated drug target. This study demonstrates the routine application of AI to biomedical big data.

Big Data For Drug Discovery and Drug Safety
Nicholas P. Tatonetti, PhD, Assistant Professor, Departments of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA

Billions of clinical measurements are recorded every day and stored in electronic health systems around the world. Each one of these experiments is a window into the human system, creating the most comprehensive and diverse medical data set ever imagined. Unfortunately, traditional statistical techniques were not developed to handle such diversity, instead they excel at analyzing homogenous data sets with first order effects. Because of this, these techniques are simply unable to untangle the sophisticated web of biological pathways and genetic interactions governing the human system. Here I focus on the effect of use if high performance techniques to digest billions of data points for the purpose of designing more effective and safer drugs.

Drug Repositioning in the Era of Precision Medicine
Craig P. Webb, PhD, NuMedii Inc., Palo Alto, California, United States

This presentation will summarize a variety of big data approaches deployed by NuMedii for drug repurposing and repositioning. We combine a variety of data driven and knowledge-based approaches to identify drug targets and new therapeutic entities for a given disease of interest. Following the in silico discovery of new drug-indication hypotheses, lead drug candidates are rapidly triaged by big-data enabled experts to assess the scientific, clinical, regulatory and commercial viability of repositioning assets. This talk will present a couple of use cases in ulcerative colitis and psoriasis to highlight the synergistic utility of chemogenomic, systems biology and knowledge-based methodologies which allow us to systematically identify new uses of existing drugs with a higher probability of clinical and commercial success.

Data-Driven Study Population Definition for Clinical and Translational Research
Chunhua Weng, PhD, Columbia University, New York City, New York, USA

The lack of patient-centeredness and population representativeness has significantly impaired the cost-benefit ratio of clinical trials and contributed to the difficulty in generalizing clinical trial evidence to clinical practice. Researchers tend to maximize the internal validity of a clinical study.  The unbalanced internal validity and external validity/generalizability often cannot be discovered until after study competition and results publication. Clinical study populations are often defined through trial and error. Researchers often copy and paste eligibility criteria from related research protocols with slight adaptations, forming a reinforcing culture for converging practices towards poorly justified, vague, ambiguous, or overly restrictive eligibility criteria. Such practices for study population definition can exacerbate health disparities among population subgroups, which are studied either rarely or excessively, over time.
To address this problem, I will present a novel data-driven methodology framework for study population definition and research eligibility criteria language generation using electronic patient data, especially data from electronic health records. I will use Type 2 diabetes as a proof of concept to show how the value distributions for A1c and age differ between the aggregated target populations from existing Type 2 diabetes trials and the real-world diabetic population. I will discuss how this framework can potentially (1) increase the transparency and contextual assessment of a new clinical trial’s generalizability early on; and (2) facilitate rapid, shared decision making among patients, clinicians, and clinical researchers in determining “studiable” populations and hence to improve the feasibility and efficiency of clinical research recruitment.

Keynote Address

The Future of Developing Drugs: Employing Artificial Intelligence and Biology
Niven R. Narain, PhD1

The past 50 years have afforded both a historical and fundamental lens into human biology driven by the discovery of DNA and explosion of technologies that unravel understanding into biochemical pathways. However, hypothesis-driven translation of this knowledge as a correlate to human disease has not kept pace with the exponential molecular library of information. The complexity and heterogeneity of disease underscore the fundamental need to use biological information as the building blocks of therapeutic development since reductionist approaches often fall short of inferring the causality of drug to target, unfortunately leading to unsuccessful clinical translation. Drug development takes an average 12-14 years and up to $ 2.6 Billion to advance a drug to market. The epidemiology of major diseases like cancer, diabetes, and the "crisis-in-waiting" parkinson's and alzheimer's in aging populations beg for a dynamic and disruptive approach towards understanding disease biology more effectively. The advent of omics technologies have enabled a revolutionary data gathering capability in a biological setting and when combined in a modular manner with clinical, demographic, functional, and molecular data; a more robust narrative of patient biology has been borne. To capture is not to comprehend, hence the employment of Bayesian mathematics combined with the supercomputing architecture represents and Artificial Intelligence (AI) application to biology that truly distinguishes healthy versus disease topographical models of disease. Artificial Intelligence is a bold and effective approach towards making sense of data to highlight areas in biology that may be actionable for drug development and also serve as candidates for biomarkers to be used to realize the vision of precision medicine.
Coauthors: Michael A. Kiebish, PhD1, Rangaprasad Sarangarjan, PhD1, Viatcheslav Akmaev, PhD1, and Vivek K. Vishnudas, PhD1
1 Berg Health, Framingham, MA, United States

Predicting Posttraumatic Stress from Multi-modular Data
Sisi Ma, PhD1

Individuals who experienced a traumatic event often display acute stress responses. The stress responses induced by trauma decline over time (remission) in most individuals, but remain prominent (non-remission) in about 10% to 20% of individuals. The prolonged stress responses are detrimental to the psychological and physiological well-being of individuals, can lead to emotional, functional, metabolic, and immune changes lasting across the life course and leading to higher rates or premature mortality.
One goal of the current study is to assess the feasibility of distinguishing the trauma survivors that would display non-remitting stress response from the ones that display remitting stress responses at a time that is early enough, so that early interventions can be administered effectively. Multimodal data including clinical information, patient history, and peripheral neuroendocrine markers were collected in the emergency room, at 1 week, 1 month, and 5 months from survivors of traumatic events. The model constructed with data from the four time points showed better performance progressively. The result also indicates that it is possible to identify individuals that would display non-remitting stress response as early as 1 week.
In addition, to investigate the causal mechanisms of the development and prognosis of PTSD, causal network were constructed among the measured variables. Both known and novel pathways leading to PTSD have been discovered. The identification of novel causal pathways to PTSD could contribute to development of new diagnosis and treatment technologies.
Coauthors: Isaac Galatzer-levy, PhD2, Alexander Statnikov, PhD1
1 Center of Heath Informatics and Bioinformatics, NYU Langone Medical Center, New York, New York
2 Department of Psychiatry, NYU School of Medicine, New York, New York


Understanding Multicellular Function and Disease with Human Tissue-Specific Networks
Arjun Krishnan*,4

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. Yet we still lack tools to systematically explore the landscape of genes and interactions that shape specialized cellular functions across hundreds of tissue types and cell lineages in the body. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, reveal genes’ changing functional roles across tissues, and illuminate disease-disease relationships. We introduce NetWAS, which combines genes with nominally significant GWAS p-values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT (, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks.
Coauthors: Casey S. Greene*,1,2,3, Aaron K. Wong*,5, Emanuela Ricciotti6,7, Rene A. Zelaya1, Daniel S. Himmelstein8, Ran Zhang9, Boris M. Hartmann10, Elena Zaslavsky10, Stuart C. Sealfon10, Daniel I. Chasman11, Garret A. FitzGerald6,7, Kara Dolinski4, Tilo Grosser6,7, Olga G. Troyanskaya4,5,12
1 Department of Genetics, The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States
2 Dartmouth-Hitchcock Norris Cotton Cancer Center, Lebanon, New Hampshire, United States
3 Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, United States
4 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States
5 Department of Computer Science, Princeton University, Princeton, New Jersey, United States
6 Department of Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
7 Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
8 Biology and Medical Informatics, University of California, San Francisco, United States.
9 Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States
10 Department of Neurology, Icahn School of Medicine at Mount Sinai, New
York, New York, United States
11 Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School
Boston, Massachusetts, United States
12 Simons Center for Data Analysis, Simons Foundation, New York, New York, United States


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