Keynote Speeches

Big Data in Biomedicine -- An NIH Perspective

Abstract

Biomedical research is becoming increasingly data driven, analytical and hence digital. In recognition of this evolution NIH has established the Office for Data Science with trans NIH responsibility for maximizing the value of this digital enterprise. This effort brings together communities, policy changes and new infrastructure to be applied to existing and new areas of research such as precision medicine. We will review these changes from the perspective of research advances that are underway and highlight how this community can further engage in these activities.

Biography

Phil E Bourne.jpg Philip E. Bourne PhD is the Associate Director for Data Science (ADDS) at the National Institutes of Health. Formally he was Associate Vice Chancellor for Innovation and Industry Alliances, a Professor in the Department of Pharmacology and Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California San Diego, Associate Director of the RCSB Protein Data Bank and an Adjunct Professor at the Sanford Burnham Institute.

Bourne's professional interests focus on service and research. He serves the national biomedical community through contributing ways to maximize the value (and hence accessibility) of scientific data. His research focuses on relevant biological and educational outcomes derived from computation and scholarly communication. This implies algorithms, text mining, machine learning, metalanguages, biological databases, and visualization applied to problems in systems pharmacology, evolution, cell signaling, apoptosis, immunology and scientific dissemination. He has published over 300 papers and 5 books, one of which sold over 150,000 copies.

Bourne is committed to maximizing the societal benefit derived from university research. Previosuly he co-founded 4 companies: ViSoft Inc., Protein Vision Inc., a company distributing independent films for free and most recently SciVee.

Bourne is committed to furthering the free dissemination of science through new models of publishing and better integration and subsequent dissemination of data and results which as far as possible should be freely available to all. He is the co-founder and founding Editor-in-Chief of the open access journal PLOS Computational Biology.

Bourne is committed to professional development through the Ten Simple Rules series of articles and a variety of lectures and video presentations.

Bourne is a Past President of the International Society for Computational Biology, an elected fellow of the American Association for the Advancement of Science (AAAS), the International Society for Computational Biology (ISCB) and the American Medical Informatics Association (AMIA).

Awards include: the Jim Gray eScience Award (2010), the Benjamin Franklin Award (2009), the Flinders University Convocation Medal for Outstanding Achievement (2004), the Sun Microsystems Convergence Award (2002) and the CONNECT Award for new inventions (1996 & 97).

Computational challenges in microbiome research

Abstract

Millions of bacteria make our bodies their home. They help keep us healthy, and disruptions in the normal microbiota are believed to contribute to a number of diseases. Cost-effective sequencing technologies have made it possible to sequence the genomes of human-associated microbial communities, leading to the birth of a new scientific discipline - metagenomics. Analyzing the resulting data, however, poses significant computational challenges, in part due to the sheer size of the data-sets, and in part due to the fact that most of the existing computational framework has been established for single organisms. In my talk I will outline several analytical challenges posed by metagenomic applications, and will describe recent results from my lab in the development of tools for analyzing metagenomic data. In particular I will discuss insights from our analysis of diarrheal disease in developing countries, as well as the effective use of co-abundance approaches for linking together data from two large metagenomic studies.

Biography

Dr. Pop is an associate professor in the Department of Computer Science and the Center for Bioinformatics and Computational Biology at the University of Maryland, College Park (UMCP). Dr. Pop received his Ph.D. in Computer Science at Johns Hopkins University where he focused on algorithms for computer graphics and Geographic Information Systems (GIS) applications. He then joined The Institute for Genomic Research (TIGR) as a Bioinformatics Scientist, where he was responsible for the development of genome assembly algorithms. During this time, Dr. Pop participated in a number of bacterial and eukaryotic genome projects including important human pathogens such as Bacillus anthracis and Entamoeba hystolitica. Since joining the University of Maryland, Dr. Pop has continued to develop novel approaches for genome assembly and analysis, and has developed extensive expertise in the analysis of metagenomic data. His lab has developed a number of widely used open-source software tools, such as the assembly suite AMOS, the NGS aligner Bowtie, the taxonomic classifier Metaphyler, and the metagenomic assembly package MetAMOS. Most recently he co-led the data analysis working group for the Human Microbiome Project and led the sub-group responsible for the assembly of the data generated in this project.

Parallel Machine Learning Approaches for Reverse Engineering Genome-Scale Networks

Abstract

Reverse engineering whole-genome networks from large-scale gene expression measurements and analyzing them to extract biologically valid hypotheses are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. In this talk, I will present our research on the development of parallel mutual information and Bayesian network based structure learning methods to eliminate such bottlenecks and facilitate genome-scale network inference. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer. Such networks can be used as a guide to predicting gene function and extracting context-specific subnetworks.

Biography

Srinivas Aluru is a professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He co-directs the Georgia Tech Strategic Initiative in Data Engineering and Science, and co-leads the NSF South Big Data Regional Innovation Hub. Earlier, he held faculty positions at Iowa State University, Indian Institute of Technology, New Mexico State University, and Syracuse University. Aluru conducts research in high performance computing, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. He pioneered the development of parallel methods in computational biology, and contributed to the assembly and analysis of complex plant genomes.

Aluru serves on the editorial boards of the IEEE Transactions on Big Data, IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, and the International Journal of Data Mining and Bioinformatics. He is currently serving as the Chair of the ACM Special Interest Group on Bioinformatics, Computational Biology and Biomedical Informatics (SIGBIO). Aluru is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, the mid-career and outstanding research achievement awards from Iowa State University, and the College of Computing Dean’s award for faculty excellence at Georgia Tech. He is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute of Electrical and Electronics Engineers (IEEE).

Toward Personalized Pan-Omic Association Analysis under Complex Structures and Big Data

Biography

Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and Director of the CMU/UPMC Center for Machine Learning and Health. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. He servers (or served) as an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He was a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Award. He was the Program Chair of ICML 2014.

Invited Talks

Invited Talk 1: Understanding Genotype-Phenotype effects in Cancer via Network Approaches

Speaker:

Dr. Teresa Przytycka, NCBI/NLM/NIH

Abstract:

Uncovering and interpreting phenotype--genotype relationships are among the most challenging open questions in disease studies. In cancer, these relationships are additionally obscured by heterogeneity of the disease. Pathway-centric approaches have emerged as methods that can empower studies of heterogeneous diseases. Over the years, using such network based approaches we have designed methods that allow detection of subnetworks dysregulated in cancer, and to establish associations gene expression and genotype. Our approaches build on variety of algorithmic techniques including graph-theoretical techniques (module cover) and on machine learning topic model approach (probabilistic genotype-phenotype model) and information flow. I will demonstrate the utility of our methods using TCGA (The Cancer Genome Atlas) data.

Short Bio:

The research in my group concentrates on computational modeling and analysis of biological processes, with emphases on hypothesis and theory-driven questions enabled by large-scale data analysis, using graph theoretical, machine learning, and algorithmic approaches. As a benefit of my previous research career in theory of algorithms and graph theory, in addition to addressing specific biological questions, an important contribution of my work is bringing advanced algorithmic knowledge to bioinformatics research. These techniques provide insights into systems biology and genome evolution that go beyond the capabilities of simpler and ad hoc approaches.

After receiving PhD in computer science, I immediately moved to a computer science faculty position first at UC Riverside and then Odense, Denmark. However, couple years later, after relocating for family reasons to the DC area, I become attracted by computational Biology. Subsequently, I was awarded Sloan postdoctoral fellowship in the Department of Biophysics, Johns Hopkins University that facilitated my transition to this newly emerged filed. As the result of this carrier path, I had the opportunity to contribute to on many diverse areas of science including theory of algorithms, graph theory, protein and RNA folding, evolution, and systems biology.

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Presentation File: Understanding Genotype-Phenotype relations in Cancer via Network Approaches

Invited Talk 2: Developing systems genomics approaches to facilitate precision medicine research

Speaker:

Dr. Mary Yang, University of Arkansas Little Rock George Washington Donaghey College of Engineering & Information Technology

Abstract:

Technology developments have rapidly produced data that facilitates the emerging precision medicine research. In particular, high-throughput next-generation sequencing (NGS) technologies have brought unprecedented opportunities in translational genomics research. However, connection of the genomic and phenotypic information to cellular functions and disease mechanisms relies on the development of effective approaches at higher systems level. My Systems Genomics Laboratory and the MidSouth Bioinformatics Center aim to integrate different genomic data to study the mechanisms underlying initiation and progression of complex diseases such as cancer. In this talk, I will present our study of integrating gene expression profiles with protein interactions to identify cancer biomarkers and disease associated pathways. By further combing with genotype information, we discovered genetic mutations associated with poor survival rate in patients with ovarian cancer. Our integrative genomics research also incorporates the study of long non-coding RNAs (lncRNAs). We identified differentially expressed lncRNAs in cancer and revealed that many over-regulated lncRNAs were bidirectionally oriented with neighboring protein-coding genes. These protein-coding genes are enriched in biological processes implicated in cancer. The systems genomics approaches enable us to establish a computational framework to comprehensively identify biomarkers and dysregulated pathways, which will facilitate the precision medicine research

Short Bio:

Dr. Mary Yang is the Director of MidSouth Bioinformatics Center and Director of the Joint Bioinformatics Ph.D. Program of University of Arkansas Little Rock George Washington Donaghey College of Engineering & Information Technology and University of Arkansas for Medical Sciences. After receiving her M.S.E.C.E, M.S., and Ph.D. degrees from Purdue University, she joined the National Human Genome Research Institute at the National Institutes of Health (NIH) in 2005 where she contributed to various large-scale projects in genomics and bioinformatics. During her tenure at NIH, she contributed to various large-scale genomics and systems biology research projects. She was recruited by the University of Arkansas in 2013 to lead the joint bioinformatics program. Dr. Yang has been Founding Editor-in-Chief of International Journal of Computational Biology and Drug Design, a NIH PubMed fully indexed journal and is on editorial broads of Journal of Supercomputing and International Journal of Pattern Recognition and Artificial Intelligence. She served as a Steering Committee Member of NIH funded Arkansas INBRE. She has been the recipient of NIH Fellows Award for Research Excellence, NIH Academic Research Enhancement Award, Bilsland Dissertation Fellowship, Purdue Research Foundation Fellowship, IEEE and ISIBM Bioinformatics and Bioengineering Outstanding Achievement Awards, and Basic Science Research Award of Arkansas Science and Technology Authority (ASTA). Dr. Yang’s research is supported by NIH, FDA and ASTA. She has published over 100 research articles in computer science and biomedical sciences.

Presentation File: Understanding Genotype-Phenotype relations in Cancer via Network Approaches

Invited Talk 3: Empowering self-management for chronic conditions through technology and analytics: Can machines care for us?

Speaker:

Huiru (Jane) Zheng, School of Computing and Mathematics, Ulster University, UK.

Abstract:

Population aging is widespread across the world. The impact of an ageing population affects all countries. As people age, they are progressively more likely to live with complex co-morbidities, disability and frailty. People with long-term conditions are the most frequent users of health care services. Technologies are changing out everyday lives and transforming healthcare. In this talk I will overview state-of-the-art technologies in telecare, and present our research work on supporting chronic care through technology and analytics. In particular, it will focus on personalized care and self-management. The challenges and opportunities of healthcare informatics will be discussed.

Short Bio:

Dr. Huiru (Jane) Zheng is a Reader in Computer Science with the School of Computing and Mathematics, Ulster University, UK. Dr. Zheng is an active researcher in healthcare informatics (including bioinformatics and medical informatics). Her research interests include machine learning, data mining and their applications to healthcare informatics. She is particularly interested in the following research areas: data integration, predictive data analysis, complex network analysis (PPI networks and drug target associations); and assistive technology to personalized healthcare. She has been a grant holder of research projects funded by EPSRC, TSB, DEL, NHS, Invest NI and European Commission including SMART Self Management, NOCTURNAL, CLARCH COPD Self Management, Self Management Platform for Connected Health, CardioWorkbench, mHealth4Africa, SenseCare and MetaPlat. She has 170+ publications in these areas.

Invited Talk 4: TBA

Speaker:

Dr. Chi-Ren Shyu, Department of Electrical and Computer Engineering Department, University of Missouri

Abstract:

TBA

Short Bio:

Invited Talk 5: Accurate and Fast RNAseq Analysis

Speaker:

Dr. Wei Wang, Department of Computer Science at University of California at Los Angeles

Abstract:

High throughput sequencing technique has been demonstrated as a revolutionary means for modern biology because it provides deep coverage and base pair-level resolution. It produces vast amount of data which pose new computational challenges, because subsequent analyses often rely on a sequence alignment step that re-establishes the origin of each read, a process that is both time consuming and error prone. In this talk, we will present our latest accomplishment in methodology advances that dramatically accelerate the analysis by removing the necessity of sequence alignment. We will demonstrate through a concrete example of RNASeq quantification, in which we are able to achieve two orders of magnitude speedup and deliver competitive accuracy.

Short Bio:

Wei Wang is a professor in the Department of Computer Science at University of California at Los Angeles and the director of the Scalable Analytics Institute (ScAi). She also serves as a co-director of the NIH BD2K Coordination Center. She received her PhD degree in Computer Science from the University of California at Los Angeles in 1999. Dr. Wang's research interests include big data, data mining, bioinformatics and computational biology, and databases. She has filed seven patents, and has published one monograph and more than one hundred research papers in international journals and major peer-reviewed conference proceedings. Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She was the recipient of a UNC Junior Faculty Development Award in 2003 and an NSF Faculty Early Career Development (CAREER) Award in 2005. She was named a Microsoft Research New Faculty Fellow in 2005. She was honored with the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC. She was recognized with an IEEE ICDM Outstanding Service Award in 2012 and an Okawa Foundation Research Award in 2013. Dr. Wang has been an associate editor of the IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Big Data, ACM Transactions on Knowledge Discovery in Data, Journal of Knowledge and Information Systems, Journal of Data Mining and Knowledge Discovery, International Journal of Knowledge Discovery in Bioinformatics, and an editorial board member of the International Journal of Data Mining and Bioinformatics and the Open Artificial Intelligence Journal. She serves on the organization and program committees of international conferences including ACM SIGMOD, ACM SIGKDD, ACM BCB, VLDB, ICDE, EDBT, ACM CIKM, IEEE ICDM, SIAM DM, SSDBM, RECOMB, BIBM.