Bio IT World Expo 2016  
Bio IT World Expo 2016

Track 5 - April 5 – 7, 2016


Computational Resources and Tools to Turn Big Data into Smart Data

Track 5 assembles thought leaders who will present case studies using computational resources and tools that take data from multiple –omics sources, including microbiomics and metabolomics, and align it with clinical action. Turning big data into smart data can lead to real-time assistance in disease prevention, prognosis, diagnostics, and therapeutics. With the ever-increasing volume of information generated for curing or treating diseases and cancers, bioinformatics technologies, tools and techniques play a critical role in turning data into actionable knowledge to meet unstated and unmet medical needs.

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Tuesday, April 5

7:00 am Workshop Registration and Morning Coffee

8:00 – 11:30 Recommended Morning Pre-Conference Workshops* Visualization for Biomedical Data Analysis: From the Basics to Applications

12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops* iConquerMS™: A Patient-Centered Research Model

* Separate registration required

2:00 – 6:00 Main Conference Registration


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5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

Wednesday, April 6

7:00 am Registration Open and Morning Coffee


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9:00 Benjamin Franklin Awards and Laureate Presentation

9:30 Best Practices Awards Program

9:45 Coffee Break in the Exhibit Hall with Poster Viewing


10:50 Chairperson’s Opening Remarks

11:00 canvasXpress: A Highly Interactive JavaScript Library for Analytic Visualization of Genomics (and Other High Dimensional) Data

Isaac Neuhaus, Ph.D., Senior Principal Scientist, Bristol-Myers Squibb

This talk will describe how this package integrates with our R environment.

11:30 eTRIKS and tranSMART in IMI’s PreDiCT-TB: Data Management, Modeling and Comparison

Francisco Bonachela Capdevila, Ph.D., Postdoc Data Coordinator, Translational Informatics and External Innovation, Janssen Pharmaceutica

PreDiCT-TB is an IMI-funded project which takes a comprehensive model-based approach to fill the gaps in the current drug development pathway in tuberculosis. Preclinical information is propagated into the clinical stage in order to optimize drug selection at the clinical phase. In this context, we have developed a tranSMART-based solution that extracts PK-PD modeling data from studies at any in vitro, in vivo or clinical stage of the drug development chain. This allows the ranking of drug regimens to be compared across preclinical and clinical studies. This ranking comparison will provide us with an informed framework for the translatability of drug regimens data during the clinical phase.

12:00 pm Fusing Systems Biology & Predictive Analytics for Multi `Omic Data: Demo of the PATH Platform for Knowledge Generation

Scott Marshall, Ph.D., Managing Director, Biomarker and IVD Analytics, Precision for Medicine

The future of healthcare will be transformed by flexible frameworks designed to discover complex signals in rich datasets through the merger of predictive genomic analytics and systems biology that are designed to incorporate information about molecular and cellular systems across multi `omic data. PATH™ a secure, scalable, cloud-based solution for predictive genomic analytics serves as a knowledge generation platform for translational and clinical research.

12:30 Session Break

12:40 Luncheon Presentation I: Medical Evidence Is Becoming the Currency of Healthcare Transformation

John Piccone, Partner, Life Sciences Offering Leader, Watson Health, IBM

This session will share experiences applying IBM Watson Real World Evidence solutions to help researchers explore huge volumes of unstructured and structured content to discover insights and information and produce medical evidence. Examples include identifying unmet medical needs; demonstrating product value and differentiation for pharmaceuticals and medical devices; improving drug comparative effective studies; and competitive intelligence.

Elsevier1:10 Luncheon Presentation II to be Announced

1:40 Session Break

Novel Bioinformatics and Data Analysis Approaches

1:50 Chairperson’s Remarks

Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

1:55 From Phenotype to Genotype: Using TranSMART for Managing Human Genetics Data

Andrew Hill, Science and Technology Lead, Research Business Technology, Pfizer

Genotype/phenotype analysis informs target identification, validation, mechanistic understanding, and precision medicine. Genetic variants and associated phenotype datasets are large, complex, and difficult to manage and access. The bioinformatics community needs to share information about both challenges and solutions. In this presentation we’ll describe our experience with using TranSMART as a repository for human genotype-phenotype data.

2:25 Presentation to be Announced

2:55 Increasing the Competitiveness of Pharma Companies: Real Time Search and Analytics Across Structured & Unstructured Data

Xavier Pornain, WW Vice President, Sales and Alliances, Sales, Sinequa

This presentation highlights how Sinequa’s platform helps leading pharma companies in the following areas: 1) Speed up submission of New Drug Applications to reduce costs for new drugs development; 2) Drive innovation, accelerate research and shorten Drug Time-to-Market; 3) Foster cooperation in R&D while respecting information governance and security; and 4) Optimize clinical trials and catalyze drug repositioning.

3:10 From Out that Shadow: Diagnosis, Discovery and Data Integration in Single-Cell Phenomics

Michael Stadnisky, Ph.D., CEO, FlowJo, LLC

The standardization, throughput, and content of single cell assays has brought flow cytometry and digital PCR into the mainstream. However, data analysis has remained in the shadows, relying on expert supervision and manual analysis, and rarely integrated into the life science data ecosystem. We show that an intuitive analysis platform can democratize diagnosis and discovery in single cell assays and significantly accelerate time to insight.

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

4:00 A New Rational Approach to Predict Regulatory SNPs and Therapeutic Targets in the Human Genome

Ernesto Prado Montes de Oca, Ph.D., Director & Principal Investigator, Personalized Medicine Laboratory (LAMPER), Medical and Pharmaceutical Biotechnology Unit, CIATEJ AC, CONACYT

Our algorithm performed 53% sensitivity and 84.6% specificity to detect functional rSNPs in the human genome. We demonstrate that our method is more sensitive and has better performance that is-rSNP and RegulomeDB. We also assessed the impact of homotypic redundancy using a novel approach called the homotypic redundancy weight factor (HWF). We can predict cell line-specific and ethnic-specific rSNPs in any disease and any gene(s) where enough genomic/epigenomic information is available.

4:30 From GWAS and Whole Genomes to Personalized Therapeutics: Non-Coding Variants for New Drugs

Leonard Lipovich, Ph.D., Associate Professor, Center for Molecular Medicine and Genetics, Wayne State University

The ENCODE (Encyclopedia of DNA Elements) Consortium revealed that two-thirds of human genes do not encode proteins, and catalogued non-coding regulatory elements genomewide. Nevertheless, bioinformatics of significant disease-associated genetic variants identified from whole-exome chips, Genome-Wide Association Studies, and whole-genome sequencing continues to focus on protein-coding genes, even when those genes are far, and separated by recombination breakpoints from, the significant variants. The audience will learn how to use public transcriptome and epigenome datasets from the UCSC Genome Browser and its underlying UCSC Genome Database, including but not limited to ENCODE data, for both manual and automated integrative reannotation of disease-associated SNPs, with the goal of ranking SNPs outside of protein-coding regions based on the likelihood of their localization in a non-coding genomic functional element. Given that numerous post-GWAS bioinformatics portals still concentrate on protein-centric SNP annotations and poorly account for non-coding data types, this is an important insight for anyone in academia and industry who is interested in improving variant annotation pipelines to better account for the vast numbers of functional, and therefore candidate disease-causative, genomic elements outside of protein-coding gene exons. The audience will also gain an appreciation of the phenomenon of “SNP clouds” that we discovered during our pipeline development. This phenomenon manifests as genomic positional aggregations of multiple significant disease-associated non-coding variants from public GWAS datasets for related but nonidentical quantitative phenotypes and diseases and that reside within discrete, < 1-Mb contiguous genomic intervals. For example, we found that multiple SNPs significantly associated with BMI, waist circumference, fasting glucose levels, fasting insulin levels, obesity, and/or type 2 diabetes frequently cluster together in short discrete genomic regions. These “SNP clouds” allude to pleiotropic regulation in-cis and/or to the existence of multiple disease-specific non-coding regulatory elements that all may target the same nearby gene, causing their distinct but partially overlapping effects on phenotypes. The ultimate goal and promise of this approach is to identify functional, directly disease causal, non-coding RNA genes and non-coding regulatory sequences from exome, GWAS and whole-genome sequencing data. These genes and sequences can be therapeutically targeted using genome editing and, post-transcriptionally, antisense oligonucleotides. The recent evolutionary history, in human populations, of the non-coding candidate disease causal variants that we have canvassed in trans-ethnic mapping efforts will allow targeting to be customized to both populations and individuals, finally making post-genomic GWAS-empowered personalized medicine a reality.

Full list of authors:
Leonard Lipovich*, Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI;
Virginia Fisher, Department of Biostatistics, Boston University School of Public Health, Boston, MA;
Aldi T. Kraja, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO;
James B. Brown, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA;
Jerome I. Rotter, LABioMed, Los Angeles, CA;
Ida Chen, LABioMed, Los Angeles, CA;
James B. Meigs, General Medicine Division, Massachusetts General Hospital, Boston, MA;
Ingrid B. Borecki, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO;
CHARGE Adiposity Working Group;
CHARGE T2D / Glycemia Working Group;
CHARGE Consortium.

* study leader, first and presenting author

5:00 Sponsored Presentation (Opportunity Available)

5:30 – 6:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing

Thursday, April 7

7:00 am Registration and Morning Coffee


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10:00 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced


10:30 Chairperson’s Opening Remarks

10:40 Dramatic Changes in US Patent Law: The Implications for Bioinformatics

John Conley, J.D., Ph.D., Professor, Law, University of North Carolina

Not too long ago, patents on software and software-based analytical methods--in medicine, finance, and business generally--were commonplace and concern about their effects was profound. Now, after a series of Supreme Court cases that brought about a dramatic shift in the approach taken by the lower courts and the Patent Office, those patents are facing legal extinction. These developments matter to bioinformatics: because of the centrality of software-dependent data analysis, whether that software can be patented—directly or indirectly—is a question of enormous economic significance to the industry. Whether software patents look like a good or bad thing will depend on where you are positioned in the industry—that is, are you primarily a creator of analytical tools or a user of others’ creations? This presentation will explain the recent developments in patent law and their legal, practical, and economic implications for the bioinformatics industry. The audience will gain an understanding of 1) why patents play an important role in bioinformatics; 2) the dramatic changes in the patentability of software-based analytical methods that have occurred over the past 3-5 years; 3) the implications of these changes for the bioinformatics industry, in legal, practical, and economic terms; and 4) the differential effects of these changes, depending on whether one is positioned as a producer or consumer of analytical inventions.

11:10 Building a Platform for Modeling Risk and Opportunities in Drug Development

Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Sabrina Molinaro, Ph.D., Institute for Clinical Physiology, National Research Council, Italy

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing


1:55 Chairperson’s Remarks

William Loging, Ph.D., Associate Professor of Genomics & Head, Production Bioinformatics, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai

2:00 A Discussion of the Three Types of Cancer Informatics Tools: Statistical, Dimensional, and Semantic, and a Commentary on Cancer Viewed as a Complex System

William Rice, M.D., Senior Vice President, Clinical Innovation, Central and West Texas Division, Hospital Corporation of America

For non-specialists, we'd like to present the 3 types of informatics tools that are used in cancer research. Statistical tools use correlative mathematics to infer relationships, dimensional tools use structural models of shapes and interactions to define physiology, and semantic approaches attempt to associate word position and context to define meaning. Parsing the topic of cancer informatics into these 3 general categories may help non-informaticists be better collaborators with computational modeling colleagues. Finally, an introduction to the topic of cancer as a complex system may highlight new kinds of opportunities to accelerate cancer research.

2:30 Talk Title to be Announced

Prahlad T. Ram, Ph.D., Associate Professor, Department of Systems Biology; Co-Director, Biostatistics, Bioinformatics and Systems Biology Program, The University of Texas MD Anderson Cancer Center

3:00 Molecular Impacts of Immune Modulating Drugs on Cancer Patients

William Loging, Ph.D., Associate Professor of Genomics & Head, Production Bioinformatics, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai

The area of Immuno-Oncology provides a novel strategy for cancer treatment by utilizing the patient’s Immune system to combat tumor growth. We investigated the impact of specific immune modulating drugs on patients with diagnosed tumors in order to understand the molecular changes that take place at the pathway level. These data are correlated to phenotypic effect and provide insights into the mechanism of immune system directed therapies for cancer.

3:30 Biosimilar Structural Comparability Assessment by NMR: From Small Proteins to Monoclonal Antibodies

Bostjan Japelj, Ph.D., Senior Scientist, Protein Biophysics and Bioinformatics, Sandoz Biopharmaceuticals

This talk will discuss 1) the insight on how to use NMR as a method to evaluate high order similarity between biosimilar and reference product on the market; 2) methods to evaluate degree of similarity between two NMR spectra of proteins shown by examples from three case studies; and 3) an update on the current state of the art NMR spectroscopy in biosimilar drug product formulations and associated challenges.

4:00 Conference Adjourns

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  • Making the World's Knowledge Computable
  • Bioinformatics in the Cloud
  • The Application of Text Analytics to Drug Safety Surveillance

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