Track 4: Bioinformatics

The Bioinformatics track assembles thought leaders who will present case studies using computational resources and tools that take data from multiple –omics sources 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.

Final Agenda

Tuesday, April 16

7:00 am Workshop Registration Open and Morning Coffee

8:0011:30 Recommended Morning Pre-Conference Workshops*

W2. Data Visualization to Accelerate Biological Discovery

12:304:00 pm Recommended Afternoon Pre-Conference Workshops*

W10. Digital Biomarkers in Pharma R&D: Technical Challenges and Strategies for Advancing Personalized Medicine

* Separate registration required.

2:006:30 Main Conference Registration Open


5:007:00 Welcome Reception in the Exhibit Hall with Poster Viewing

Wednesday, April 17

7:30 am Registration Open and Morning Coffee


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


10:50 Chairperson’s Remarks

Andrew LeBeau, PhD, Associate Vice President, Biologics, Marketing, Dotmatics

11:00 Visualizing and Integrating Pathway Information

Sebastian Scharf, PhD, Data Scientist, Roche

One approach in drug discovery that is becoming more and more popular is looking at pathways instead of single genes to address the needs in the clinic today. More and more projects involve this more complex view when unraveling disease biology as well as assessing novel therapeutic targets. In this talk, we present a tool to visualize and integrate information from pathways and other sources to allow streamlined decision making based on different available data.

11:30 Identifying Key Mechanisms of Alzheimer’s Disease with Omics Data

Sudeshna Das, PhD, Assistant Professor, Neurology, Massachusetts General Hospital/Harvard Medical School

We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways. Our methods identified important genes in the pathophysiology of AD that provide further insight into the calcium signaling and calcineurin pathways.

Dotmatics_red_background 12:00 pm Advanced Bioinformatics Techniques for Biologics Drug Discovery

Andrew LeBeau, PhD, Associate Vice President, Biologics, Marketing, Dotmatics

Biologics drug discovery requires advanced analytics methods to associate design changes (i.e., targeted mutations) in sequence structure with experimental performance in screening assays. Applying knowledge from small molecule drug discovery, adapted to sequence-based compounds, this presentation will cover a range of techniques, spanning global, neighborhood, and local computations.

12:15 Using Data and Analytics to Unlock the Microbiome Impact on Human Health

Alexandr Ivliev, Director, Bioinformatics, Clarivate Analytics

The microbiome and its role in a myriad of human diseases has quickly opened a new research front in Pharmaceutical R&D. The human microbiome includes thousands of microbial strains that vary in presence and abundance across subjects, geographies, and normal and pathological conditions. Despite the availability of large amounts and types of microbiome data, there is a lack of a comprehensive, integrated and methodical data analysis approach in microbiome. Clarivate Analytics is offering an end-to-end solution in the microbiome domain. Our solution takes advantage of our combined expertise in the fields of bioinformatics, biomedical data science and enterprise-level custom software development, as well as our world leading products such as CortellisTM and MetaBaseTM. In this talk, learn how you can unlock the power of your proprietary microbiome data in combination with the growing body of microbiome knowledge from the public domain and scientific literature, towards novel actionable therapeutic insights.

12:30 Session Break


12:40 Luncheon Presentation: Breaking the Data Silos! Lundbeck’s Transition to Integrated Drug Discovery Informatics Platform

Ludovic Otterbein, Director, Research Informatics & Operations, Lundbeck

The fragmented nature of life sciences R&D results in researchers not having access to most up to date information or duplicating experiments, leading to wasted time and resources. Lundbeck is implementing a chemistry centric repository to enable scientists to access vital chemical and biological information from the Reaxys and RMC databases, and internal Lundbeck data from a single, seamless interface.

1:10 Session Break


1:50 PANEL DISCUSSION: Revolutionizing Drug Development with New Disease Models and Simulation Methods: Case Studies of Collaborative Global Efforts

Chairperson and Moderator:

Anil Srivastava, President, Open Health Systems Laboratory


Jeffrey Buchsbaum, MD, PhD, AM, Medical Officer and Program Director, Radiation Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute/NIH

Rajendra Joshi, PhD, Associate Director and HOD Bioinformatics Group, Centre for Development of Advanced Computing (C-DAC), Pune University Campus

Tonglei Li, PhD, Allen Chao Chair and Professor of Department of Industrial and Physical Pharmacy, College of Pharmacy, Purdue University

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing, Meet the Experts: Bio-IT World Editorial Team, and Book Signing with Joseph Kvedar, MD, Author, The Internet of Healthy Things℠ (Book will be available for purchase onsite) 


4:00 PANEL DISCUSSION: The Difference between Biomarkers and Diagnostics: It’s Bigger Than You Think


Michael N. Liebman, PhD, IPQ Analytics, LLC and Strategic Medicine, Inc.


Michael Montgomery, MD, Global Executive Director, Incyte Corporation

Jonathan Morris, MD, Vice President, Provider Solutions; Chief Medical Informatics Officer, Real World Insights, IQVIA

Jun Zhu, PhD, Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai

Biomarkers are used to evaluate cancer risk, detect end stage cancer and suggest optimal therapy for specific patients. Recent advances in -omics research continue to enable researchers to classify molecular fingerprints of specific cancers. Discovery and development of new cancer markers remains a major research focus to improve screening, diagnosis, and treatment but is hampered by the limited knowledge of the details of disease progression. This challenges the ability to identify markers that may be causal rather than correlative and impacts their use as true diagnostics. By example, literature analysis of 250,000 papers listing biomarkers in cancer yield less than 100 FDA approved diagnostics. Every experiment yields a biomarker; however, every experiment does not yield a diagnostic that can more accurately drive clinical action. How can we close this gap? It is critical to develop a better understanding of the disease process and how observations from genomics, proteomics, and metabolomics may impact that process in different ways. This interactive panel will explore experimental and analytic methods, issues and challenges impacting identification, validation, development and implementation in cancer, diagnosis and treatment.

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

Thursday, April 18

7:30 am Registration Open and Morning Coffee


9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced


10:30 Chairperson’s Remarks

Chris Friedline, PhD, Scientific Consultant, Diamond Age Data Science

10:40 CO-PRESENTATION: Standard Measures and Bioinformatics Tools for Collaborative Research

Carol M. Hamilton, PhD, Senior Director, Bioinformatics, Research Computing Division, RTI International

Stephen W. Edwards, PhD, Bioinformatics Senior Scientist, Research Computing Division, RTI International

The Web-based PhenX (consensus measures for Phenotypes and eXposures) Toolkit,, is a catalog of standard measures to facilitate collaborative biomedical research.  Use of PhenX measures ensures consistent collection of phenotype and exposure data to enable comparability for the treatment and outcomes among patients in different studies.  Many health outcomes share common risk factors, so the ability to combine studies increases statistical power in research studies of genetic conditions and gene-environment interactions. This, in turn, increases the impact of individual studies.  The PhenX Toolkit currently contains more than 700 protocols from 25 research domains, including protocols that provide additional depth for Substance Abuse and Addiction, Mental Health, Tobacco Regulatory Research, and Sickle Cell Disease research.  The PhenX Toolkit provides information about the protocols and tools to help integrate the protocols into existing study designs.  PhenX collaborates with other resources and initiatives, including the Common Data Element (CDE) Resource Portal, the NIH CDE Task Force, the Research Electronic Data Capture (REDCap), and the database of Genotypes and Phenotypes (dbGaP ). REDCap modules are available for all protocols in the PhenX Toolkit providing an easy way to add PhenX data collection protocols directly to REDCap projects. With the increased emphasis on data sharing by the NIH, standard measures are more important than ever to maximize the impact of precision medicine initiatives such as TOPMed and All of Us. Incorporation of standard measures into the study design for clinical and translational research will increase the compatibility of the studies, facilitate advances of precision medicine and increase impact on human health and quality of life. 

11:10 Oncology Big Data Efforts in China: A Combination of Medical Informatics and Bioinformatics

Weike Mo, PhD, FAACC, Vice President, Precision Medicine, Digital China Health

As a governmental effort, China National Cancer Center is building a centralized medical data platform for all Chinese cancer patients with Digital China Health, a private partner for the project. To date, we have collected data for more than 6 million patients from over 30 oncology specialty hospitals. We have applied medical informatics, bioinformatics, and artificial intelligence technologies to clean up, structure, and utilize all the data. Although we are at an early stage of understanding the big data set, we have seen its potential to revolutionize precision medicine practice in hospitals, insurance companies, as well as pharmaceutical companies.

11:40 Human – Microbe Interactions in Target Discovery

Somdutta Saha, PhD, Scientific Consultant, Diamond Age Data Science

Rapid advances in science increasingly suggest that the collective genome of intestinal microbial communities, the gut microbiome, play a crucial role in maintaining normal functioning of human metabolic and immune systems. Dysbiosis, the state of imbalance among bacterial species or the microbiota in the host system, has been associated with many immunity-related diseases. Despite progress in the field, there is lack of knowledge in understanding the molecular mechanism of the host-microbe interaction for systematic screening. Present day experiment facilities and difficulty in human clinical trials with microbes also limit the efforts at finding the causal relationships underlying microbial “cross-talk”. Thus, it is imperative to explore other models for deciphering dysbiosis mechanistically.  From a drug discovery perspective, understanding the underlying molecular mechanism of the host-microbe interaction is of profound importance for enriching our knowledge of drug uptake and delivery. In this work, we have combined the power of cheminformatics and computational data analysis from human clinical trial data to assess the effect of microbiome. We have identified potential bacterial metabolite - host target interactions that revealed extensive immuno-modulation, tissue remodeling and anti-inflammatory activities in a standardized panel of human primary cell-based phenotypic assays representing various tissue and disease settings.

12:10 pm  Enjoy Lunch on Your Own (Lunch Available for Purchase in the Exhibit Hall)

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

methods and models to better utilize data for risk assessment, diagnosis and treatment decisions
harborview 3

1:55 Chairperson’s Remarks

Yuval Itan, PhD, Assistant Professor, Department of Genetics and Genomic Sciences; Member, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai

2:00 Pinpointing Transcript-Damaging Disease-Causing Variants as a Major Step towards RNA Therapeutics

Sahar Gelfman, PhD, Associate Research Scientist, Columbia University Medical Center

The difficulty in capturing pathogenic variants that indirectly damage mRNA formation results in overlooking synonymous and intronic variants when searching for disease risk in sequenced genomes. The Transcript-inferred Pathogenicity (TraP) model was developed to identify sequence context changes that affect splicing decisions and the formation of the final transcript. A random forest model is trained on previously described pathogenic and benign synonymous mutations and identifies damaging variants with over 97% specificity and with a sensitivity three-four times higher than other available scores. Importantly, the specific mode of action of TraP damaging variants can be rescued using carefully designed small molecules, thus identifying these variants is a big step towards personalized treatments for mutation carriers. Since its publication in 2017, TraP has become a major resource for genetic diagnostics that is helping to change the common conception that pathogenic genetic variation is caused solely by coding mutations. TraP has been incorporated in diagnostic pipelines in tens of research institutes worldwide, among which are the NIH, Nationwide Children’s Hospital, SickKids foundation, Massachusetts General Hospital and others. TraP is also available as a website for single queries ( that is used systematically by over 1,500 users from clinics and genetic institutes in over 40 countries worldwide, providing successful diagnosis of genetic disorders and affecting treatment decisions.

2:30 AI Assisted Rapid Clinical Whole Genome Sequencing for Critical Care

Ray Veeraraghavan, PhD, Director of IT & Informatics, Rady Children's Institute for Genomic Medicine

3:00 Deciphering the Complex Heterogeneity of Cancer

Patrice M. Milos, PhD, Co-Founder/President and CEO, Medley Genomics, Inc.

In 2017, 1.7 million people in the US were diagnosed with cancer, and even though cancer survival rates have increased, it still accounts for 1 in 4 deaths annually. Cancer, a heterogeneous disease, has significant tumor cell variability within individual patients, as well as across categories of patients, creating complex barriers to effective and lasting cures for patients. Understanding this heterogeneity will be required to individualize care for patients. Medley Genomics provides a software platform that uses patent-pending algorithms and advanced data analytics to describe a patient's diverse tumor cell mixture. This enables creation of unique molecular diagnostic fingerprints for improving patient diagnosis, monitoring and treatment of cancer, and helps to improve novel oncology therapies and therapeutic combinations including individual cancer vaccine development. 

3:30 Estimating Genotypic Heterogeneity Underlying Human Disease

Yuval Itan, PhD, Assistant Professor, Department of Genetics and Genomic Sciences; Member, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai

Whole exome and whole genome sequencing provide hundreds of thousands of genetic variants per patient, of them only very few are pathogenic. Current computational methods are inefficient in differentiating pathogenic mutations from neutral genetic variants that are predicted to be damaging, and cannot predict the functional outcome of mutations. We will present: (1) a deep learning approach to efficiently detect pathogenic mutations by utilizing extensive annotations and patients’ phenotypic data; (2) a machine learning method combined with natural language processing to estimate whether a mutation results in gain- or loss-of-function; and (3) a cases-controls gene burden study to detect genes and pathways enriched with rare and high impact disease-causing mutations in exomes of over 2,000 Ashkenazi Jewish patients suffering from inflammatory bowel disorder. Finally, we will present new tools to visualize and extract useful information of human, mutations, and DNA/protein sequences for better utilization of next generation sequencing data and understanding of human disease genomics.

4:00 Conference Adjourns

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