Track 1: Concepts, Tools and Techniques for Translational Bioinformatics
Translational informatics research requires the co-ordinated analysis of molecular as well as clinical phenotypic information to understand the pathophysiology or disease as well as to predict responses to therapeutic interventions. This track will focus on methods, tools and techniques ranging from text-mining, cloud computing and Semantic Web Technologies as well as information integration and data warehousing. Presentations will demonstrate case studies, prototype implementations and mature, production grade tools and platforms.
Track 2: Integrative Analysis of multi modal measurements
With over 30 different high-throughput measurement modalities for measuring the disease state at multiple levels, there is constant innovation for devising integrative analytic methods that relate molecular measurements to clinical phenotypes—particularly by identifying biomarkers for diagnosis, prognosis and personalization of care. This track will focus on systems approaches to integrate multiple kinds of data to facilitate drug discovery, drug repositioning and biomarker discovery.
Track 3: Base pairs to Bedside
It is expected that 30,000 people will have whole genome sequenced just this year. Our current approaches to understand genetic information in the clinical setting need overhaul. With the increase in the ability to capture phenotypic information – both in the research and clinical settings – our capability to represent and relate phenotypes with genotypes to understand the natural history of diseases has become rate limiting. This track will focus on efforts to link phenotypes in clinical descriptions with the massive amounts of individual genomic information that will be available soon.
Track 4: Informatics with Big Data
Given the rapid advances in natural language processing and access to vast computing infrastructure as well as sophisticated ontologies, data-mining and machine learning tools have converged in manner that allows us to perform "Big Data" analyses. Simultaneously, because of the changes in public policy, information technology, and electronic heath record (EHR) adoption, large datasets are increasingly available in healthcare bringing us close to the "threshold of sufficient data Web-scale data mining efforts have shown that algorithms behave differently when applied to very large datasets and that with sufficient data simple approaches can work well. This track will focus on efforts that apply Web-scale data-mining methods on massive datasets.