Sunday, March 27, 2016

Pacific Symposium on Biocomputing 2017

January 3-7, 2017

Fairmont Orchid Resort

Kohala Coast, Big Island, Hawaii, USA

Motivation

Medicine is increasingly becoming a data science, offering potential for improved health and therapies. Genome sequencing and large-scale molecular and systemic phenotyping are already available for large patient studies, and are now being integrated into clinical care, including obstetrics and family planning. Electronic health records are becoming increasingly accessible for analysis to quantitatively evaluate medical practices and for clinical decisions support.  Beyond genomics, many new consumer sensors are being used for high temporal resolution biomedical monitoring. The increasing repertoire of molecular and cellular data, combined with electronic representations of physiological/phenotypic state at high temporal resolution, is leading to new insights into the molecular underpinnings of health and wellness. To truly achieve the vaunted goals of precision medicine, substantial gains still need to be made in methods of data integration, analysis and interpretation, namely the computational biology component.

This session will explore new and open problems pertaining to various genome-wide and other large scale data, including rare and common SNPs, structural variants, epigenetic scans, multi-omic data, intermediate phenotypes, clinical variables from electronic medical records, disease and quantified-self sensor-based data

 Submissions

We will particularly embrace submissions that span several of these types of data. The focus will be on methods that are scalable to real-world problems and help to elicit results from genome sequence analysis along with and high-dimensional phenotype data.We will welcome four types of contributions: (1) descriptions of new problems and ideas on how to tackle them, (2) development of improved solutions to existing problems, (3) adaptations that allow existing methods to scale to real-world data sets, and (4) reports on results from such methods including validated diagnoses based on novel genetic information. We further explicitly invite contributions that have direct projected use for therapeutic decisions and treatment. 

Examples of topics and problems within the scope of this session:

  • Methods for incorporating methods or data from biological perturbations generated by advanced genome editing techniques into predictive or integrative models, ideally across many phenotype layers including health records.
  • Models, tools, and resources for longitudinal personal multi-omic data: high-dimensional time series analysis, disease onset prediction, finding genotype-dependent interactions between phenotypes and environment, incorporation of personal sensor data, tools to enable application of the analyses.
  •  Methodology for making use of rare and low frequency variants arising from whole-genome and exome sequencing: combining information across individuals, prioritization of mutations, and interpreting the impact of genetic variation on phenotypic outcomes and therapy.
  • Gene expression studies: modeling observed or unobserved regulatory factors, characterizing cell types in heterogeneous tissues, removing confounding effects, inferring the activity of disease-relevant regulators.
  •  Development of causal models for genotype, gene expression, disease labels and intermediate phenotypes. 
  • Comprehensive evaluation and comparison of existing methods. 
  • Innovative software and resources for personalized medicine: analysis software for clinical geneticists and researchers (functional analysis, prioritization, clinical relevance, and visualization of genetic variation; analysis and visualization of longitudinal phenotypes from electronic medical records and personalized-omics data), databases of actionable and likely benign mutations; genomic prediction tools. 
  • Computational methods for estimating and/or incorporating genetic ancestry for genomic discovery or precision medicine implementation. 
  • Impactful efforts to implement translation of population based precision medicine methods to the provider through the electronic health record or mHealth applications.

All submissions are reviewed and accepted on a competitive basis.

Session Chairs 

Bruce Aronow is Co-Director of the Computational Medicine Center and Professor of Pediatrics at Cincinnati Children's Hospital. Dr. Aronow has developed bioinformatics applications and databases that facilitate the detection of conserved structural features that contribute to gene regulation and function, harmful SNPs or mutations that confer disease risk, and improved recognition of gene-anatomy and gene-disease associations.

 Steven E. Brenner is a computational biologist and Professor at the University of California, Berkeley with an Adjunct appointment at the University of California, San Francisco.  He has interests in protein function prediction, RNA gene regulation, and personal genome interpretation—particularly as applied to newborns.

Dana C. Crawford is Associate Professor of Epidemiology and Biostatistics and Assistant Director in the Institute for Computational Biology at Case Western Reserve University.  As a genetic epidemiologist, she accesses epidemiologic and clinical collections to characterize common and rare genetic variants associated with human common disease and traits with an emphasis on diverse populations.

Joshua C. Denny is Associate Professor of Biomedical Informatics and Medicine and Director of the Center for Precision Medicine at Vanderbilt University.  His interests include natural language processing, accurate phenotype identification from electronic health record data, and using the electronic health record to discover genome-phenome associations to better understand disease and drug response, including the development of the EHR-based phenome-wide association studies. 

Sean D. Mooney is a biomedical informatician and is the Chief Research Information Officer and a Professor in the Department of Biomedical Informatics and Medical Education at the University of Washington Medical Center.  His interests focus on clinical genome interpretation and developing informatics platforms for enabling the next generation of clinical research. 

Alexander A. Morgan is a biomedical informatics researcher and medical student at Stanford University.   He is working in genome interpretation, the use of wearables and consumer electronic devices for health surveillance, and improving models of disease.


Important Dates  

August 7, 2016 Paper submissions due

September 12, 2016 Notification of paper acceptance

October 3, 2016 Camera-ready final paper deadline


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