Pacific Symposium on Biocomputing 2018

January 3-7, 2018

Fairmont Orchid Resort

Kohala Coast, Big Island, Hawaii, USA

Puako Petroglyph Archeological District

 

Motivation 

Precision medicine is often described as providing a patient the optimal tailored treatment the first time as opposed to standard treatment or trial and error.  The concept of precision medicine is not new, yet the implementation of precision medicine is relatively nascent.  Despite technological advances, the adoption of 'omic data in routine clinical practice has been slow.  To accelerate its clinical adoption, additional data relevant for implementation are required, including basic discovery and the translation of these findings into better prevention or treatment strategies.  Further layered on to this is a real need for the inclusion of diversity from multiple angles including race/ethnicity, geography, and socioeconomic status.

This session aims to emphasize new and emerging methods development and analyses across multiple data types that inform precision medicine discovery or implementation efforts including genotype-phenotype data; other 'omic data; lifestyle and environmental variables; and electronic health records data.

 

Submissions 

The session is open to a broad range of computational topics applicable to precision medicine research.  As precision medicine research is active in both discovery and implementation encompassing a broad range of topics, the session is expected to review original research and methodology related to precision medicine's most pressing or anticipated problems or needs.  We welcome submissions that advance the field of precision medicine, ranging from basic scientific understanding of relevant biology through technical approaches to clinical implementation.  We particularly encourage submissions that address opportunities and challenges relevant to the generalization of precision medicine to diverse groups or that directly address health disparities.  We appreciate submissions that involve multiple data types and have applications to real world data.  We will also welcome submissions from biologists and clinicians who have identified interesting new analysis problems such as new measurement technologies or datasets requiring novel computational analyses.

Examples of topics and problems within the scope this session include (but are not limited to)

•  Methods for multi-ethnic analyses to identify genomic contributors that explain or inform observed health disparities.
• Methods or approaches to clinically annotate and interpret whole exome or whole genome sequencing data, including in populations from diverse genetic and environmental backgrounds.
• Methodology for making use of rare and low frequency noncoding variants arising from whole-genome and exome sequencing.
•  Approaches 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 electronic health records.
• Methods for the incorporation of lifestyle, environmental, and electronic health record data to identify contributors alone and/or interacting with the genome that explain observed health disparities.
• Development of causal or predictive models for genotype, gene expression, disease labels, and intermediate phenotypes across populations.
• Computational methods for estimating and/or incorporating genetic ancestry for genomic discovery or precision medicine implementation.

All submissions are reviewed and accepted on a competitive basis.

 

Session Chairs

Bruce J. 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 is Professor of Biomedical Informatics and Medicine and Director of the Center for Precision Medicine at Vanderbilt University Medical Center.  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.   

Alexander A. Morgan is 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 4, 2017 Paper submissions due

September 15, 2017 Notification of paper acceptance

October 2, 2017 Camera-ready final paper deadline

 

 

 

More Important Dates

PSB paper format and template

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

More important dates 

PSB paper format and template