The twentieth Pacific Symposium on Biocomputing (PSB), will be held January 4-8, 2015 at the Fairmont Orchid on the Big Island of Hawaii. PSB will bring together top researchers from North America, the Asian Pacific nations, Europe and around the world to exchange research results and address open issues in all aspects of computational biology. PSB will provide a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB intends to attract a balanced combination of computer scientists and biologists, presenting significant original research, demonstrating computer systems, and facilitating formal and informal discussions on topics of importance to computational biology.
To provide focus for the very broad area of biological computing, PSB is organized into a series of specific sessions. Each session will involve both formal research presentations and open discussion groups.
Please see the PSB paper format template and instructions at http://psb.stanford.edu/psb-online/psb-submit/index.html.
The accepted file formats are: postscript (*.ps) and Adobe Acrobat (*.pdf). Attached files should be named with the last name of the first author (e.g. altman.ps or altman.pdf). Hardcopy submissions or unprocessed TEX or LATEX files or electronic submissions not submitted through the paper management system will be rejected without review.
Each paper must be accompanied by a cover letter. The cover letter should be the first page of your paper submission. The cover letter must state the following:
Submitted papers are limited to twelve (12) pages (not including the cover letter) in our publication format. Please format your paper according to instructions found at http://psb.stanford.edu/psb-online/psb-submit/. If figures can not be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit. Color images are accepted for publication at no additional charge.
Papers must be submitted to the PSB 2015 paper management system at http://psb.wufoo.com/forms/psb-paper-submission/.
Contact PSB (psb.hawaii @ gmail.com) for additional information about paper submission requirements.
Precision medicine promises to transform cancer treatment in the next decade through the use of high-throughput sequencing and other technologies to identify telltale molecular aberrations that in turn suggest therapeutic vulnerabilities of each patient's tumor. Beyond generalized cancer therapies that are indiscriminate in nature, we aim to address the "panomics" of cancer - the complex combination of patient-specific characteristics that drive the development of each person's tumor and response to therapy. The American Society of Clinical Oncology has used this definition of cancer panomics in their vision document: Shaping the Future of Oncology: Envisioning Cancer Care in 2030. The realization of this vision will require new infrastructure and computational methods to integrate this data effectively and query it in real-time for therapy and/or clinical trial selection for each patient.
Contact: Francisco De La Vega
Email: franciscodlv at annaisystems dot com
The goal of this session is to encourage research and tool development for greater understanding of the impact of environmental exposures on complex traits and disease outcomes, elucidating the relationship between genetic variation and environmental exposure, and the exploration of genetic interactions and outcomes.
Sarah Pendergrass: sap29 at psu dot edu
Shefali S. Verma: szs14 at psu dot edu
Molly Hall: mah546 at psu dot edu
This session focuses on biomedical discovery through crowdsourcing and mining crowd data - the analysis of intelligence or data obtained through open collaboration. This session will examine the use and advancement of crowdsourcing (e.g. microtask environments, games with a purpose, workflow sequestration) and crowd data (e.g. human genomics sequence data, electronic health records, social media data) in a variety of biomedical applications including text mining, data mining and aggregation.
Contact: Robert Leaman
Email: robert dot leaman at nih dot gov
The public is increasingly controlling their own health data, as well as finding opportunities to participate in ongoing scientific research. This session will explore global health projects that incorporate direct collaborations with the public, including the creation of novel databases using personalized health information or the generation of successful computational solutions using crowdsourcing approaches.
Contact: Richard Gayle
Email: gayler at spreadingscience dot com
We are organizing a session focused on personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine. Briefly, our 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. 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. The session is intended to have a broad target audience including method developers and practitioners in the fields of medical and human genetics, statistical genetics and related areas.
There has been great interest and research initiatives in the biomedical community around harnessing "big data," including data from the literature, high-throughput gene expression experiments, array CGH and high-throughput siRNA and many other types of data to generate novel hypothesis to address the most crucial biomedical questions and aid in the discovery of more effective and improved therapeutic options for the treatment of complex diseases. Given its ever increasing volume and diversity, interest in taming "big data" through methods and systems to extract, represent, and transform it to knowledge that can be used for reasoning and question answering will only increase over time, enabling scientists to effectively use it for discovery and validation.
Contact: Graciela H. González
Email: ggonzalez at asu dot edu