PSB is offering four workshops during the meeting (exact dates to be determined). These workshops were created to provide an opportunity for a gathering that will not be based on peer-reviewed papers included in the proceedings book. The workshops will consist of presentations by invited speakers. Abstract submissions for the workshops will be evaluated by the workshop co-chairs.
Each workshop has a chair who is responsible for organizing submissions. Please contact the specific session chair relevant to your interests for further information. Links on each of the session titles below lead to more detailed calls for participation.
Big data in biomedicine presents a great opportunity to understand health and disease states at an unprecedented level. This workshop highlights landmark achievements in big data analytics, infrastructures, and applications in omics integration. With the growing number and size of biomedical datasets worldwide, we envision that approaches discussed in this workshop will facilitate the development of precision medicine.
Contact: Kun-Hsing Yu
Email: kunhsingyu at gmail dot com
Biomedical research is rapidly becoming data-intensive as investigators are generating and using increasingly large, complex, multidimensional, and diverse datasets. The Big Data to Knowledge (BD2K) initiative was established to enable the biomedical research community to use the various types of Big Data for research. As a special program under BD2K, the objective of NIH Mentored Research Scientist Development Award (K01) is to support for intensive research career development under the guidance of an experienced mentor, or sponsor, in biomedical Big Data Science
Contact: Lana Garmire
Email: lgarmire at gmail dot com
The purpose of this workshop is to introduce and discuss the future of bioinformatics as a mature discipline. We have previously defined this evolution and its impact as No-Boundary Thinking (NBT) in Bioinformatics (Huang et al. 2013; Huang et al. 2015). The NBT philosophy puts bioinformaticians in the driver seat for asking and answering research questions because they are in the best position to integrate and synthesize knowledge across many disciplines to articulate a question that might have broader impact than one formulated from the knowledge of a single discipline. NBT puts the emphasis on knowledge-based question definition with data serving a secondary role. This is counter to the current philosophy of letting big data drive the questions that are asked (Huang et al. 2015).
Contact: Jason H. Moore
Email: jhmoore at upenn dot edu
The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing. When such open data can be accessed and employed for discovery purposes, a broad spectrum of high impact end-points is made possible. These span the spectrum from identification of de novo biomarker complexes that can inform precision medicine, to the repositioning or repurposing of extant agents for new and cost-effective therapies, to the assessment of population level influences on disease and wellness. Of note, these types of uses of open data can be either primary, wherein open data is the substantive basis for inquiry, or secondary, wherein open data is used to augment or enrich project-specific or proprietary data that is not open in and of itself.
Call for Abstracts
Contact: Philip Payne