PSB Workshops

A large amount of genomic, transcriptomic and other omics data from different species of reef building corals, the uni-cellular dinoflagellates, plus coral microbiome data (where corals have possibly the most complex microbiome yet discovered, consisting of over 20,000 different species), is becoming increasingly available for corals. This is a terrific opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling and urgent investigation of critical factors that influence reef health and resilience. It is particularly relevant for this session to occur at PSB, given the abundance of and reliance on coral reefs in Hawai’i and the conference’s traditional association with the region. We welcome work in progress as well as general computational biology research that has the potential to advance the study of corals.

Contact: Lenore Cowen
Email: cowen at cs dot tufts dot edu

Translational bioinformatics (TBI) is a multi-disciplinary and rapidly emerging field of biomedical data sciences and informatics that includes the development of technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health implications. TBI is a relatively young discipline that spans a wide spectrum from big data to comprehensive analytics to diagnostics and therapeutics. TBI involves applying novel methods to the storage, analysis, and interpretation of a massive volume of ‘omics and clinical data, and it bridges the gap between bench research and real-world applications to human health. Many health-related topics are increasingly falling within the scope of TBI, including rare and complex human disease, cancer, biomarkers, pharmacogenomics, drug repositioning, and clinical decision support systems.

Contact: Dokyoon Kim
Email: Dokyoon dot Kim at pennmedicine dot upenn dot edu

The goal of User-Centered Design (UCD) is to create a product that satisfies users needs, has an interface that is effective, and, in general, is a tool that people want to use. This workshop will be an overview of UCD as well as how it has been successfully applied to several computational biology tools. Attendees will leave with an understanding of common UCD practices and models of how they might apply them to their own tools.

Contact: Mary Goldman
Email: mary at soe dot ucsc dot edu

Artificial intelligence (AI) and other computational and bioinformatic approaches have become a critical component of biomedical research. The wealth of available medical data and pertinent research questions have driven experts across many scientific fields to begin developing computational methods to drive innovation in medical research. However, AI in healthcare is often labelled as “disruptive”, a word simultaneously embracing its innovative nature, while warning against its turbulent impact on a broad range of health-care related disciplines. As a result, many healthcare stakeholders continue to be reserved, and even outright resistant, to AI advances for clinical outcomes.

Contact: Carly Bobak
Email: Carly.A.Bobak.GR at dartmouth dot edu

As rich data streams are accumulating across people and time, they provide a powerful opportunity to address limitations in our existing scientific knowledge and to overcome operational challenges in healthcare and life sciences. Yet the relative weighting of insights vs. methodologies in our current research ecosystem tends to skew the computational community away from algorithm evaluation and operationalization, resulting in well-reported proliferation of scientific outcomes of unknown reliability. Algorithm selection and use is hindered by several problems that persist across our field. One is the impact of the self-assessment bias, which can lead to mis-representations in the accuracy of research results. A second challenge is the impact of data context on algorithm performance. Biology and medicine are dynamic and heterogeneous. Data is collected under varying conditions. For algorithms, this means that performance is not universal -- and should be evaluated across a range of conditions. These issues are increasingly difficult as algorithms are trained and used on data collected outside of the research setting - where data collection is not well controlled and data access may be limited by privacy or proprietary reasons. This workshop will focus on approaches that are emerging across the researcher community to quantify the accuracy of algorithms and the reliability of their outputs.

Contact: Lara Mangravite
Email: lara dot mangravite at sagebionetworks dot org