Adapting bioinformatics curricula for big data.

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs.

Human Intrigue: Meta-analysis approaches for big questions with big data while shaking up the peer review process.

Scientific innovation has long been heralded the collaborative effort of many people, groups, and studies to drive forward research. However, the traditional peer review process relies on reviewers acting in a silo to critically judge research. As research becomes more cross-disciplinary, finding reviewers with appropriate expertise to provide feedback on an entire paper is increasingly difficult. We sought to pilot a crowd peer review process that allowed reviewers to interact with one another in the spirit of collaborative science. We focused this session on manuscripts using meta-analysis, to fully embrace the importance of collaborative and open scientific research in the field of biocomputing. Our pilot study found that researchers enjoy a more collaborative peer review process and felt that the process led to higher quality feedback for submitting authors than traditional review offers.

Raising the stakeholders: Improving patient outcomes through interprofessional collaborations in AI for healthcare.

Research into AI implementations for healthcare continues to boom. However, successfully launching these implementations into healthcare clinics requires the co-operation and collaboration of multiple stakeholders in healthcare including healthcare professionals, administrators, insurers, legislators, advocacy groups, as well as the patients themselves. The co-operation and collaboration of these interprofessional groups is necessary not just in the final stages of launching AI based solutions in healthcare, but along each stage of the research design and analysis. In this workshop, we solicited talks from researchers who have embraced the idea of interprofessional collaboration across many different stakeholder groups at multiple stages of their research. We specifically focus on projects which included heavy collaborations from healthcare professionals, embraced the research subjects' communities as critical research partners, as well as included researchers who are advocating for systemized changes to include interprofessional stakeholders as evaluators of AI research in healthcare.