CHARTING THE EVOLUTION AND TRANSFORMATIVE IMPACT OF THE PACIFIC SYMPOSIUM ON BIOCOMPUTING THROUGH A 30-YEAR RETROSPECTIVE ANALYSIS OF COLLABORATIVE NETWORKS AND THEMES USING MODERN COMPUTATIONAL TOOLS.

Founded nearly 30 years ago, the Pacific Symposium on Biocomputing (PSB) has continually promoted collaborative research in computational biology, annually highlighting emergent themes that reflect the expanding interdisciplinary nature of the field. This study aimed to explore the collaborative and thematic dynamics at PSB using topic modeling and network analysis methods. We identified 14 central topics that have characterized the discourse at PSB over the past three decades.

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.

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.

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.