A field guide to cultivating computational biology

TL;DR

This paper discusses how computational biology, as an interdisciplinary field, leverages large-scale biomedical data to advance scientific discovery and health, while addressing career and institutional challenges.

Contribution

It highlights the importance of computational biology and proposes solutions for supporting researchers, institutions, and publishers to foster its growth.

Findings

Computational biology has significantly contributed to scientific knowledge and health.

Challenges in career advancement hinder the field's growth.

Proposed solutions aim to support researchers and institutions.

Abstract

Biomedical research centers can empower basic discovery and novel therapeutic strategies by leveraging their large-scale datasets from experiments and patients. This data, together with new technologies to create and analyze it, has ushered in an era of data-driven discovery which requires moving beyond the traditional individual, single-discipline investigator research model. This interdisciplinary niche is where computational biology thrives. It has matured over the past three decades and made major contributions to scientific knowledge and human health, yet researchers in the field often languish in career advancement, publication, and grant review. We propose solutions for individual scientists, institutions, journal publishers, funding agencies, and educators.