# A field guide to cultivating computational biology

arXiv: https://arxiv.org/abs/2104.11364 · HTML: https://mlsanity.com/paper/2104.11364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)

## Abstract (arXiv)

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.