We need to keep a reproducible trace of facts, predictions, and hypotheses from gene to function in the era of big data

Simon Kasif and Richard J Roberts

PLOS Biology, 2020, vol. 18, issue 11, 1-10

Abstract: How do we scale biological science to the demand of next generation biology and medicine to keep track of the facts, predictions, and hypotheses? These days, enormous amounts of DNA sequence and other omics data are generated. Since these data contain the blueprint for life, it is imperative that we interpret it accurately. The abundance of DNA is only one part of the challenge. Artificial Intelligence (AI) and network methods routinely build on large screens, single cell technologies, proteomics, and other modalities to infer or predict biological functions and phenotypes associated with proteins, pathways, and organisms. As a first step, how do we systematically trace the provenance of knowledge from experimental ground truth to gene function predictions and annotations? Here, we review the main challenges in tracking the evolution of biological knowledge and propose several specific solutions to provenance and computational tracing of evidence in functional linkage networks.

Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000999 (text/html)
https://journals.plos.org/plosbiology/article/file ... 00999&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pbio00:3000999

DOI: 10.1371/journal.pbio.3000999

Access Statistics for this article

More articles in PLOS Biology from Public Library of Science
Bibliographic data for series maintained by plosbiology ().