Knowledge-based biomedical Data Science release_d5kjfs24nvdsne4mmzrdfb5wsq

by Lawrence E Hunter

Published in EPJ Data Science by IOS Press.

2017   Volume 1, Issue 1-2, p1-7

Abstract

Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled "Big Data to Knowledge (BD2K)." The main emphasis of the more than $200M allocated to that program has been on "Big Data;" the "Knowledge" component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application.
In text/plain format

Archived Files and Locations

application/pdf  100.8 kB
file_iihaoqjrsfhqzmfeklbu6dyb3q
web.archive.org (webarchive)
content.iospress.com (web)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2017-10-17
Language   en ?
DOI  10.3233/ds-170001
PubMed  30294517
PMC  PMC6171523
Container Metadata
Open Access Publication
In DOAJ
In Keepers Registry
ISSN-L:  2193-1127
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 5a5045a5-73ab-45c0-96fd-cda646263415
API URL: JSON