Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment.

ABSTRACT:

Background

Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data.

Objectives

We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools.

Methods

We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assemb

SUBMITTER: Scholten B 

PROVIDER: S-EPMC8893280 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature