Predicting cross-tissue hormone-gene relations using balanced word embeddings

Published in "bioRxiv 2021.01.28.428707"
Aditya Jadhav , Tarun Kumar , Mohit Raghavendra , Tamizhini Loganathan , Manikandan Narayanan"

Large volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing.