SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases

Motivation Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases). Results This study presents a novel machine learning approach using multimodal vector-quanti...

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Bibliographic Details

Published in:	Bioinformatics (Oxford, England) Vol. 41; no. 2
Main Authors:	Namba, Satoko, Li, Chen, Yuyama Otani, Noriko, Yamanishi, Yoshihiro
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 04.02.2025
Subjects:	Autoencoder Computational Biology - methods Disease - genetics Drug Discovery - methods Humans Original Paper Supervised Machine Learning
ISSN:	1367-4811, 1367-4803, 1367-4811
Online Access:	Get full text
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