Prediction of Synthetic Lethal Interactions in Human Cancers Using Multi-View Graph Auto-Encoder

Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have rece...

Full description

Bibliographic Details
Main Authors:	Cai, R. (Author), Fang, Y. (Author), Hao, Z. (Author), Li, X. (Author), Wu, D. (Author), Wu, M. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	algorithm Algorithms antineoplastic agent Antineoplastic Agents Article Arts computing Attention mechanisms autoencoder bioinformatics Cell culture convolutional neural network copy number variation CRISPR associated endonuclease Cas9 Experimental methods false positive result feature extraction Forecasting gene expression gene interaction gene mutation gene ontology genetics Graph algorithms graph auto-encoder graph neural network Graph structures Graphic methods human human cancers Humans Jurkat cell line K-562 cell line Learning algorithms lethality Low consistency machine learning Machine learning Machine Learning multi view graph auto encoder multi-view neoplasm Neoplasms prediction Prediction errors Prediction methods protein p53 protein protein interaction receiver operating characteristic Reconstruction error Signal encoding Smad3 protein State of the art Synthetic lethal Synthetic lethality
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Prediction of Synthetic Lethal Interactions in Human Cancers Using Multi-View Graph Auto-Encoder

Internet

Similar Items