Article

Graph Contextualized Attention Network for Predicting Synthetic Lethality in Human Cancers

2021-01-28

Abstract excerpt

<h4>Motivation</h4> Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0b480762-94a7-5a18-a578-721bd66abe9d
DOI: 10.1101/2021.01.27.428345

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Graph Contextualized Attention Network for Predicting Synthetic Lethality in Human CancersDOI 10.1101/2021.01.27.428345Read

KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality.Semantic proximity Read

Using graph-based model to identify cell specific synthetic lethal effectsSemantic proximity Read

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.Semantic proximity Read

Genome-scale prediction of context-specific synthetic lethality beyond protein interaction networksSemantic proximity Read

Artificial Molecular Network (AMoNet): graph learning for cancer survival prediction from targeted sequencingSemantic proximity Read

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response PredictionSemantic proximity Read

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing dataSemantic proximity Read

CONE: COntext-specific Network Embedding via Contextualized Graph AttentionSemantic proximity Read

ELISL: early-late integrated synthetic lethality prediction in cancer.Semantic proximity Read

Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers DiscoverySemantic proximity Read

Modeling cancer dependency with deep graph modelsSemantic proximity Read

Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional networkSemantic proximity Read