Article

BIONIC: Biological Network Integration using Convolutions

2021-03-16

Abstract excerpt

Biological networks constructed from varied data, including protein-protein interactions, gene expression data, and genetic interactions can be used to map cellular function, but each data type has individual limitations such as bias and incompleteness. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive re...

Topics

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

Identifiers and source

Literature Corpus work: ffa84095-7943-52e2-840c-b2db989178a2
DOI: 10.1101/2021.03.15.435515

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

BIONIC: Biological Network Integration using ConvolutionsDOI 10.1101/2021.03.15.435515Read

Integrating multi-network topology for gene function prediction using deep neural networksSemantic proximity Read

deepNF: Deep network fusion for protein function predictionSemantic proximity Read

PAGAN predicts digenic interactions by generalizing single-gene representations in biological networksSemantic proximity Read

Deep functional synthesis: a machine learning approach to gene functional enrichmentSemantic proximity Read

EssSubgraph improves performance and generalizability of mammalian essential gene prediction with large networksSemantic proximity Read

Network-guided supervised learning on gene expression using a graph convolutional neural networkSemantic proximity Read

Effective expression analysis using gene interaction matrices and convolutional neural networksSemantic proximity Read

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integrationSemantic proximity Read

iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks.Semantic proximity Read

Speos: An ensemble graph representation learning framework to predict core genes for complex diseasesSemantic proximity Read

PEARL: Integrative multi-omics classification and omics feature discovery via deep graph learningSemantic proximity Read

Dango: Predicting higher-order genetic interactions.Semantic proximity Read