Uncover disease genes by maximizing information flow in the phenome-interactome network.
about
Fundamentals of protein interaction network mappingDisease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network modelPrioritizing protein complexes implicated in human diseases by network optimizationWalking on a tissue-specific disease-protein-complex heterogeneous network for the discovery of disease-related protein complexesIntegrating human omics data to prioritize candidate genesNetwork analysis of GWAS dataPinpointing disease genes through phenomic and genomic data fusion.Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data.Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network.Drug repositioning by integrating target information through a heterogeneous network model.Constructing a gene semantic similarity network for the inference of disease genesA systems biology approach to the global analysis of transcription factors in colorectal cancer.Genetic association studies in lumbar disc degeneration: a systematic reviewPrioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.Identifying potential cancer driver genes by genomic data integrationInferring host gene subnetworks involved in viral replication.Walking on a user similarity network towards personalized recommendations.Network-based Phenome-Genome Association Prediction by Bi-Random Walk.A new method to improve network topological similarity search: applied to fold recognitionPhenome-driven disease genetics prediction toward drug discovery.Uncover miRNA-Disease Association by Exploiting Global Network SimilaritySAMNet: a network-based approach to integrate multi-dimensional high throughput datasets.SoftPanel: a website for grouping diseases and related disorders for generation of customized panels.Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.Recent approaches to the prioritization of candidate disease genes.Transfer learning across ontologies for phenome-genome association prediction.Walking on multiple disease-gene networks to prioritize candidate genes.Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Context-sensitive network-based disease genetics prediction and its implications in drug discovery.PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.Mimvec: a deep learning approach for analyzing the human phenomeDrug target predictions based on heterogeneous graph inference.Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data.Metrical Consistency NMF for Predicting Gene-Phenotype Associations.A new method for classifying different phenotypes of kidney transplantation.Identifying human microRNA-disease associations by a new diffusion-based method.Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression.A comprehensive dataset of genes with a loss-of-function mutant phenotype in Arabidopsis.
P2860
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P2860
Uncover disease genes by maximizing information flow in the phenome-interactome network.
description
2011 nî lūn-bûn
@nan
2011 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@ast
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@en
type
label
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@ast
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@en
prefLabel
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@ast
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@en
P2093
P2860
P356
P1433
P1476
Uncover disease genes by maximizing information flow in the phenome-interactome network.
@en
P2093
P2860
P304
P356
10.1093/BIOINFORMATICS/BTR213
P407
P577
2011-07-01T00:00:00Z