HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
about
Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised ClusteringMeta-signature LncRNAs serve as novel biomarkers for colorectal cancer: integrated bioinformatics analysis, experimental validation and diagnostic evaluationMCMDA: Matrix completion for MiRNA-disease association predictionOmics Approaches to Identify Potential Biomarkers of Inflammatory Diseases in the Focal Adhesion Complex.CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.Improved low-rank matrix recovery method for predicting miRNA-disease association.The integration of weighted human gene association networks based on link prediction.A potential role of microRNAs in protein accumulation in cellular senescence analyzed by bioinformatics.Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets.miRNA and mRNA expression analysis reveals potential sex-biased miRNA expressionLarge-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis.RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.DRMDA: deep representations-based miRNA-disease association prediction.LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction.miR-186 regulates chemo-sensitivity to paclitaxel via targeting MAPT in non-small cell lung cancer (NSCLC).Screening candidate microRNA-mRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approachA novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.Computational prediction of human disease-related microRNAs by path-based random walk.Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinomaAutophagy-related gene expression is an independent prognostic indicator of glioma.Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function.A deep ensemble model to predict miRNA-disease association.EPMDA: an expression-profile based computational model for microRNA-disease association prediction.Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.MicroRNA-7-5p mediates the signaling of hepatocyte growth factor to suppress oncogenes in the MCF-10A mammary epithelial cell.PRMDA: personalized recommendation-based MiRNA-disease association prediction.Metformin ameliorates skeletal muscle insulin resistance by inhibiting miR-21 expression in a high-fat dietary rat model.miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships.Diagnosis, prognosis and bioinformatics analysis of lncRNAs in hepatocellular carcinoma.Dengue virus causes changes of MicroRNA-genes regulatory network revealing potential targets for antiviral drugs.EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction.GIMDA: Graphlet interaction-based MiRNA-disease association prediction.LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction.Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction.SNHG16/miR-140-5p axis promotes esophagus cancer cell proliferation, migration and EMT formation through regulating ZEB1.ELLPMDA: Ensemble Learning and Link Prediction for miRNA-Disease Association prediction.Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.
P2860
Q29248741-FFCA38C7-B613-4F8C-B717-2FE75A7B7EA8Q33559988-F50A460C-6DD0-4CF4-B5D6-6405C0780AE5Q33591474-7F29C1AF-AE74-4DCD-96E2-76EB6870F771Q33631616-F6406740-47D1-43A9-9C90-59972E3D11BDQ33704765-B83BB9A9-21FE-4B8D-A909-ECD9C6F28951Q33920938-D10F4FB3-15EC-4DE8-B0E2-3F835B383F71Q36264145-5116C25E-0771-4092-AD1B-B5BF02DF397EQ36395036-C070C7D4-419C-4956-927F-063E13F09791Q37335617-8EB7A710-D010-4288-B133-251908962153Q37550217-907C47FE-4AAB-4121-B1A2-57DF060D3C0BQ37710699-B126CE0B-4B4A-4822-9F42-CBA123E5A45BQ38377688-9C5E49AF-0F5B-47FD-81F6-FEB481715BF1Q38377910-BE7D1494-6EF2-472C-B555-7E81A26E515DQ38600495-4FC2829B-BADB-4B2F-BA66-1196FE8ACE66Q38645220-DED1FABC-9134-461C-9FD0-D2A68345A456Q38740398-9754995F-36B0-434F-9449-34486C6A1C5CQ41197547-94C3B5F3-93D1-4D9F-ADEE-2D984C92BE76Q41570635-6F60A33F-BC4F-42D1-B544-92CAD33DB936Q41709384-260DA00C-5C53-484C-B9FB-42EF491DD530Q41823084-8B4A8E83-1658-4203-A1BB-8BB0BE46D5B3Q42365256-080273F7-46F7-4DB6-98FB-E0EA4279BA06Q44201372-83123BF5-19F2-4CBD-A0C4-7371308C6B76Q45334190-F9F9C16A-4FEB-478B-8FD7-7FA56EE963E7Q45734757-D72265DB-2A8F-4085-A8C0-A28DAB5631C0Q45943884-9177C2C4-0C88-4621-BBB6-C46DE3195B0EQ47097674-5D749B55-BB3F-4E01-AA3C-DC277767C82FQ47107336-52721942-98FD-410F-9296-83AA22024F96Q47114178-1AC7C29E-0C7A-40ED-B334-04BA522F22C2Q47118967-C9C276A0-9587-4FDB-AEAE-890BBD98A01BQ47137904-7EE16128-8B68-49DE-8C01-B104E32F4C2DQ47160779-A62C0B0C-3332-4B56-B1E8-F96D19249529Q47165163-48C847E1-B277-4BAB-81BB-27B0916E468FQ47172264-C0CC3695-EB1C-4C25-91E8-A4E851052C73Q47224473-1CCF2449-4F23-4A6A-BAE9-2B9D0EF7905BQ47238496-1D1EDCB2-AD6C-49B1-B4E1-9B179B86EC28Q47322786-88A909DF-9B33-4849-BB93-D2842013A8DFQ48507333-B6F5E5A4-C56D-40B8-AB4D-03F87D248E37Q49303062-F9D626AB-F9C1-4666-A5A4-ECD4EB6027F0Q52331347-CE3CEB50-35AA-46D2-8270-758229299636Q52565466-E8A10C51-3637-4BC0-A305-E17C4A1E5657
P2860
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 12 August 2016
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@en
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@nl
type
label
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@en
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@nl
prefLabel
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@en
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@nl
P2093
P2860
P356
P1433
P1476
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.
@en
P2093
Chenggang Clarence Yan
Gui-Ying Yan
Yu-An Huang
P2860
P304
65257-65269
P356
10.18632/ONCOTARGET.11251
P407
P50
P577
2016-08-12T00:00:00Z