PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
about
Improved low-rank matrix recovery method for predicting miRNA-disease association.A potential role of microRNAs in protein accumulation in cellular senescence analyzed by bioinformatics.LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction.Investigation of a common gene expression signature in gastrointestinal cancers using systems biology approaches.A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.BRWLDA: bi-random walks for predicting lncRNA-disease associations.Annexin A1 down-regulation in head and neck squamous cell carcinoma is mediated via transcriptional control with direct involvement of miR-196a/b.The interaction between NOLC1 and IAV NS1 protein promotes host cell apoptosis and reduces virus replication.A deep ensemble model to predict miRNA-disease association.Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.MNDR v2.0: an updated resource of ncRNA-disease associations in mammals.MicroRNA-7-5p mediates the signaling of hepatocyte growth factor to suppress oncogenes in the MCF-10A mammary epithelial cell.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.Dengue virus causes changes of MicroRNA-genes regulatory network revealing potential targets for antiviral drugs.LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction.Conceptual and computational framework for logical modelling of biological networks deregulated in diseases.Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.PCPA protects against monocrotaline-induced pulmonary arterial remodeling in rats: potential roles of connective tissue growth factor.SNHG16/miR-140-5p axis promotes esophagus cancer cell proliferation, migration and EMT formation through regulating ZEB1.A novel method for identifying potential disease-related miRNAs via a disease-miRNA-target heterogeneous network.Prediction and characterization of human ageing-related proteins by using machine learning.Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.GRMDA: Graph Regression for MiRNA-Disease Association Prediction.SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction.MicroRNA and transcriptome analysis in periocular Sebaceous Gland Carcinoma.A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification.Human Microbe-Disease Association Prediction Based on Adaptive BoostingPrediction of potential disease-associated microRNAs by composite network based inferenceThe Bipartite Network Projection-Recommended Algorithm for Predicting Long Non-coding RNA-Protein InteractionsMDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association predictionThe intragenic mRNA-microRNA regulatory network during telogen-anagen hair follicle transition in the cashmere goatSc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA inIdentifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix FactorizationBPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network
P2860
Q33920938-44558323-2BD2-4D2B-A6A7-A31AC502A3C6Q36395036-30C6351B-4E1D-432C-93F8-599530237FC4Q38645220-7DB3FB8F-0D5C-4EF0-92D0-E089AA7B988FQ40059454-27815A6E-C0B6-4819-80A3-D6FFE983E368Q41570635-AC6F3400-978E-4DBA-8FB4-80BFF1C934E5Q41621167-D3D18604-8DC7-4BE0-BD22-ADD4F278F928Q41694107-2BDAE5A6-8DB2-4DAC-B5E3-C6CFE08FB753Q42175178-9735FA30-B605-4D51-9233-E1606A26846EQ45323725-847290C5-46D7-4741-8884-20C5039FB627Q45334190-25F3EE28-1340-413C-9EAC-41EBF77DB711Q45943884-C173211F-5E82-4FAA-8BDF-24E5E68C2962Q46268145-7602CFFC-5561-4ACB-B375-9939DBDC26F7Q47107336-64FCE2EC-050D-4C5F-AA3E-0B5500C8987DQ47118967-8368EFCF-7715-4CDD-8DB1-F77DBBE5E596Q47137904-4BEDB60A-6B90-41E4-A8D6-2DC07D68A892Q47165163-A6AA5AD2-83CF-43E4-A552-4DC8881FABDDQ47238496-C93CF037-D05A-4022-A772-6C4ABED27914Q47316698-642D9B84-332D-4AB7-84F4-3AAE64CBB961Q47322786-94094196-78D3-42ED-920E-F9318E2D9E7BQ47562362-7933D2DD-2821-40A2-B23D-9B1FE5BCD7A6Q49303062-2C006F6C-D83D-4740-AA65-A42FBAE6C0DEQ50048914-95B04770-8341-42BC-AA08-A3CD4CA16414Q50420614-D0213A9F-B07F-4DE5-8225-58C0B0C7A8CEQ52316573-6A926607-D79A-4C8C-9C2F-BF999D6FDAFFQ52565466-3214B980-8C37-4D93-99D7-101586B5F4A9Q52593100-051C2948-8011-498B-AE7C-22124EC90FF2Q52668402-0B55780A-E12E-49CA-9AF1-62C5CDF5AD0DQ55265121-23C7C4D8-70EA-411F-8F9B-47578D3638EBQ55270985-8FC9AE38-85F5-4E07-9287-D6E643A4E9E4Q55280238-5C8DDCA8-F110-44A2-85E7-70C30485E7A5Q55345239-10F5002C-7911-4774-844E-6B1E66F1927AQ55405255-53717489-8C82-455A-86D6-115EDAFB973FQ57816760-25B1AC4C-03D0-4507-9322-7CE4E79842DCQ58562840-2712DB4F-3551-46EA-B481-F959B8BCF699Q58604632-07CF27E8-FC5E-4296-B2CC-3F069D994D2DQ58704945-8BF9D8F9-60CD-4F56-A687-EB9923245CB9Q58705657-DA2F9231-D250-4315-BBA2-05EB8CEDB781Q58746202-6650570D-B287-494A-B2DC-AF4DF917307CQ58797269-E24B9086-1845-4406-9A9E-85F6EFDFF4C6Q59136386-8E87B922-12B1-4929-97DC-010AC7B70516
P2860
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
description
2017 nî lūn-bûn
@nan
2017年の論文
@ja
2017年学术文章
@wuu
2017年学术文章
@zh-cn
2017年学术文章
@zh-hans
2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
@yue
2017年學術文章
@zh
2017年學術文章
@zh-hant
name
PBMDA: A novel and effective p ...... isease association prediction.
@ast
PBMDA: A novel and effective p ...... isease association prediction.
@en
type
label
PBMDA: A novel and effective p ...... isease association prediction.
@ast
PBMDA: A novel and effective p ...... isease association prediction.
@en
prefLabel
PBMDA: A novel and effective p ...... isease association prediction.
@ast
PBMDA: A novel and effective p ...... isease association prediction.
@en
P2093
P2860
P50
P1476
PBMDA: A novel and effective p ...... isease association prediction.
@en
P2093
Gui-Ying Yan
Zexuan Zhu
Zheng-Wei Li
Zhenkun Wen
P2860
P304
P356
10.1371/JOURNAL.PCBI.1005455
P577
2017-03-24T00:00:00Z