Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.
about
Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysisAucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics dataIdentifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs.Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics dataProteomics identification of desmin as a potential oncofetal diagnostic and prognostic biomarker in colorectal cancer.Inference of cancer-specific gene regulatory networks using soft computing rules.Regulatory cross-talk of mouse liver polyamine and methionine metabolic pathways: a systemic approach to its physiopathological consequences.Identifying the gene signatures from gene-pathway bipartite network guarantees the robust model performance on predicting the cancer prognosis.Statistical inference and reverse engineering of gene regulatory networks from observational expression data.FOX-2 dependent splicing of ataxin-2 transcript is affected by ataxin-1 overexpressionIdentification of Enolase 1 and Thrombospondin-1 as serum biomarkers in HBV hepatic fibrosis by proteomicsGene network inference and visualization tools for biologists: application to new human transcriptome datasets.FBXW7 negatively regulates ENO1 expression and function in colorectal cancer.Increased plasma levels of the APC-interacting protein MAPRE1, LRG1, and IGFBP2 preceding a diagnosis of colorectal cancer in women.Gene expression deregulation by KRAS G12D and G12V in a BRAF V600E context.Microarray-based cancer prediction using soft computing approachMining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network.Identifying breast cancer subtype related miRNAs from two constructed miRNAs interaction networks in silico method.The role of chemokines in intestinal inflammation and cancer.Molecular networks for the study of TCM pharmacology.α-Enolase, a multifunctional protein: its role on pathophysiological situationsPPDB: A Tool for Investigation of Plants Physiology Based on Gene Ontology.Revealing the Strong Functional Association of adipor2 and cdh13 with adipoq: A Gene Network Study.PPDB - A tool for investigation of plants physiology based on gene ontology.Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networksα-enolase promotes tumorigenesis and metastasis via regulating AMPK/mTOR pathway in colorectal cancer.An approach to infer putative disease-specific mechanisms using neighboring gene networks.Genes with relevance for early to late progression of colon carcinoma based on combined genomic and transcriptomic information from the same patients.A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer.Rough set soft computing cancer classification and network: one stone, two birds.Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach.In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically.Next Generation Immunotherapy for Pancreatic Cancer: DNA Vaccination is Seeking New Combo Partners.
P2860
Q26801048-F4B4A51A-1936-4F45-93E6-137B7A9646C2Q30882805-1EC6F89D-9315-445E-9F55-89A7261C7497Q31031810-F78CEB13-D127-4931-991A-3A704871943BQ31070165-8899AA9A-EF1F-4B20-A3AB-D1DA9294BCF6Q33450135-3994056E-100C-41A5-BCDB-A1F3E4D89AF8Q33834457-45070BBD-5550-47F1-A63F-56FF48354C05Q33983597-6780F380-5BEF-416F-9F5A-0915F4FBB5D1Q34003648-059E90BA-A1F8-4CB7-B389-B65D61A4749BQ34191343-8788D23E-9DAF-4324-B8E7-66C02996C3FAQ34292752-566DD705-F98E-4A74-AE3D-B3FE27ECAC23Q34812703-129496FA-61A1-4925-AF68-193032AD7C08Q35860667-7C9AF14D-5E96-41A5-B6AD-B598A9E375B8Q36004642-811296E8-3E15-4027-BC3B-6B29833DAE48Q36164349-79F011F6-F8B4-476B-BD42-82211680D1ADQ37040359-374BAD73-BE42-41FB-A19A-91225F277811Q37313817-1FF4A316-920D-404A-A656-0C28A8CEA324Q37319389-58E43AA0-3D72-44E7-9DE1-B799A6942F51Q37369098-CC9A36D7-3342-495C-A714-7E207A234891Q37455621-AD5C5B7D-7EB5-4825-BEC8-9C0A9BE05B05Q37663707-82B39230-5331-40D1-A601-EAB5ACCE0C56Q38057216-56EF108E-7631-4A94-B87A-C699D8D67893Q38458738-BA8C9170-B420-4B37-9AC6-8BD8A75673A4Q38470599-B31138D8-A8D3-44DF-B4EF-15D60830D58FQ38473031-8A49BB9C-3A8A-45B6-B182-00EE9AC0768FQ38619881-075AEA33-A42F-4E25-94C4-2F60A1E60349Q38725305-E717ABAF-8007-4BE2-81B7-1E32848C267EQ38960008-008593C2-45CD-4854-B4C5-EB5161434CA9Q40475980-66687AF3-DBC7-4823-92C4-4969923DDAEEQ41949320-1F14D6E0-E8CB-4875-BD6F-88505AA9E73BQ42557829-0CECB032-19F5-4DE0-9BC7-73C3CBC1F49CQ47427464-3E395AF3-4762-4712-875E-3CF2AA3E8FE9Q47698219-95D45E05-ACF6-4E6A-997E-504161946B1FQ49805542-99E9013C-CF87-422A-BA41-C29C5B76C9C3
P2860
Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.
description
2008 nî lūn-bûn
@nan
2008 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Constructing disease-specific ...... ase 1 as the central elements.
@ast
Constructing disease-specific ...... ase 1 as the central elements.
@en
type
label
Constructing disease-specific ...... ase 1 as the central elements.
@ast
Constructing disease-specific ...... ase 1 as the central elements.
@en
prefLabel
Constructing disease-specific ...... ase 1 as the central elements.
@ast
Constructing disease-specific ...... ase 1 as the central elements.
@en
P2093
P2860
P356
P1433
P1476
Constructing disease-specific ...... ase 1 as the central elements.
@en
P2093
Baofeng Yang
Chuanxing Li
Lihong Wang
Yadong Wang
P2860
P2888
P356
10.1186/1752-0509-2-72
P577
2008-08-10T00:00:00Z
P5875
P6179
1022823650