A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.
about
Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithmThe Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung CancerFinding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactionsGene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification SystemIdentification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and ProteinsThe Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-LifeSimilarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.Predicting anatomic therapeutic chemical classification codes using tiered learningA graphic method for identification of novel glioma related genes.Gene ontology and KEGG enrichment analyses of genes related to age-related macular degeneration.Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway.Prediction of multi-type membrane proteins in human by an integrated approach.Prediction of drug indications based on chemical interactions and chemical similarities.Discovery of new candidate genes related to brain development using protein interaction information.Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach.Exploring Mouse Protein Function via Multiple Approaches.Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach.Mining for novel tumor suppressor genes using a shortest path approach.Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction.A computational method for the identification of candidate drugs for non-small cell lung cancer.Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network.Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method.Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm.A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals.A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration.
P2860
Q28391239-7B84603B-FCFC-468F-8631-422358C3620FQ28395836-2B82AC68-155E-4DB7-8D3D-F9449AAF4F7DQ28543010-6CB748EF-BAFF-4AD0-B667-28E136207B18Q28547097-9926E46D-F5F1-46FB-9F25-6A5B9BE0B586Q28548040-CE552E2B-2870-4B53-9088-E5C16A865C50Q28552953-1EA345B5-3E3A-457D-ADCF-E608A9FEFF87Q30886870-D9477524-567E-4ABE-8BE9-801424B83607Q33802222-98443071-43B9-4688-9E3A-3F56BE893D23Q33891429-BF936B61-3C3A-48B7-903F-FA5DE407C717Q34073365-E4C1C4E6-0BC6-4B47-A1C9-B48729B8D65EQ34160138-43A95D9E-CD80-4499-AE37-E92D9EC0BBC8Q35133778-F46968BB-4F69-4540-ADDD-520C6ADE0E15Q35187910-47E96BAE-323F-4402-9C95-E53B3266CE85Q35551929-BA5E6799-EE30-42A6-89D3-AD4F039F926FQ36105215-E3782C26-65D8-40FA-ABE8-D879446F7918Q36173081-4501ACAA-EAF8-4181-AB5F-BA3FE54477B7Q36193025-66966BC1-1255-4D07-8923-A1AB6043312DQ37672624-1BFACAB3-DE20-4384-865A-A2FCC3C57C7CQ38460190-F2DACDE7-246E-4A5F-9705-0EA0B4A6AA9DQ38471324-0D2D7C0C-30C4-43E2-8716-7F5D3C096554Q38618335-83368C34-3009-46C0-87B7-06F787DB2BF4Q40124339-A58E30A0-E775-4F4B-810C-4D8FA4F20E67Q41709408-B5841E12-5EA8-46D6-92D7-DA0DECF2C918Q42089159-3DD5226F-062E-40CB-A9F0-940E31D23AC9Q42181500-D648139D-C0EA-45A2-8C72-4AD12D99B32AQ45943794-89AEBD25-008A-4318-9E7E-40EC10E4041BQ47356007-A0B04E66-96D6-44D7-B615-822100863A4FQ50718626-AB142ACF-F80A-4871-B85F-E3B2AE85998EQ51839738-E790FCD7-4F6C-45D0-9EBB-DE2DE4E8E653Q52593454-745AAA24-FFC5-408E-9C08-7DED29300A2C
P2860
A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
A hybrid method for prediction ...... Therapeutic Chemical classes.
@en
type
label
A hybrid method for prediction ...... Therapeutic Chemical classes.
@en
prefLabel
A hybrid method for prediction ...... Therapeutic Chemical classes.
@en
P2860
P50
P356
P1433
P1476
A hybrid method for prediction ...... l Therapeutic Chemical classes
@en
P2093
P2860
P304
P356
10.1039/C3MB70490D
P577
2014-02-04T00:00:00Z