Using epigenomics data to predict gene expression in lung cancer

BMC Bioinformatics

Volumn 16, Issue 5, 2015, Pages

  Li, Jeffery ^a   Ching, Travers ^b,c   Huang, Sijia ^b,c   Garmire, Lana X ^b,c  

^a Johns Hopkins University   (United States)

^b UNIVERSITY OF HAWAII   (United States)

^c UNIVERSITY OF HAWAII CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALKYLATION; ARTIFICIAL INTELLIGENCE; BIOLOGICAL ORGANS; DECISION TREES; DISEASES; FEATURE EXTRACTION; FORECASTING; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENES; LEARNING SYSTEMS; METHYLATION;

10-FOLD CROSS-VALIDATION; ACCURATE PREDICTION; EPIGENETIC ALTERATIONS; FEATURE SELECTION AND CLASSIFICATION; HISTONE MODIFICATION; NUCLEOTIDE COMPOSITION; PROTEIN-CODING TRANSCRIPTS; RANDOM FOREST CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

HISTONE;

ARTIFICIAL INTELLIGENCE; CPG ISLAND; DNA METHYLATION; EPIGENETICS; GENE EXPRESSION REGULATION; GENETICS; GENOMICS; HIGH THROUGHPUT SEQUENCING; HUMAN; HUMAN GENOME; LUNG TUMOR; PROCEDURES;

ARTIFICIAL INTELLIGENCE; CPG ISLANDS; DNA METHYLATION; EPIGENOMICS; GENE EXPRESSION REGULATION, NEOPLASTIC; GENOME, HUMAN; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HISTONES; HUMANS; LUNG NEOPLASMS;

EID: 84961509372     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-16-S5-S10     Document Type: Article

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