Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features

Scientific World Journal

Volumn 2015, Issue , 2015, Pages

  Adetiba, Emmanuel ^a   Olugbara, Oludayo O ^a  

^a DURBAN UNIVERSITY OF TECHNOLOGY   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; EPIDERMAL GROWTH FACTOR RECEPTOR; NUCLEOTIDE; PROTEIN P53; EGFR PROTEIN, HUMAN; KRAS PROTEIN, HUMAN; PROTEIN P21; TP53 PROTEIN, HUMAN; TUMOR MARKER;

ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER SCREENING; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; GENE MAPPING; GENE MUTATION; GENE SEQUENCE; GENOME ANALYSIS; HISTOGRAM; HISTOGRAM OF ORIENTED GRADIENT; IMAGE PROCESSING; INTERMETHOD COMPARISON; LOCAL BINARY PATTERN; LUNG CANCER; MACHINE LEARNING; MATHEMATICAL PARAMETERS; NEXT GENERATION SEQUENCING; NON SMALL CELL LUNG CANCER; NONHUMAN; NUCLEOTIDE SEQUENCE; PREDICTION; RAT; SARCOMA; SUPPORT VECTOR MACHINE; VIRUS ONCOGENE; CARCINOMA, NON-SMALL-CELL LUNG; COMPARATIVE STUDY; EVALUATION STUDY; GENETICS; HUMAN; LUNG NEOPLASMS; MUTATION;

RATTUS;

BIOMARKERS, TUMOR; CARCINOMA, NON-SMALL-CELL LUNG; HUMANS; LUNG NEOPLASMS; MUTATION; NEURAL NETWORKS (COMPUTER); PROTO-ONCOGENE PROTEINS P21(RAS); RECEPTOR, EPIDERMAL GROWTH FACTOR; SUPPORT VECTOR MACHINE; TUMOR SUPPRESSOR PROTEIN P53;

EID: 84924402069     PISSN: 23566140     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2015/786013     Document Type: Article

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