Modeling of Cancer Classifier to Predict Site of Origin.
Cancer classification based on site of origin is very significant research issue for prediction and treatment of cancer. This paper is addressing the problem of cancer classification for Homo Sapiens genes composed of amino acid chain. Cancer gene network is realized by equivalent electrical circuits based on hydrophilic/ hydrophobic property of amino acid and a classifier is modeled to determine the cancer origin. The phase value, peak gain value and shape of Nyquist curve of network model are investigated to characterize different types of cancer gene origins. The model achieves 81.09% of classification accuracy and proves to be more sensitive and simple, since it shows 69% better performance compare to the existing nucleotide based method. The proposed classifier successfully predicts the site of origin of 93 cancer gene samples.
Tanusree
T
Roy
Soma
S
Barman
D000596:Amino Acids / Q000737:chemistry / Q000235:genetics; D003198:Computer Simulation; D052138:Genes, Neoplasm / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008962:Models, Theoretical; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D012372:ROC Curve; D017422:Sequence Analysis, DNA / Q000379:methods*
D000596:Amino Acids