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Identification of Homo sapiens cancer classes based on fusion of hidden gene features.

Classification of Homo sapiens cancer genes in molecular level is a challenging research issue as they are extremely pseudo random in nature. Signature gene features need to be exposed to distinctly identify the gene class. Tree-structured filter bank is chosen to perform feature extraction and dime...

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Published in:Journal of Biomedical Informatics Vol. 110
Main Authors: Das, Joyshri, Barman (Mandal), Soma
Format: research Journal Article
Published: Academic Press Inc. Oct2020
Online Access:View this record in EBSCOhost
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      dt: Oct2020
      vid: 110
      pid: 735
      pub: Academic Press Inc.
      place: Burlington, Massachusetts
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        10.1016/j.jbi.2020.103555
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        atl: Identification of Homo sapiens cancer classes based on fusion of hidden gene features.
      aug:
        au:
          Das, Joyshri
          Barman (Mandal), Soma
        affil: Institute of Radio Physics & Electronics, University of Calcutta, India
      sug:
        subj:
          Algorithms
          Neoplasms
          Image Processing, Computer Assisted
          Human
          Statistics
          Probability
          Comparative Studies
          Multicenter Studies
          Evaluation Research
          Validation Studies
      ab: Classification of Homo sapiens cancer genes in molecular level is a challenging research issue as they are extremely pseudo random in nature. Signature gene features need to be exposed to distinctly identify the gene class. Tree-structured filter bank is chosen to perform feature extraction and dimension reduction of the genes. Extracted gene features are fused using Gaussian mixture probability distribution function and identify different cancer classes depending on amount of correlation and exploiting maximum likelihood function. The algorithm is tested on 161 sample gene data of 7 different cancer classes. Sensitivity, specificity, accuracy, precision and F-score are used as metrics to judge the performance of the system and ROC is plotted in comparison with existing electrical network model based classifier. The proposed classifier can identify more than stated number of cancer classes which is a major limitation of the existing electrical network based method. The proposed algorithm is validated by comparing the results with other seven existing image processing based methods.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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