Literature DB >> 25730835

Performance Analysis of Network Model to Identify Healthy and Cancerous Colon Genes.

Tanusree Roy, Soma Barman.   

Abstract

Modeling of cancerous and healthy Homo Sapiens colon gene using electrical network is proposed to study their behavior. In this paper, the individual amino acid models are designed using hydropathy index of amino acid side chain. The phase and magnitude responses of genes are examined to screen out cancer from healthy genes. The performance of proposed modeling technique is judged using various performance measurement metrics such as accuracy, sensitivity, specificity, etc. The network model performance is increased with frequency, which is analyzed using the receiver operating characteristic curve. The accuracy of the model is tested on colon genes and achieved maximum 97% at 10-MHz frequency.

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Year:  2015        PMID: 25730835     DOI: 10.1109/JBHI.2015.2408366

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  3 in total

1.  A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences.

Authors:  Amin Khodaei; Mohammad-Reza Feizi-Derakhshi; Behzad Mozaffari-Tazehkand
Journal:  Bioimpacts       Date:  2020-03-24

2.  Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.

Authors:  Amin Khodaei; Parvaneh Shams; Hadi Sharifi; Behzad Mozaffari-Tazehkand
Journal:  Biomed Signal Process Control       Date:  2022-09-23       Impact factor: 5.076

3.  Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network.

Authors:  Hawraa Haj-Hassan; Ahmad Chaddad; Youssef Harkouss; Christian Desrosiers; Matthew Toews; Camel Tanougast
Journal:  J Pathol Inform       Date:  2017-02-28
  3 in total

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