Abstract
Background: Artificial Neural Networks (ANNs) can be used to classify tumor of Hepatocellular carcinoma based on their gene expression signatures. The neural network is trained with gene expression profiles of genes that were predictive of recurrence in liver cancer, the ANNs became capable of correctly classifying all samples and distinguishing the genes most suitable for the organization. The ability of the trained ANN models in recognizing the Cancer Genes was tested as we analyzed additional samples that were not used beforehand for the training procedure, and got the correctly classified result in the validation set. Bootstrapping of training and analysis of dataset was made as external justification for more substantial result.
Result: The best result achieved when the number of hidden layers was 10. The R2 value with training is 0.99136, R2 value obtained with testing is 0.80515, R2 value obtained after validation is 0.76678 and finally, with the total number of sets the R2 value is 0.93417. Performance was reported on the basis of graph plotted between Mean Squared Error (MSE) and 23 epoch. The value of gradient of the curve was 152 after 6 validation checks and 23 iterations.
Conclusion: A successful attempt at developing a method for diagnostic classification of tumors from their gene-expression autographs that efficiently classify tumors and helps in decision making for providing appropriate treatment to the patients suffering from Hepatocellular carcinoma has been carried out.
Keywords: Gene database, Artificial neural network, Gene signatures, Classification, Hepatocellular carcinoma, Liver cancer.
Current Genomics
Title:Artificial Neural Network as a Classifier for the Identification of Hepatocellular Carcinoma Through Prognosticgene Signatures
Volume: 19 Issue: 6
Author(s): Satya Eswari Jujjavarapu*Saurabh Deshmukh
Affiliation:
- Department of Biotechnology, National Institute of Technology Raipur, Raipur - 492010,India
Keywords: Gene database, Artificial neural network, Gene signatures, Classification, Hepatocellular carcinoma, Liver cancer.
Abstract: Background: Artificial Neural Networks (ANNs) can be used to classify tumor of Hepatocellular carcinoma based on their gene expression signatures. The neural network is trained with gene expression profiles of genes that were predictive of recurrence in liver cancer, the ANNs became capable of correctly classifying all samples and distinguishing the genes most suitable for the organization. The ability of the trained ANN models in recognizing the Cancer Genes was tested as we analyzed additional samples that were not used beforehand for the training procedure, and got the correctly classified result in the validation set. Bootstrapping of training and analysis of dataset was made as external justification for more substantial result.
Result: The best result achieved when the number of hidden layers was 10. The R2 value with training is 0.99136, R2 value obtained with testing is 0.80515, R2 value obtained after validation is 0.76678 and finally, with the total number of sets the R2 value is 0.93417. Performance was reported on the basis of graph plotted between Mean Squared Error (MSE) and 23 epoch. The value of gradient of the curve was 152 after 6 validation checks and 23 iterations.
Conclusion: A successful attempt at developing a method for diagnostic classification of tumors from their gene-expression autographs that efficiently classify tumors and helps in decision making for providing appropriate treatment to the patients suffering from Hepatocellular carcinoma has been carried out.
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Cite this article as:
Jujjavarapu Eswari Satya *, Deshmukh Saurabh , Artificial Neural Network as a Classifier for the Identification of Hepatocellular Carcinoma Through Prognosticgene Signatures, Current Genomics 2018; 19 (6) . https://dx.doi.org/10.2174/1389202919666180215155234
DOI https://dx.doi.org/10.2174/1389202919666180215155234 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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