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.
Export Options
About this article
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 |
Call for Papers in Thematic Issues
Advanced Computational Algorithms and Artificial Intelligence in Clinical Pharmacogenomics
In the era of personalized medicine, understanding the relationship between genetics and drug response is crucial. This issue delves into innovative methodologies, leveraging deep computational analysis and artificial intelligence, to enhance the field of Clinical Pharmacogenomics. The interdisciplinary approach harnesses the power of advanced high-throughput genotyping technologies, sophisticated computational analysis, ...read more
Applications of Single-cell Sequencing Technology in Reproductive Medicine
Single cell sequencing (SCS) technology utilizes individual cells' genetic material to sequence their genome, transcriptome, and epigenetics at the molecular level. It offers insights into cell heterogeneity and enables the study of limited biological materials. Since its recognition as a valuable technique in 2011, single cell sequencing has yielded numerous ...read more
Big Data in Cancer Research
Cancer is a significant threat to human life and health, remaining a highly aggressive killer. It is a leading cause of death worldwide and represents a crucial medical issue for humanity. However, in the past decade, the effectiveness of new synthetic anticancer agents has not matched the current clinical speculation. ...read more
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with ...read more
Related Journals
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Imatinib Mesylate for the Treatment of Solid Tumours: Recent Trials and Future Directions
Current Enzyme Inhibition Seek and Destroy: The Use of Natural Compounds for Targeting the Molecular Roots of Cancer
Current Drug Targets Interleukin-15 in Gene Therapy of Cancer
Current Gene Therapy Pertinence of Apoptosis Markers for the Improvement of In Vitro Fertilization (IVF)
Current Medicinal Chemistry The Role of STAT 3 in Tissue Fibrosis
Current Chemical Biology Trends in Cell-Based Electrochemical Biosensors
Current Medicinal Chemistry ABC Transporters in Multidrug Resistance and Pharmacokinetics, and Strategies for Drug Development
Current Pharmaceutical Design Bitter Gourd (Momordica charantia) is a Cornucopia of Health: A Review of its Credited Antidiabetic, Anti-HIV, and Antitumor Properties
Current Molecular Medicine Insights into Nanotherapeutic Strategies as an Impending Approach to Liver Cancer Treatment
Current Topics in Medicinal Chemistry microRNAs as Anti-Cancer Therapy
Current Pharmaceutical Design Targeting ATP7A to Increase the Sensitivity of Neuroblastoma Cells to Retinoid Therapy
Current Cancer Drug Targets Antiviral Activities of Human Host Defense Peptides
Current Medicinal Chemistry Influence of Tumor Microenvironment on the Distribution and Elimination of Nano-formulations
Current Drug Metabolism Environmental Factors Contributing to Susceptibility to Tuberculosis
Current Respiratory Medicine Reviews Formulation, Characterisation and In vitro Cytotoxic Effect of Lens culinaris Medikus Seeds Extract Loaded Chitosan Microspheres
Current Molecular Pharmacology Apoptotic Signaling Pathways as a Target for the Treatment of Liver Diseases
Mini-Reviews in Medicinal Chemistry Targeting of Leukemia-Initiating Cells to Develop Curative Drug Therapies: Straightforward but Nontrivial Concept
Current Cancer Drug Targets Using Microgravity for Defining Novel Anti-Atherosclerotic Therapy
Current Genomics Synergistic Activities of a Silver(I) Glutamic Acid Complex and Reactive Oxygen Species (ROS): A Novel Antimicrobial and Chemotherapeutic Agent
Current Medicinal Chemistry Cell Dormancy and Tumor Refractory
Anti-Cancer Agents in Medicinal Chemistry