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.
Graphical Abstract
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
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
Deep learning in Single Cell Analysis
The field of biology is undergoing a revolution in our ability to study individual cells at the molecular level, and to integrate data from multiple sources and modalities. This has been made possible by advances in technologies for single-cell sequencing, multi-omics profiling, spatial transcriptomics, and high-throughput imaging, as well as ...read more
New insights on Pediatric Tumors and Associated Cancer Predisposition Syndromes
Because of the broad spectrum of children cancer susceptibility, the diagnosis of cancer risk syndromes in children is rarely used in direct cancer treatment. The field of pediatric cancer genetics and genomics will only continue to expand as a result of increasing use of genetic testing tools. It's possible that ...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
-
Coordinated Expression of Pax-5 and FAK1 in Metastasis
Anti-Cancer Agents in Medicinal Chemistry Lysine Acetyltransferases CBP and p300 as Therapeutic Targets in Cognitive and Neurodegenerative Disorders
Current Pharmaceutical Design Studies on Structures and Functions of Kinases leading to Prostate Cancer and Their Inhibitors
Current Enzyme Inhibition Interaction and Reaction of the Antioxidant Mn<sup>III</sup> [Meso-Tetrakis(4-NMethyI Pyridinium) Porphyrin] with the Apoptosis Reporter Lipid Phosphatidylserine
Current Physical Chemistry Sirtuins Family- Recent Development as a Drug Target for Aging, Metabolism, and Age Related Diseases
Current Drug Targets Knockdown of Insulin-Like Growth Factor I Receptor Inhibits the Growth and Enhances Chemo-Sensitivity of Liver Cancer Cells
Current Cancer Drug Targets Targeting of NF-kappaB Signaling Pathway, other Signaling Pathways and Epigenetics in Therapy of Multiple Myeloma
Cardiovascular & Hematological Disorders-Drug Targets Recombinant Antibodies in Cancer Therapy
Current Protein & Peptide Science Nonviral Vectors for Cancer Gene Therapy: Prospects for Integrating Vectors and Combination Therapies
Current Gene Therapy An Emerging Strategy for Cancer Treatment Targeting Aberrant Glycogen Synthase Kinase 3β
Anti-Cancer Agents in Medicinal Chemistry Revisiting Non-Cancer Drugs for Cancer Therapy
Current Topics in Medicinal Chemistry Polymers Based on Phenyl Boric Acid in Tumor-Targeted Therapy
Anti-Cancer Agents in Medicinal Chemistry Kavalactone Pharmacophores for Major Cellular Drug Targets
Mini-Reviews in Medicinal Chemistry Follicular Immunology Environment and the Influence on In Vitro Fertilization Outcome
Current Women`s Health Reviews Cancer Drug Discovery Targeting Histone Methyltransferases: An Update
Current Medicinal Chemistry Structure-Function Relationships and Clinical Applications of L-Asparaginases
Current Medicinal Chemistry Biotechnological Approaches for the Treatment of Inflammatory Diseases
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry Current Development of ROS-Modulating Agents as Novel Antitumor Therapy
Current Cancer Drug Targets Inhibiting the Secretion of Hepatitis B Surface Antigen (HBsAg) to Treat Hepatitis B Infection- a Review
Infectious Disorders - Drug Targets Main Nutritional and Environmental Risk Factors in Children with Leukemia from a Public Hospital of the State of Guanajuato, Mexico
Current Cancer Therapy Reviews