Authors:
Monika Simjanoska
;
Ana Madevska Bogdanova
and
Sasho Panov
Affiliation:
Ss. Cyril and Methodius University, Macedonia, The Former Yugoslav Republic of
Keyword(s):
Colorectal Cancer, Affymetrix, Illumina, Machine Learning, Gene Ontology, Bayesian Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
Colorectal cancer is the fourth most common cause of death worldwide. Recently, many microarray experiments have been done to investigate the expression of the genes in the colorectal tissues and thus, to find the answers for its occurrence. Previously, we used experiments obtained from both Illumina and Affymetrix microarray platforms to analyze the gene expression in healthy and carcinogenic tissues. As a result we got specific sets of biomarkers that we used to build an accurate Bayesian diagnostic system. The high degree of classifier's sensitivity and specificity intrigued us to proceed with the research of the significant genes we discovered, the biomarkers. Therefore, in this paper we aim towards biomarkers identification and the functional groups they are associated with, i.e., we performed gene ontology analysis. Investigating the genes that control the colorectal carcinogenic tissue development is of central importance to the verification of the biomarkers' revealing method
's validity. Moreover, we showed the importance of their participation in the prior distributions modeling, which is the key part for achieving an accurate Bayesian classification, regardless their strict disease and disorder association.
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