R Visakh1, K A Abdul Nazeer2. 1. Department of Computer Science and Engineering, NIT Calicut, Kerala, India. Electronic address: visakh_p140056cs@nitc.ac.in. 2. Department of Computer Science and Engineering, NIT Calicut, Kerala, India. Electronic address: nazeer@nitc.ac.in.
Abstract
BACKGROUND: Identification of pathways that show significant difference in activity between disease and control samples have been an interesting topic of research for over a decade. Pathways so identified serve as potential indicators of aberrations in phenotype or a disease condition. Recently, epigenetic mechanisms such as DNA methylation are known to play an important role in altering the regulatory mechanism of biological pathways. It is reasonable to think that a set of genes that show significant difference in expression and methylation interact together to form a network of pathways. Existing pathway identification methods fail to capture the complex interplay between interacting pathways. RESULTS: This paper proposes a novel framework to identify biological pathways that are dysregulated by epigenetic mechanisms. Experiments on four benchmark cancer datasets and comparison with state-of-the-art pathway identification methods reveal the effectiveness of the proposed approach. CONCLUSION: The proposed framework incorporates both topology and biological relationships of pathways. Comparison with state-of-the-art techniques reveals promising results. Epigenetic signatures identified from pathway interaction networks can help to advance Molecular Pathological Epidemiology (MPE) research efforts by predicting tumor molecular changes.
BACKGROUND: Identification of pathways that show significant difference in activity between disease and control samples have been an interesting topic of research for over a decade. Pathways so identified serve as potential indicators of aberrations in phenotype or a disease condition. Recently, epigenetic mechanisms such as DNA methylation are known to play an important role in altering the regulatory mechanism of biological pathways. It is reasonable to think that a set of genes that show significant difference in expression and methylation interact together to form a network of pathways. Existing pathway identification methods fail to capture the complex interplay between interacting pathways. RESULTS: This paper proposes a novel framework to identify biological pathways that are dysregulated by epigenetic mechanisms. Experiments on four benchmark cancer datasets and comparison with state-of-the-art pathway identification methods reveal the effectiveness of the proposed approach. CONCLUSION: The proposed framework incorporates both topology and biological relationships of pathways. Comparison with state-of-the-art techniques reveals promising results. Epigenetic signatures identified from pathway interaction networks can help to advance Molecular Pathological Epidemiology (MPE) research efforts by predicting tumor molecular changes.
Authors: Kathleen M Chen; Jie Tan; Gregory P Way; Georgia Doing; Deborah A Hogan; Casey S Greene Journal: BioData Min Date: 2018-07-03 Impact factor: 2.522