Abstract
A group of functionally related genes that take part in similar biological activities constitutes a functional module. Genes collaborating in a common module might induce similar pathological disease and share common genetic origins for the associated disease phenotypes. Computationally isolating such functional modules is useful in unveiling biological and cellular processes or molecular basis of associated diseases. As a result detecting such functional modules is an important and burning issue in the computational biology research.
Various techniques have been proposed for the last few decades to find functional modules or target factor modules in gene regulation networks. Biological modules are overlapping in nature where the same gene may take part in multiple network modules. In addition, data used for inference or detection of modules in silico are noisy in nature. Traditional hard computing methods appear to be ineffective in handling uncertainty, impreciseness, or fuzzy nature in the solutions. The soft computing paradigm is effective in handling such issues. In this work, we discuss a few soft computing methods for detecting regulatory modules and validate the effectiveness of the candidate methods in the light of publicly available expression data with respect to various statistical and topological validation measures.
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Acknowledgment
SR thanks the Department of Biotechnology, Government of India, for giving him the Overseas Research Associateship (vide sanction BT/NE/20/2011) and the University of Colorado, Colorado Springs, for the support to conduct part of the research at LINC Lab in the Department of Computer Science.
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Roy, S., Manners, H.N., Jha, M., Guzzi, P.H., Kalita, J.K. (2018). Soft Computing Approaches to Extract Biologically Significant Gene Network Modules. In: Purohit, H., Kalia, V., More, R. (eds) Soft Computing for Biological Systems. Springer, Singapore. https://doi.org/10.1007/978-981-10-7455-4_3
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