Identifying cancer-associated modules from microRNA co-expression networks: a multiobjective evolutionary approach
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METHODOLOGIES AND APPLICATION
Identifying cancer-associated modules from microRNA co-expression networks: a multiobjective evolutionary approach Paramita Biswas1 · Anirban Mukhopadhyay1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract MicroRNAs (miRNAs) are a class of very small noncoding RNA molecules. Although they are not directly involved in protein translation process, they indirectly regulate production of proteins by targeting different protein-coding genes or messenger RNAs (mRNAs). Several miRNAs are known to have crucial role in progression of different diseases in the human body such as cancer, diabetes, viral infection and cardiovascular diseases. Therefore, it is very important to understand the regulatory relationship among the genes and miRNAs in order to find the potential drug targets for these life-threatening diseases. In this article, a multiobjective miRNA module detection algorithm has been proposed to identify a group of miRNAs associated with several cancer types. This module detection algorithm optimizes two objective functions simultaneously. The first objective function is based on the change in miRNA co-expression pattern across the different phenotypic conditions, and the second objective function is based on the functional similarity within the miRNA pairs. Here, non-dominated sorting genetic algorithm-II (NSGA-II) has been utilized to optimize both the objective functions simultaneously so that differentially co-expressed miRNA modules having greater functional similarity can be detected. The superiority of the proposed technique is demonstrated by comparing its performance in identifying microRNA markers with that of the other existing module detection algorithms. Furthermore, the biological significance of the mRNA targets of the identified miRNA markers has been investigated. Keywords MicroRNA · Multiobjective optimization · NSGA-II · Differentially co-expressed module · Semantic similarity · Gene ontology
1 Introduction MicroRNAs (miRNAs) are small noncoding RNAs (Bartel 2004) of length 21–25 nucleotides. More than 700 miRNAs have been found in humans. They have a distinct regulatory mechanism to control animal development and physiology (Ambros 2004). Recent studies have revealed that these tiny (22nt) RNA particles have a great impact on tumor progression by targeting messenger RNAs (mRNAs) for translational repression (Mukhopadhyay and Maulik 2013; Lu et al. 2005). Some studies have confirmed that Communicated by V. Loia.
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Anirban Mukhopadhyay [email protected] Paramita Biswas [email protected]
1
Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal 741235, India
miRNAs are also involved in various human malignancy formations (Olive et al. 2010; He et al. 2005). A few miRNAs show abrupt changes in their expression profiles in normal and malignant cells. Therefore, the identification of differential expression patterns within miRNA pairs may help us understand the regulatory relationship between the miR
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