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Book ChapterDOI

Machine Learning Approach for Identification of miRNA-mRNA Regulatory Modules in Ovarian Cancer

05 Dec 2017-pp 438-447
TL;DR: An existing robust mutual information based Maximum-Relevance Maximum-Significance algorithm has been used and is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer.
Abstract: Ovarian cancer is a fatal gynecologic cancer. Altered expression of biomarkers leads to this deadly cancer. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies the pathways associated with ovarian cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered ovarian cancer pathways. In this regard, an existing robust mutual information based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in ovarian cancer. A set of miRNA-mRNA modules are identified first than their association with ovarian cancer are studied exhaustively. The effectiveness of the proposed approach is compared with existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer.
Citations
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Proceedings ArticleDOI
01 Dec 2018
TL;DR: The integrated approach is designed by incorporating protein-protein interaction network data and gene expression data to select a set of genes that are highly related to diabetes also they are functionally related among themselves and the effectiveness of the approach is demonstrated over other existing methods.
Abstract: Increase in number of people diagnosed with diabetes makes this disease a new health threat in the 21st century. Understanding the etiology of and finding a way to prevent diabetes, especially type 2 diabetes mellitus, is an urgent challenge for the health care community and our society. Pancreatic islet cells are responsible for maintaining normal blood glucose level and if there is any disturbance that leads to the onset of diabetes. Human pancreatic islet cells contain $\alpha$,$\beta$,$\delta$, and PP cells. Understanding the contribution of each type of cell through gene expression in type 2 diabetes mellitus is very important for the development of diagnostic tools. Therefore, gene expression data of $\alpha$,$\beta$,$\delta$ and PP cells can be used. Single cell RNA sequencing technology has been found useful to generate expression data for individual cells. The gene expression data is usually used to find genes that are related to clinical outcome. However, in a biological process a set of genes are involved that share functional similarity. Analysing only single type of data may not generate significant type 2 diabetes mellitus genes. In this regard, an integrated approach has been used to analyse single-cell RNA sequencing data of human pancreatic islet cells. The integrated approach is designed by incorporating protein-protein interaction network data and gene expression data to select a set of genes that are highly related to diabetes also they are functionally related among themselves. The effectiveness of the approach is demonstrated over other existing methods.
References
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Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations


"Machine Learning Approach for Ident..." refers methods in this paper

  • ...For the biological interpretation of highly significant modules P -value = 0, the Cytoscape [16] plug-in ClueGO [2] has been used to perform pathway enrichment analysis....

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  • ...For the biological interpretation of highly significant modules P -value = 0, the Cytoscape [16] plug-in ClueGO [2] has been used to perform pathway enrichment...

    [...]

Book
01 Jan 1973
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

13,647 citations

Journal ArticleDOI
TL;DR: H hierarchical and self-consistent orthology annotations are introduced for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution in the STRING database.
Abstract: The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

8,224 citations


"Machine Learning Approach for Ident..." refers methods in this paper

  • ...Next, STRING database [17] is used to generate connections between the genes of each module to check whether the genes of obtained modules are involved in same biological function or not....

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  • ...Finally, the obtained modules are evaluated using STRING database [17], pathway enrichment analysis, and disease ontology....

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Journal ArticleDOI
TL;DR: Two founding members of the microRNA family were originally identified in Caenorhabditis elegans as genes that were required for the timed regulation of developmental events and indicate the existence of multiple RISCs that carry out related but specific biological functions.
Abstract: MicroRNAs are a family of small, non-coding RNAs that regulate gene expression in a sequence-specific manner. The two founding members of the microRNA family were originally identified in Caenorhabditis elegans as genes that were required for the timed regulation of developmental events. Since then, hundreds of microRNAs have been identified in almost all metazoan genomes, including worms, flies, plants and mammals. MicroRNAs have diverse expression patterns and might regulate various developmental and physiological processes. Their discovery adds a new dimension to our understanding of complex gene regulatory networks.

6,282 citations


"Machine Learning Approach for Ident..." refers background in this paper

  • ...Extensive studies have been conducted to understand their role in different biological processes and diseases [1,6,10]....

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