Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.
TLDR
Two methods to predict microRNA-disease association by integrating the social network analysis method with machine learning and based on networks derived from known microRNAs, diseases, and micro RNA-microRNA associations are introduced.Abstract:
MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.read more
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References
More filters
Journal ArticleDOI
MicroRNA signatures in human cancers
George A. Calin,Carlo M. Croce +1 more
TL;DR: MiRNA-expression profiling of human tumours has identified signatures associated with diagnosis, staging, progression, prognosis and response to treatment and has been exploited to identify miRNA genes that might represent downstream targets of activated oncogenic pathways, or that target protein-coding genes involved in cancer.
Journal Article
MicroRNA signatures in human cancers
George A. Calin,Carlo M. Croce +1 more
TL;DR: The causes of the widespread differential expression of miRNA genes in malignant compared with normal cells can be explained by the location of these genes in cancer-associated genomic regions, by epigenetic mechanisms and by alterations in the miRNA processing machinery as discussed by the authors.
Journal Article
Oncomirs : microRNAs with a role in cancer
TL;DR: I MicroRNAs (miRNAs) are an abundant class of small non-protein-coding RNAs that function as negative gene regulators as discussed by the authors, and have been shown to repress the expression of important cancer-related genes and might prove useful in the diagnosis and treatment of cancer.
Journal ArticleDOI
Oncomirs — microRNAs with a role in cancer
TL;DR: Evidence has shown that miRNA mutations or mis-expression correlate with various human cancers and indicates that miRNAs can function as tumour suppressors and oncogenes.
Journal ArticleDOI
MicroRNA gene expression deregulation in human breast cancer.
Marilena V. Iorio,Manuela Ferracin,Chang Gong Liu,Angelo Veronese,Riccardo Spizzo,Silvia Sabbioni,Eros Magri,Massimo Pedriali,Muller Fabbri,Manuela Campiglio,Sylvie Ménard,Juan P. Palazzo,Anne L. Rosenberg,Piero Musiani,Stefano Volinia,Italo Nenci,George A. Calin,Patrizia Querzoli,Massimo Negrini,Carlo M. Croce +19 more
TL;DR: It is shown that, compared with normal breast tissue, miRNAs are also aberrantly expressed in human breast cancer, and the overall miRNA expression could clearly separate normal versus cancer tissues, with the most significantly deregulated mi RNAs being mir-125b, mir-145, mir -21, and mir-155.
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