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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68632
Title: miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles
Other Titles: Not Available
Authors: Upendra Kumar Pradhan
Nitesh Kumar Sharma
Prakash Kumar
Ashwani Kumar
Sagar Gupta
Ravi Shankar
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR::Indian Agricultural Statistics Research Institute
CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur (HP), India,
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
Published/ Complete Date: 2021-10-12
Project Code: Not Available
Keywords: Machine Learning
Bayesian network
XGBoost
RBP
miRNA
Publisher: PLOS
Citation: Pradhan UK, Sharma NK, Kumar P, Kumar A, Gupta S, Shankar R (2021) miRbiom: Machine-learning on Bayesian causal nets of RBPmiRNA interactions successfully predicts miRNA profiles. PLoS ONE 16(10): e0258550. https://doi. org/10.1371/journal.pone.0258550
Series/Report no.: Not Available;
Abstract/Description: Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.
Description: Not Available
ISSN: 1932-6203
Type(s) of content: Article
Sponsors: Not Available
Language: English
Name of Journal: PLoS ONE
Journal Type: peer-reviewed open access scientific journal
NAAS Rating: 8.74
Impact Factor: 3.24
Volume No.: 16(10)
Page Number: e0258550
Name of the Division/Regional Station: Statistical Genetics
Source, DOI or any other URL: https://doi.org/10.1371/journal.pone.0258550
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68632
Appears in Collections:AEdu-IASRI-Publication

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