NDLI: Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set

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Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set

Incorporation of biological pathway knowledge in the construction of priors for optimal Bayesian classification

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Multiple sequence alignment with hidden Markov models learned by random drift particle swarm optimization

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Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set

Content Provider	ACM Digital Library
Author	Ji, Rongrong Liao, Minghong Gao, Yue He, Zengyou Wei, Leyi Zou, Quan
Copyright Year	2014
Abstract	MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing data sets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative. To generate a representative negative set, we proposed a novel negative sample selection technique, and successfully collected negative samples with improved quality. Two recent classifiers rebuilt with the proposed negative set achieved an improvement of ∼6 percent in their predictive performance, which confirmed this assumption. Based on the proposed negative set, we constructed a training set, and developed an online system called miRNApre specifically for human pre-miRNA identification. We showed that miRNApre achieved accuracies on updated human and nonhuman data sets that were 34.3 and 7.6 percent higher than those achieved by current methods. The results suggest that miRNApre is an effective tool for pre-miRNA identification. Additionally, by integrating miRNApre, we developed a miRNA mining tool, mirnaDetect, which can be applied to find potential miRNAs in genome-scale data. MirnaDetect achieved a comparable mining performance on human chromosome 19 data as other existing methods.
Starting Page	192
Ending Page	201
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2013.146
Volume Number	11
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	High-quality negative set MicroRNA MicroRNA identification Multi-level negative sample selection
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set

Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set

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