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

Leyi Wei [et.al.] - Nama Orang;

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 non-human 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.
Index Terms—MicroRNA, multi-level negative sample selection, high-quality negative set, microRNA identification

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Informasi Detail

Judul Seri: IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
No. Panggil: -
Penerbit: New York : IEEE., 2014
Deskripsi Fisik: IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 11, NO. 1, JANUARY/FEBRUARY 2014
Bahasa: English
ISBN/ISSN: 1545-5963
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: VOL. 11, NO. 1, JANUARY/FEBRUARY 2014
Subjek: TEKNIK
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Yuli/Agus

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