Predicting miRNA's target from primary structure by the nearest neighbor algorithm.
We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets. The NNA was then applied, learning from these data to produce a predictor. We implemented minimum redundancy maximum relevance (mRMR) and properties forward selection (PFS) to reduce the redundancy of our encoding system, obtaining 91 most efficient properties. Finally, via the Jackknife cross-validation test, we got a positive accuracy of 69.2% and an overall accuracy of 96.0% with all the 253 properties. Besides, we got a positive accuracy of 83.8% and an overall accuracy of 97.2% with the 91 most efficient properties. A web-server for predictions is also made available at http://app3.biosino.org:8080/miRTP/index.jsp.
Lin
Kao
K
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
Qian
Ziliang
Z
Lu
Lin
L
Lu
Lingyi
L
Lai
Lihui
L
Gu
Jieyi
J
Zeng
Zhenbing
Z
Li
Haipeng
H
Cai
Yudong
Y
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001483:Base Sequence / Q000502:physiology*; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D005544:Forecasting; D035683:MicroRNAs / Q000378:metabolism* / Q000502:physiology; D058977:Molecular Sequence Annotation / Q000379:methods; D008969:Molecular Sequence Data; D034622:RNA Interference / Q000502:physiology; D017385:Sequence Homology
D035683:MicroRNAs