Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC

Ju, Zhe; Cao, Junzhe; Gu, Hong

doi:10.1016/j.jtbi.2016.02.020

Cited by 93 publications

(40 citation statements)

References 42 publications

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“…Of these three, however, the jackknife is the least arbitrary that can always yield a unique outcome for a given benchmark dataset, as elaborated by Chou 31 and widely recognized and increasingly adopted by researchers to analyze the quality of various predictors (see, e.g., Kabir and Hayat, 64 Kumar et al., 73 Chen et al., 78 Ali and Hayat, 130 Khan et al., 131 Mondal and Pai, 132 Dehzangi et al., 133 Ahmad et al., 134 Ju et al., 135 and Behbahani et al 136 …”

Section: Methodsmentioning

confidence: 96%

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu

Yang

Chou

2017

Molecular Therapy - Nucleic Acids

231

107

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Involved with important cellular or gene functions and implicated with many kinds of cancers, piRNAs, or piwi-interacting RNAs, are of small non-coding RNA with around 19–33 nt in length. Given a small non-coding RNA molecule, can we predict whether it is of piRNA according to its sequence information alone? Furthermore, there are two types of piRNA: one has the function of instructing target mRNA deadenylation, and the other does not. Can we discriminate one from the other? With the avalanche of RNA sequences emerging in the postgenomic age, it is urgent to address the two problems for both basic research and drug development. Unfortunately, to the best of our knowledge, so far no computational methods whatsoever could be used to deal with the second problem, let alone deal with the two problems together. Here, by incorporating the physicochemical properties of nucleotides into the pseudo K-tuple nucleotide composition (PseKNC), we proposed a powerful predictor called 2L-piRNA. It is a two-layer ensemble classifier, in which the first layer is for identifying whether a query RNA molecule is piRNA or non-piRNA, and the second layer for identifying whether a piRNA is with or without the function of instructing target mRNA deadenylation. Rigorous cross-validations have indicated that the success rates achieved by the proposed predictor are quite high. For the convenience of most biologists and drug development scientists, the web server for 2L-piRNA has been established at http://bioinformatics.hitsz.edu.cn/2L-piRNA/, by which users can easily get their desired results without the need to go through the mathematical details.

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Section: Methodsmentioning

confidence: 96%

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu

Yang

Chou

2017

Molecular Therapy - Nucleic Acids

231

107

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“…However, as elucidated in Chou 55 in the above three choices, the jackknife test has been demonstrated to be the least arbitrary that can always yield a unique outcome for a given benchmark dataset. Therefore, the jackknife test has been widely recognized and increasingly adopted by researchers to analyze the quality of various predictors 83, 103, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131. In view of this, we also used the jackknife test to examine the quality of the current prediction method.…”

Section: Methodsmentioning

confidence: 99%

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Feng

Ding

Yang

et al. 2017

Molecular Therapy - Nucleic Acids

265

112

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There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called “iRNA-PseColl” has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform’s web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.

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“…Of these three, however, the jackknife test is deemed the least arbitrary that can always yield a unique outcome for a given benchmark dataset as elucidated in [28]. Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (see, e.g., [65–68, 70, 108–119]).…”

Section: Methodsmentioning

confidence: 99%

iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

et al. 2016

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The initiation of replication is an extremely important process in DNA life cycle. Given an uncharacterized DNA sequence, can we identify where its origin of replication (ORI) is located? It is no doubt a fundamental problem in genome analysis. Particularly, with the rapid development of genome sequencing technology that results in a huge amount of sequence data, it is highly desired to develop computational methods for rapidly and effectively identifying the ORIs in these genomes. Unfortunately, by means of the existing computational methods, such as sequence alignment or kmer strategies, it could hardly achieve decent success rates. To address this problem, we developed a predictor called “iOri-Human”. Rigorous jackknife tests have shown that its overall accuracy and stability in identifying human ORIs are over 75% and 50%, respectively. In the predictor, it is through the pseudo nucleotide composition (an extension of pseudo amino acid composition) that 96 physicochemical properties for the 16 possible constituent dinucleotides have been incorporated to reflect the global sequence patterns in DNA as well as its local sequence patterns. Moreover, a user-friendly web-server for iOri-Human has been established at http://lin.uestc.edu.cn/server/iOri-Human.html, by which users can easily get their desired results without the need to through the complicated mathematics involved.

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Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC

Cited by 93 publications

References 42 publications

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

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