iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Chen, Wei; Feng, Pengmian; Lin, Hao; Chou, Kuo‐Chen

doi:10.1093/nar/gks1450

Cited by 567 publications

(394 citation statements)

References 57 publications

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“…In particular, the SE was defined by the proportion of real positive cases that were correctly predicted as positive [104,105] (shown in Equation 1):…”

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

“…The MCC was an important metric reflecting the stability of a protein function predictor, which described the correlation between a predictive value and an actual value [104,105]. It has been considered as one of the most comprehensive parameters in any category of predictors due to its full consideration of all four results (TP, TN, FP and FN).…”

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

“…It has been considered as one of the most comprehensive parameters in any category of predictors due to its full consideration of all four results (TP, TN, FP and FN). In particular, the MCC could be calculated by where TP, TN, FP and FN indicated the number of true positives, true negatives, false positives and false negatives, respectively [104,105]. It is very important to emphasize that this set of metrics is valid only for the single-label systems (in which each protein only belongs to one functional class).…”

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

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Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

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The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction.Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research. KEYWORDSfalse discovery rate; machine learning; protein function prediction; support vector machine; BLAST Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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“…In particular, the SE was defined by the proportion of real positive cases that were correctly predicted as positive [104,105] (shown in Equation 1):…”

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

Section: Assessing the Identification Accuracies Of The Studied Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

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show abstract

“…However, in 1997 Nielsen et al utilised an artificial intelligence (AI) based approach for the widely used SignalP algorithm [32]. Bendtsen [34][35][36] and summarised in a review [37], to develop a useful prediction method for a biological system, we need to consider the following procedures: (i) construct or select a valid benchmark dataset to train and test the model or predictor; (ii) introduce a powerful algorithm to operate the prediction; (iv) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; and (v) subsequently establish a user-friendly web-server for the model or prediction method that is accessible to the public [38].…”

Section: Introductionmentioning

confidence: 99%

Signal peptide discrimination and cleavage site identification using SVM and NN

Kazemian

Yusuf

White

2014

Computers in Biology and Medicine

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tAbout 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short aminoacid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification.The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model.

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“…Recently, the concept of PseAAC was further extended to represent the feature vectors of DNA and nucleotides [21,22], as well as other biological samples (see, for example [23][24][25]). Because it has been widely and *Address correspondence to this author at the Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece; Tel: +302421074163; Fax: +302421074169; E-mail: thkarak@uth.gr increasingly used, recently two powerful softwares, specifically 'PseAAC-Builder' [26] and 'propy' [27], were introduced for generating various special Chou's pseudo amino acid compositions, in addition to the web server 'PseAAC' [28] built in 2008.…”

Section: Introductionmentioning

confidence: 99%

A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

Georgiou¹,

Karakasidis²,

Megaritis³

2013

TOBIOIJ

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Abstract:The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In this survey, we present results concerning genetic sequences and Chou's pseudo amino acid composition as well as methodologies developed based on this concept along with elements of fuzzy set theory, and emphasize on fuzzy clustering and its application in analysis of genetic sequences.

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iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Cited by 567 publications

References 57 publications

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Signal peptide discrimination and cleavage site identification using SVM and NN

A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

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