Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression

Su, Qiang; Lu, Wei; Du, Dongshu; Chen, Fuxue; Niu, Bing; Chou, Kuo‐Chen

doi:10.18632/oncotarget.17210

Cited by 56 publications

(15 citation statements)

References 69 publications

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“…To construct a valid statistical model for a biology problem based on protein sequences [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], a rule of five steps is needed [ 98 ]. Firstly, a valid construction of datasets for both training and testing the model is required.…”

Section: Methodsmentioning

confidence: 99%

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

Yang

et al. 2018

IJMS

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Abstract:The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Mycobacterium tuberculosis). As a result, the substantially higher sensitivity of SVM and BLAST was observed compared with that of PNN and KNN. However, the machine learning algorithms (PNN, KNN and SVM) were found capable of substantially reducing the false discovery rate (SVM < PNN < KNN). In sum, 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.

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Section: Methodsmentioning

confidence: 99%

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

Yang

et al. 2018

IJMS

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“…To develop a useful sequence-based statistical predictor for a biological system as reported in a series of recent publications [74][75][76][77][78][79][80][81][82][83], the Chou's 5-step rule should be observed [84]: (1) How to construct or select a valid dataset to train and test the predictor? (2) How to formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted?…”

Section: Methodsmentioning

confidence: 99%

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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“…As pointed out in [ 76 ], user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors or any computational tools. Actually, user-friendly web-servers as given in a series of recent publications [ 23 - 25 , 30 , 32 , 34 - 36 , 69 , 70 , 77 - 91 ] will significantly enhance the impacts by attracting the broad experimental scientists [ 66 , 92 ]. We will do our best to establish a web-server for 2L-PCA as soon as possible.…”

Section: Methodsmentioning

confidence: 99%

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

Wang

Xie

et al. 2017

Oncotarget

Self Cite

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A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

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Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression

Cited by 56 publications

References 69 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

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

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

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