1
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Liu B, Yang Z, Liu Q, Zhang Y, Ding H, Lai H, Li Q. Computational prediction of allergenic proteins based on multi-feature fusion. Front Genet 2023; 14:1294159. [PMID: 37928245 PMCID: PMC10622758 DOI: 10.3389/fgene.2023.1294159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.
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Affiliation(s)
- Bin Liu
- Department of Anesthesiology, The Fourth People's Hospital of Sichuan Province, Chengdu, Sichuan, China
| | - Ziman Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hui Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyan Lai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Research Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
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2
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Shomali A, Vafaei Sadi MS, Bakhtiarizadeh MR, Aliniaeifard S, Trewavas A, Calvo P. Identification of intelligence-related proteins through a robust two-layer predictor. Commun Integr Biol 2022; 15:253-264. [DOI: 10.1080/19420889.2022.2143101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Aida Shomali
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | | | - Sasan Aliniaeifard
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Anthony Trewavas
- School of Biological Sciences, Institute of Molecular Plant Science, University of Edinburgh, UK
| | - Paco Calvo
- Minimal Intelligence Lab, University of Murcia, Spain
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3
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Liu S, Cui C, Chen H, Liu T. Ensemble Learning-Based Feature Selection for Phage Protein Prediction. Front Microbiol 2022; 13:932661. [PMID: 35910662 PMCID: PMC9335128 DOI: 10.3389/fmicb.2022.932661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022] Open
Abstract
Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, traditional experimental methods are both expensive and time-consuming. In this study, an ensemble learning-based feature selection method is proposed to find important features for phage protein identification. The method uses four types of protein sequence-derived features, quantifies the importance of each feature by adding perturbations to the features to influence the results, and finally splices the important features among the four types of features. In addition, we analyzed the selected features and their biological significance.
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Affiliation(s)
- Songbo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chengmin Cui
- Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing, China
| | - Huipeng Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- *Correspondence: Huipeng Chen
| | - Tong Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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4
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βLact-Pred: A Predictor Developed for Identification of Beta-Lactamases Using Statistical Moments and PseAAC via 5-Step Rule. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8974265. [PMID: 34956358 PMCID: PMC8709780 DOI: 10.1155/2021/8974265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/22/2021] [Indexed: 12/02/2022]
Abstract
Beta-lactamase (β-lactamase) produced by different bacteria confers resistance against β-lactam-containing drugs. The gene encoding β-lactamase is plasmid-borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the β-lactam family. β-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as βLact-Pred for the identification of β-lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta-lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self-consistency testing, jackknife testing, cross-validation, and independent testing. The overall accuracy of the predictor for self-consistency, jackknife testing, cross-validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor “βLact-Pred” has surpassed results from the existing methods.
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5
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Wang M, Yue L, Yang X, Wang X, Han Y, Yu B. Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Shah AA, Khan YD. Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification. Sci Rep 2020; 10:16913. [PMID: 33037248 PMCID: PMC7547663 DOI: 10.1038/s41598-020-73107-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 08/20/2020] [Indexed: 11/08/2022] Open
Abstract
Glutamic acid is an alpha-amino acid used by all living beings in protein biosynthesis. One of the important glutamic acid modifications is post-translationally modified 4-carboxyglutamate. It has a significant role in blood coagulation. 4-carboxyglumates are required for the binding of calcium ions. On the contrary, this modification can also cause different diseases such as bone resorption, osteoporosis, papilloma, and plaque atherosclerosis. Considering its importance, it is necessary to predict the occurrence of glutamic acid carboxylation in amino acid stretches. As there is no computational based prediction model available to identify 4-carboxyglutamate modification, this study is, therefore, designed to predict 4-carboxyglutamate sites with a less computational cost. A machine learning model is devised with a Multilayered Perceptron (MLP) classifier using Chou's 5-step rule. It may help in learning statistical moments and based on this learning, the prediction is to be made accurately either it is 4-carboxyglutamate residue site or detected residue site having no 4-carboxyglutamate. Prediction accuracy of the proposed model is 94% using an independent set test, while obtained prediction accuracy is 99% by self-consistency tests.
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Affiliation(s)
- Asghar Ali Shah
- Department of Computer Sciences, Bahria University Lahore Campus, Lahore, 25000, Pakistan.
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7
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Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8894478. [PMID: 33029195 PMCID: PMC7530508 DOI: 10.1155/2020/8894478] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/08/2020] [Accepted: 09/14/2020] [Indexed: 11/29/2022]
Abstract
Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100. In this paper, improved methods for HSP prediction are proposed—the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM). In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset. The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature. The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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8
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Bian H, Guo M, Wang J. Recognition of Mitochondrial Proteins in Plasmodium Based on the Tripeptide Composition. Front Cell Dev Biol 2020; 8:578901. [PMID: 33043014 PMCID: PMC7525148 DOI: 10.3389/fcell.2020.578901] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/13/2020] [Indexed: 01/31/2023] Open
Abstract
Mitochondria play essential roles in eukaryotic cells, especially in Plasmodium cells. They have several unusual evolutionary and functional features that are incredibly vital for disease diagnosis and drug design. Thus, predicting mitochondrial proteins of Plasmodium has become a worthwhile work. However, existing computational methods can only predict mitochondrial proteins of Plasmodium falciparum (P. falciparum for short), and these methods have low accuracy. It is highly desirable to design a classifier with high accuracy for predicting mitochondrial proteins for all Plasmodium species, not only P. falciparum. We proposed a novel method, named as PM-OTC, for predicting mitochondrial proteins in Plasmodium. PM-OTC uses the Support Vector Machine (SVM) as the classifier and the selected tripeptide composition as the features. We adopted the 5-fold cross-validation method to train and test PM-OTC. Results demonstrate that PM-OTC achieves an accuracy of 94.91%, and performances of PM-OTC are superior to other methods.
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Affiliation(s)
- Haodong Bian
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.,Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, China
| | - Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China.,Stage Key Laboratories of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot, China
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9
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Zhu YH, Hu J, Qi Y, Song XN, Yu DJ. Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites. Comb Chem High Throughput Screen 2020; 22:455-469. [PMID: 31553288 DOI: 10.2174/1386207322666190925125524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 06/21/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors. MATERIALS AND METHODS In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm. RESULTS Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.
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Affiliation(s)
- Yi-Heng Zhu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jun Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yong Qi
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xiao-Ning Song
- School of Internet of Things, Jiangnan University, Wuxi 214122, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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10
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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11
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12
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Arif M, Ahmad S, Ali F, Fang G, Li M, Yu DJ. TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. J Comput Aided Mol Des 2020; 34:841-856. [PMID: 32180124 DOI: 10.1007/s10822-020-00307-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/09/2020] [Indexed: 02/08/2023]
Abstract
Cell-penetrating peptides (CPPs) are short length permeable proteins have emerged as drugs delivery tool of therapeutic agents including genetic materials and macromolecules into cells. Recently, CPP has become a hotspot avenue for life science research and paved a new way of disease treatment without harmful impact on cell viability due to nontoxic characteristic. Therefore, the correct identification of CPPs will provide hints for medical applications. Considering the shortcomings of traditional experimental CPPs identification, it is urgently needed to design intelligent predictor for accurate identification of CPPs for the large scale uncharacterized sequences. We develop a novel computational method, called TargetCPP, to discriminate CPPs from Non-CPPs with improved accuracy. In TargetCPP, first the peptide sequences are formulated with four distinct encoding methods i.e., composite protein sequence representation, composition transition and distribution, split amino acid composition, and information theory features. These dominant feature vectors were fused and applied intelligent minimum redundancy and maximum relevancy feature selection method to choose an optimal subset of features. Finally, the predictive model is learned through different classification algorithms on the optimized features. Among these classifiers, gradient boost decision tree algorithm achieved excellent performance throughout the experiments. Notably, the TargetCPP tool attained high prediction Accuracy of 93.54% and 88.28% using jackknife and independent test, respectively. Empirical outcomes prove the superiority and potency of proposed bioinformatics method over state-of-the-art methods. It is highly anticipated that the outcomes of this study will provide a strong background for large scale prediction of CPPs and instructive guidance in clinical therapy and medical applications.
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Affiliation(s)
- Muhammad Arif
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Saeed Ahmad
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Ge Fang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Min Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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13
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Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2020; 20:638-658. [PMID: 29897410 PMCID: PMC6556904 DOI: 10.1093/bib/bby028] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/02/2018] [Indexed: 01/03/2023] Open
Abstract
Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.
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Affiliation(s)
- Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.,Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Yanan Wang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Neil D Rawlings
- EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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14
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Prediction of Extracellular Matrix Proteins by Fusing Multiple Feature Information, Elastic Net, and Random Forest Algorithm. MATHEMATICS 2020. [DOI: 10.3390/math8020169] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Extracellular matrix (ECM) proteins play an important role in a series of biological processes of cells. The study of ECM proteins is helpful to further comprehend their biological functions. We propose ECMP-RF (extracellular matrix proteins prediction by random forest) to predict ECM proteins. Firstly, the features of the protein sequence are extracted by combining encoding based on grouped weight, pseudo amino-acid composition, pseudo position-specific scoring matrix, a local descriptor, and an autocorrelation descriptor. Secondly, the synthetic minority oversampling technique (SMOTE) algorithm is employed to process the class imbalance data, and the elastic net (EN) is used to reduce the dimension of the feature vectors. Finally, the random forest (RF) classifier is used to predict the ECM proteins. Leave-one-out cross-validation shows that the balanced accuracy of the training and testing datasets is 97.3% and 97.9%, respectively. Compared with other state-of-the-art methods, ECMP-RF is significantly better than other predictors.
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15
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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16
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics 2019; 111:1274-1282. [DOI: 10.1016/j.ygeno.2018.08.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/17/2022]
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18
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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19
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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20
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Yu B, Qiu W, Chen C, Ma A, Jiang J, Zhou H, Ma Q. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 2019; 36:1074-1081. [DOI: 10.1093/bioinformatics/btz734] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 09/04/2019] [Accepted: 09/25/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Motivation
Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design.
Results
We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases.
Availability and implementation
The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China
| | - Wenying Qiu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Cheng Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Jing Jiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- School of Aerospace Engineering, Xiamen University, Xiamen 361001, China
| | - Hongyan Zhou
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 2019; 15:496-509. [DOI: 10.2174/1573406415666181217114710] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/17/2022]
Abstract
Background/Objective:Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.Methods:Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.Results:Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.Conclusion:Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.
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Affiliation(s)
- Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Genqiang Chen
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Qi Mao
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Zhao W, Li GP, Wang J, Zhou YK, Gao Y, Du PF. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J Theor Biol 2019; 473:38-43. [DOI: 10.1016/j.jtbi.2019.04.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 04/29/2019] [Indexed: 12/11/2022]
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Akbar S, Hayat M, Kabir M, Iqbal M. iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180816101653] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
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Tian B, Wu X, Chen C, Qiu W, Ma Q, Yu B. Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. J Theor Biol 2019; 462:329-346. [DOI: 10.1016/j.jtbi.2018.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/08/2018] [Accepted: 11/15/2018] [Indexed: 12/26/2022]
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Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2436924. [PMID: 30834257 PMCID: PMC6374881 DOI: 10.1155/2019/2436924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 01/04/2019] [Accepted: 01/20/2019] [Indexed: 11/29/2022]
Abstract
The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.
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Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics 2018; 111:1785-1793. [PMID: 30529532 DOI: 10.1016/j.ygeno.2018.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA
| | - Peng Wang
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hui-Ting Ge
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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Ju Z, Wang SY. Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm. J Theor Biol 2018; 457:6-13. [DOI: 10.1016/j.jtbi.2018.08.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/07/2018] [Accepted: 08/15/2018] [Indexed: 11/29/2022]
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Zhang S, Liang Y. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J Theor Biol 2018; 457:163-169. [DOI: 10.1016/j.jtbi.2018.08.042] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/25/2018] [Accepted: 08/31/2018] [Indexed: 10/28/2022]
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31
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Liang Y, Zhang S. Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback–Leibler divergence. J Theor Biol 2018; 454:22-29. [DOI: 10.1016/j.jtbi.2018.05.035] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 05/19/2018] [Accepted: 05/29/2018] [Indexed: 12/14/2022]
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Jia JH, Chou KC. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics 2018; 110:239-246. [DOI: 10.1016/j.ygeno.2017.10.008] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 10/23/2017] [Accepted: 10/25/2017] [Indexed: 01/23/2023]
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Hoseini ASH, Mirzarezaee M. Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks. IRANIAN JOURNAL OF BIOTECHNOLOGY 2018; 16:e1933. [PMID: 31457027 PMCID: PMC6697825 DOI: 10.15171/ijb.1933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 01/11/2018] [Accepted: 01/13/2018] [Indexed: 01/09/2023]
Abstract
Background Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from protein sequences. In contrast, protein interactions have been less investigated. Objectives As protein interactions usually occur in the same or adjacent places, using this feature to find the location would be efficient and impressive. This study did not aim at increasing the total accuracy of the conducted research. The study has focused on the features of the proteins’ interaction and their employment which lead to a higher accuracy. Materials and Methods In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results. In this regards, we have gathered some of the most common features including Amino Acid Composition, Dipeptide Compositions, Pseudo Amino Acid Compositions (PseAAC), Position Specific Scoring Matrix (PSSM), Functional Domain, Gene Ontology information, and the Pair-wise sequence alignment. The results of the classification are compared to the ones using protein interactions. For achieving this goal different machine learning algorithms were tested. Results The best-obtained results of using single feature set obtained using SVM classifier for PseAAC feature. The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively. In another experiment, using just protein interaction data with the different cutting points resulted in obtaining an accuracy of 93.035% for the protein location prediction. Conclusion In total, it was shown that protein(s) interaction has a significant impact on the prediction of the mitochondrial proteins’ location. This feature can separately distinguish the locations well. Using this feature the accuracy of the results is raised up to 5%.
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Affiliation(s)
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.,School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition. J Theor Biol 2018; 450:86-103. [DOI: 10.1016/j.jtbi.2018.04.026] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/10/2018] [Accepted: 04/16/2018] [Indexed: 01/16/2023]
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Cheng X, Lin WZ, Xiao X, Chou KC. pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 2018; 35:398-406. [DOI: 10.1093/bioinformatics/bty628] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 07/11/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Xiang Cheng
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Wei-Zhong Lin
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Kuo-Chen Chou
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Bakhtiarizadeh MR, Rahimi M, Mohammadi-Sangcheshmeh A, Shariati J V, Salami SA. PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach. Sci Rep 2018; 8:9025. [PMID: 29899414 PMCID: PMC5998058 DOI: 10.1038/s41598-018-27338-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/25/2018] [Indexed: 11/08/2022] Open
Abstract
Successful spermatogenesis and oogenesis are the two genetically independent processes preceding embryo development. To date, several fertility-related proteins have been described in mammalian species. Nevertheless, further studies are required to discover more proteins associated with the development of germ cells and embryogenesis in order to shed more light on the processes. This work builds on our previous software (OOgenesis_Pred), mainly focusing on algorithms beyond what was previously done, in particular new fertility-related proteins and their classes (embryogenesis, spermatogenesis and oogenesis) based on the support vector machine according to the concept of Chou's pseudo-amino acid composition features. The results of five-fold cross validation, as well as the independent test demonstrated that this method is capable of predicting the fertility-related proteins and their classes with accuracy of more than 80%. Moreover, by using feature selection methods, important properties of fertility-related proteins were identified that allowed for their accurate classification. Based on the proposed method, a two-layer classifier software, named as "PrESOgenesis" ( https://github.com/mrb20045/PrESOgenesis ) was developed. The tool identified a query sequence (protein or transcript) as fertility or non-fertility-related protein at the first layer and then classified the predicted fertility-related protein into different classes of embryogenesis, spermatogenesis or oogenesis at the second layer.
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Affiliation(s)
| | - Maryam Rahimi
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | - Vahid Shariati J
- Genome Center, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites. MOLECULAR THERAPY. NUCLEIC ACIDS 2018; 11:468-474. [PMID: 29858081 PMCID: PMC5992483 DOI: 10.1016/j.omtn.2018.03.012] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 03/25/2018] [Accepted: 03/27/2018] [Indexed: 01/09/2023]
Abstract
RNA modifications are additions of chemical groups to nucleotides or their local structural changes. Knowledge about the occurrence sites of these modifications is essential for in-depth understanding of the biological functions and mechanisms and for treating some genomic diseases as well. With the avalanche of RNA sequences generated in the post-genomic age, many computational methods have been proposed for identifying various types of RNA modifications one by one. However, so far no method whatsoever has been developed for simultaneously identifying several different types of RNA modifications. To address such a challenge, we developed a predictor called "iRNA-3typeA," by which we can simultaneously identify the occurrence sites of the following three most frequently observed modifications in RNA: (1) N1-methyladenosine (m1A), (2) N6-methyladenosine (m6A), and (3) adenosine to inosine (A-to-I). It has been shown via rigorous cross-validations for the RNA sequences from Homo sapiens and Mus musculus transcriptomes that the success rates achieved by the powerful new predictor are quite high. For the convenience of broad experimental scientists, a user-friendly web server for iRNA-3typeA has been established at http://lin-group.cn/server/iRNA-3typeA/. It is anticipated that iRNA-3typeA may become a useful high throughput tool for genome analysis.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China; Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA
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Khan YD, Rasool N, Hussain W, Khan SA, Chou KC. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal Biochem 2018; 550:109-116. [DOI: 10.1016/j.ab.2018.04.021] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 04/19/2018] [Accepted: 04/21/2018] [Indexed: 01/29/2023]
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Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2018. [DOI: 10.1093/bib/bby028 epub ahead of print].] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Yanan Wang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Neil D Rawlings
- EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget 2018; 8:41178-41188. [PMID: 28476023 PMCID: PMC5522291 DOI: 10.18632/oncotarget.17104] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 03/15/2017] [Indexed: 01/24/2023] Open
Abstract
Occurring at cytosine (C) of RNA, 5-methylcytosine (m5C) is an important post-transcriptional modification (PTCM). The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists’ convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.
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iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. J Theor Biol 2018; 442:11-21. [DOI: 10.1016/j.jtbi.2018.01.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/23/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 2018; 7:44310-44321. [PMID: 27322424 PMCID: PMC5190098 DOI: 10.18632/oncotarget.10027] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 05/29/2016] [Indexed: 12/30/2022] Open
Abstract
Protein hydroxylation is a posttranslational modification (PTM), in which a CH group in Pro (P) or Lys (K) residue has been converted into a COH group, or a hydroxyl group (−OH) is converted into an organic compound. Closely associated with cellular signaling activities, this type of PTM is also involved in some major diseases, such as stomach cancer and lung cancer. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of P or K, which ones can be hydroxylated, and which ones cannot? With the explosive growth of protein sequences in the post-genomic age, the problem has become even more urgent. To address such a problem, we have developed a predictor called iHyd-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition (PseAAC) and introducing the “Random Forest” algorithm to operate the calculation. Rigorous jackknife tests indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for iHyd-PseCp has been established at http://www.jci-bioinfo.cn/iHyd-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Bi-Qian Sun
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.,Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 2018; 8:4208-4217. [PMID: 27926534 PMCID: PMC5354824 DOI: 10.18632/oncotarget.13758] [Citation(s) in RCA: 199] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 11/23/2016] [Indexed: 01/14/2023] Open
Abstract
Catalyzed by adenosine deaminase (ADAR), the adenosine to inosine (A-to-I) editing in RNA is not only involved in various important biological processes, but also closely associated with a series of major diseases. Therefore, knowledge about the A-to-I editing sites in RNA is crucially important for both basic research and drug development. Given an uncharacterized RNA sequence that contains many adenosine (A) residues, can we identify which one of them can be of A-to-I editing, and which one cannot? Unfortunately, so far no computational method whatsoever has been developed to address such an important problem based on the RNA sequence information alone. To fill this empty area, we have proposed a predictor called iRNA-AI by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). It has been shown by the rigorous jackknife test and independent dataset test that the performance of the proposed predictor is quite promising. For the convenience of most experimental scientists, a user-friendly web-server for iRNA-AI has been established at http://lin.uestc.edu.cn/server/iRNA-AI/, by which users can easily get their desired results without the need to go through the mathematical details.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
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iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 2018; 7:69783-69793. [PMID: 27626500 PMCID: PMC5342515 DOI: 10.18632/oncotarget.11975] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/06/2016] [Indexed: 02/07/2023] Open
Abstract
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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Qiu WR, Xiao X, Xu ZC, Chou KC. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget 2018; 7:51270-51283. [PMID: 27323404 PMCID: PMC5239474 DOI: 10.18632/oncotarget.9987] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 05/23/2016] [Indexed: 11/26/2022] Open
Abstract
Protein phosphorylation is a posttranslational modification (PTM or PTLM), where a phosphoryl group is added to the residue(s) of a protein molecule. The most commonly phosphorylated amino acids occur at serine (S), threonine (T), and tyrosine (Y). Protein phosphorylation plays a significant role in a wide range of cellular processes; meanwhile its dysregulation is also involved with many diseases. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of S, T, or Y, which ones can be phosphorylated, and which ones cannot? To address this problem, we have developed a predictor called iPhos-PseEn by fusing four different pseudo component approaches (amino acids’ disorder scores, nearest neighbor scores, occurrence frequencies, and position weights) into an ensemble classifier via a voting system. Rigorous cross-validations indicated that the proposed predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iPhos-PseEn has been established at http://www.jci-bioinfo.cn/iPhos-PseEn, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.,Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Sattari MT, Farkhondeh A, Patrick Abraham J. Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:4776-4786. [PMID: 29198027 DOI: 10.1007/s11356-017-0844-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 11/24/2017] [Indexed: 06/07/2023]
Abstract
Water quality is a major concern around the world, particularly in dry climates. Usually, assessment of surface water quality is costly and time-consuming. In this situation, a method which could estimate the water quality accurately with the minimum of hydro-chemical parameters would be appealing. In this study, three data mining methods, namely, M5 model tree, support vector machine (SVM), and Gaussian process (GP), were employed to estimate the sodium adsorption ratio (SAR) indicator in the Shahrchay River located in the west of the Urmia Lake basin, Iran. Results from these methods were compared with an artificial neural network (ANN). Different hydro-chemical parameters were assessed and the most effective parameters were selected. Five combinations of the selected parameters were developed as input parameters to the models. The results indicated that the M5 model tree has a superior performance among the data mining methods, where the combination of sodium and electrical conductivity (Na and EC) is used as input parameters, with a coefficient of determination (R2) = 0.987, root mean squared error (RMSE) = 0.017, mean absolute error (MAE) = 0.012, and mean relative error (MRE) = 5.584. Also, a sensitivity analysis was carried out which reported that the SAR is more sensitive to Na, Ca, and EC, respectively. This research highlights that the M5 model tree can be successfully employed for the estimation of SAR. It also indicates that the practical and simple linear equations and optimization performed with the M5 model tree reduce time and cost.
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Affiliation(s)
- Mohammad Taghi Sattari
- Department of Water Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
| | - Arya Farkhondeh
- Department of Water Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran
| | - John Patrick Abraham
- School of Engineering, University of St. Thomas, Minnesota, 2115 Summit Avenue, St. Paul, MN, USA
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Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC. iDNA6mA-PseKNC: Identifying DNA N 6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 2018; 111:96-102. [PMID: 29360500 DOI: 10.1016/j.ygeno.2018.01.005] [Citation(s) in RCA: 188] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 12/24/2017] [Accepted: 01/07/2018] [Indexed: 11/29/2022]
Abstract
N6-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan 063000, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
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48
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 2018; 7:34558-70. [PMID: 27153555 PMCID: PMC5085176 DOI: 10.18632/oncotarget.9148] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Accepted: 04/09/2016] [Indexed: 01/22/2023] Open
Abstract
Carbonylation is a posttranslational modification (PTM or PTLM), where a carbonyl group is added to lysine (K), proline (P), arginine (R), and threonine (T) residue of a protein molecule. Carbonylation plays an important role in orchestrating various biological processes but it is also associated with many diseases such as diabetes, chronic lung disease, Parkinson's disease, Alzheimer's disease, chronic renal failure, and sepsis. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of K, P, R, or T, which ones can be carbonylated, and which ones cannot? To address this problem, we have developed a predictor called iCar-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset. Rigorous target cross-validations on a same set of carbonylation-known proteins indicated that the new predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iCar-PseCp has been established at http://www.jci-bioinfo.cn/iCar-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can also be used to analyze many other problems in computational proteomics.
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Affiliation(s)
- Jianhua Jia
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Bingxiang Liu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Du PF, Zhao W, Miao YY, Wei LY, Wang L. UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. Int J Mol Sci 2017; 18:ijms18112400. [PMID: 29135934 PMCID: PMC5713368 DOI: 10.3390/ijms18112400] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/01/2017] [Accepted: 11/03/2017] [Indexed: 01/12/2023] Open
Abstract
With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.
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Affiliation(s)
- Pu-Feng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Wei Zhao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Yang-Yang Miao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
- School of Chemical Engineering, Tianjin University, Tianjin 300350, China.
| | - Le-Yi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Likun Wang
- Institute of Systems Biomedicine, Beijing Key Laboratory of Tumor Systems Biology, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
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Liu B, Yang F, Huang DS, Chou KC. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 2017; 34:33-40. [DOI: 10.1093/bioinformatics/btx579] [Citation(s) in RCA: 235] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 09/13/2017] [Indexed: 12/30/2022] Open
Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- The Gordon Life Science Institute, Boston, MA, USA
| | - Fan Yang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
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