51
|
Guo Y, Hou L, Zhu W, Wang P. Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes. Front Genet 2021; 12:797641. [PMID: 34887905 PMCID: PMC8650314 DOI: 10.3389/fgene.2021.797641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/05/2021] [Indexed: 11/29/2022] Open
Abstract
Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body's life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.
Collapse
Affiliation(s)
- Yuxin Guo
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Liping Hou
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Peng Wang
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| |
Collapse
|
52
|
Zhao Q, Ma J, Wang Y, Xie F, Lv Z, Xu Y, Shi H, Han K. Mul-SNO: A novel prediction tool for S-nitrosylation sites based on deep learning methods. IEEE J Biomed Health Inform 2021; 26:2379-2387. [PMID: 34762593 DOI: 10.1109/jbhi.2021.3123503] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Protein s-nitrosylation (SNO is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM and bidirectional encoder representations from Transformers (BERT . Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0.911 and 0.796 based on 10-fold cross-validation and independent data sets, respectively. The prediction server can be obtained for free at http://lab.malab.cn/~mjq/Mul-SNO/.
Collapse
|
53
|
Zhang Y, Jiang Z, Chen C, Wei Q, Gu H, Yu B. DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier. Interdiscip Sci 2021; 14:311-330. [PMID: 34731411 DOI: 10.1007/s12539-021-00488-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Accurate prediction of drug-target interactions (DTIs), which is often used in the fields of drug discovery and drug repositioning, is regarded a key challenge in the study of drug science. In this paper, a new method called DeepStack-DTIs is proposed to predict DTIs. First, for the target protein, pseudo-position specific score matrix, pseudo amino acid composition and SPIDER3 are used to extract the different feature information of the target protein. Meanwhile, the path-based fingerprint features of each drug are extracted. Then, the synthetic minority oversampling technique (SMOTE) and light gradient boosting machine (LightGBM) are used for data balancing and feature selection, respectively. Finally, the processed features are input to the deep-stacked ensemble classifier composed of gated recurrent unit (GRU), deep neural network (DNN), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and logistic regression (LR) to predict DTIs. Under the five-fold cross-validation and compared with existing methods, the proposed method achieves higher prediction accuracy on the gold standard dataset. To evaluate the predictive power of DeepStack-DTIs, we validate the method on another dataset and predict the drug-target interaction network. The results indicate that DeepStack-DTIs has excellent predictive ability than the other methods, and provides novel insights for the prediction of DTIs. A novel method DeepStack-DTIs for drug-target interactions prediction. PsePSSM, PseAAC, SPIDER3 and FP2 are fused to convert protein sequence and drug molecule information into digital information, respectively. The SMOTE algorithm is used to balance the dataset and LightGBM feature selection algorithm is employed to remove redundant and irrelevant features to select the optimal feature subset. This optimal feature subset is inputted into the deep-stacked ensemble classifier to predict drug-target interactions. The experimental results show DeepStack-DTIs method can significantly improve the prediction accuracy of drug-target interactions.
Collapse
Affiliation(s)
- Yan Zhang
- College of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China.,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
| | - Zhiwen Jiang
- 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
- School of Computer Science and Technology, Shandong University, Qingdao, 266237, China
| | - Qinqin Wei
- 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
| | - Haiming Gu
- 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
| | - Bin Yu
- 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. .,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, China.
| |
Collapse
|
54
|
Lyu Y, Zhang Z, Li J, He W, Ding Y, Guo F. iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2809-2815. [PMID: 33481715 DOI: 10.1109/tcbb.2021.3053608] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://github.com/Not-so-middle/iEnhancer-KL.git.
Collapse
|
55
|
Jiao S, Zou Q, Guo H, Shi L. iTTCA-RF: a random forest predictor for tumor T cell antigens. J Transl Med 2021; 19:449. [PMID: 34706730 PMCID: PMC8554859 DOI: 10.1186/s12967-021-03084-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/16/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging. METHODS In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm. RESULTS Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA . CONCLUSIONS We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.
Collapse
Affiliation(s)
- Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Huannan Guo
- Department of Oncology, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China.
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
| |
Collapse
|
56
|
Liu T, Chen J, Zhang Q, Hippe K, Hunt C, Le T, Cao R, Tang H. The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite. Curr Med Chem 2021; 29:807-821. [PMID: 34636289 DOI: 10.2174/0929867328666211005140625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/28/2021] [Accepted: 08/15/2021] [Indexed: 11/22/2022]
Abstract
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
Collapse
Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Jiamao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University. United States
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University. United States
| | - Thu Le
- Department of Computer Science, Pacific Lutheran University. United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University. United States
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| |
Collapse
|
57
|
Wang Y, Xu L, Zou Q, Lin C. prPred-DRLF: Plant R protein predictor using deep representation learning features. Proteomics 2021; 22:e2100161. [PMID: 34569713 DOI: 10.1002/pmic.202100161] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/30/2021] [Accepted: 09/21/2021] [Indexed: 12/17/2022]
Abstract
Plant resistance (R) proteins play a significant role in the detection of pathogen invasion. Accurately predicting plant R proteins is a key task in phytopathology. Most plant R protein predictors are dependent on traditional feature extraction methods. Recently, deep representation learning methods have been successfully applied in solving protein classification problems. Motivated by this, we propose a new computational approach, called prPred-DRLF, which uses deep representation learning feature models to encode the amino acids as numerical vectors. The results show that the fused features of bidirectional long short-term memory (BiLSTM) embedding and unified representation (UniRep) embedding have a better performance than other features for plant R protein identification using a light gradient boosting machine (LGBM) classifier. The model was evaluated using an independent test achieving an accuracy of 0.956, F1-score of 0.933, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.997. Meanwhile, compared with the state-of-the-art prPred and HMMER method, prPred-DRLF shows an overall improvement in accuracy, F1-score, AUC, and recall. prPred-DRLF is a higher-performance plant R protein prediction tool based on two kinds of deep representation learning technologies and offers a user-friendly interface for inspecting possible plant R proteins. We hope that prPred-DRLF will become a useful tool for biological research. A user-friendly webserver for prPred-DRLF is freely accessible at http://lab.malab.cn/soft/prPred-DRLF. The Python script can be downloaded from https://github.com/Wangys-prog/prPred-DRLF.
Collapse
Affiliation(s)
- Yansu Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Chen Lin
- School of Informatics, Xiamen University, Xiamen, China
| |
Collapse
|
58
|
Maurya R, Pathak VK, Burget R, Dutta MK. Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach. Comput Biol Med 2021; 137:104862. [PMID: 34534793 DOI: 10.1016/j.compbiomed.2021.104862] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/26/2021] [Accepted: 09/07/2021] [Indexed: 11/30/2022]
Abstract
The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.
Collapse
Affiliation(s)
- Ritesh Maurya
- Centre for Advanced Studies, Dr A.P.J. Abdul Kalam Technical University, Lucknow, India.
| | | | - Radim Burget
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, BRNO University of Technology, Czech Republic.
| | - Malay Kishore Dutta
- Centre for Advanced Studies, Dr A.P.J. Abdul Kalam Technical University, Lucknow, India.
| |
Collapse
|
59
|
Qiu W, Lv Z, Xiao X, Shao S, Lin H. EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks. Comput Struct Biotechnol J 2021; 19:4961-4969. [PMID: 34527200 PMCID: PMC8437786 DOI: 10.1016/j.csbj.2021.08.044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/07/2021] [Accepted: 08/27/2021] [Indexed: 11/15/2022] Open
Abstract
An computational method was developed to identify G-protein coupled receptors. Three word-embedding models and a bag-of-words model are used to extract original features. A high accuracy was achieved by using fusion information. A powerful tool was established.
G Protein-Coupled Receptors (GPCRs) are one of the largest membrane protein receptor family in human, which are also important targets for many drugs. Thence, it’s of great significance to judge whether a protein is a GPCR or not. However, identifying GPCRs by experimental methods is very expensive and time-consuming. As more and more GPCR primary sequences are accumulated, it’s feasible to develop a computational model to predict GPCRs precisely and quickly. In this paper, a novel method called EMCBOW-GPCR has been proposed to improve the accuracy of identifying GPCRs based on natural language processing (NLP). For representing GPCRs, three word-embedding models and a bag-of-words model are used to extract original features. Then, the original features are thrown into a Deep-learning algorithm to extract features further and reduce the dimension. Finally, the obtained features are fed into Extreme Gradient Boosting. As shown with the results comparison, the overall prediction metrics of EMCBOW-GPCR are higher than the state of the arts. In order to be convenient for more researchers to use EMCBOW-GPCR, the method and source code have been opened in github, which are available at https://github.com/454170054/EMCBOW-GPCR, and a user-friendly web-server for EMCBOW-GPCR has been established at http://www.jci-bioinfo.cn/emcbowgpcr.
Collapse
Affiliation(s)
- Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Zhe Lv
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Shuai Shao
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
60
|
Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 2021; 21:408-420. [PMID: 30649170 DOI: 10.1093/bib/bby124] [Citation(s) in RCA: 107] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/16/2022] Open
Abstract
Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.
Collapse
Affiliation(s)
- Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jie Hu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| |
Collapse
|
61
|
Chen C, Shi H, Jiang Z, Salhi A, Chen R, Cui X, Yu B. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Comput Biol Med 2021; 136:104676. [PMID: 34375902 DOI: 10.1016/j.compbiomed.2021.104676] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023]
Abstract
Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non-redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning.
Collapse
Affiliation(s)
- 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; School of Computer Science and Technology, Shandong University, Qingdao, 266237, China
| | - Han Shi
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Zhiwen Jiang
- 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
| | - Adil Salhi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Ruixin 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
| | - Xuefeng Cui
- School of Computer Science and Technology, Shandong University, Qingdao, 266237, China
| | - Bin Yu
- 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; Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, China.
| |
Collapse
|
62
|
Zhou J, Bo S, Wang H, Zheng L, Liang P, Zuo Y. Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy. Front Cell Dev Biol 2021; 9:707938. [PMID: 34336861 PMCID: PMC8323781 DOI: 10.3389/fcell.2021.707938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/10/2021] [Indexed: 11/17/2022] Open
Abstract
The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.
Collapse
Affiliation(s)
- Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Suling Bo
- College of Computer and Information, Inner Mongolia Medical University, Hohhot, China
| | - Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| |
Collapse
|
63
|
Feng P, Feng L, Tang C. Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae. Curr Pharm Des 2021; 27:1219-1229. [PMID: 33167827 DOI: 10.2174/1381612826666201109110703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/20/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND N6-methyladenosine (m6A) plays critical roles in a broad range of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As complement to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. METHODS In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to conduct a comprehensive review and comparison of existing methods. RESULTS Since research works on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progress of computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites are presented. CONCLUSION Taken together, we anticipate that this review will serve as an important guide for computational analysis of m6A modifications.
Collapse
Affiliation(s)
- Pengmian Feng
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Lijing Feng
- School of Sciences, North China University of Science and Technology, Tangshan 063000, China
| | - Chaohui Tang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| |
Collapse
|
64
|
Xu L, Ru X, Song R. Application of Machine Learning for Drug-Target Interaction Prediction. Front Genet 2021; 12:680117. [PMID: 34234813 PMCID: PMC8255962 DOI: 10.3389/fgene.2021.680117] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/28/2021] [Indexed: 11/13/2022] Open
Abstract
Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.
Collapse
Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Xiaoqing Ru
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Rong Song
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| |
Collapse
|
65
|
Tang F, Zhang L, Xu L, Zou Q, Feng H. The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis. Brief Bioinform 2021; 22:6295810. [PMID: 34113984 DOI: 10.1093/bib/bbab227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/07/2021] [Accepted: 05/24/2021] [Indexed: 12/16/2022] Open
Abstract
Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. By taking advantage of the g-gap tripeptide and tetrapeptide composition feature descriptors, we increased the accuracy of the classification model of cancerlectin and lectin to 98.54% and 95.38%, respectively. About 36 cancerlectin and 135 lectin features were selected for functional characterization by P/N feature ranking method, which particularly selects the features in positive samples. The specific protein domains of cancerlectins are found to be p-GalNAc-T, crystal and annexin by comparing with lectins through the exclusion method. Moreover, the combined GO analysis showed that the conserved cation binding sites of cancerlectin specific domains are covered by selected feature peptides, suggesting that the capability of cation binding, critical for enzyme activity and stability, could be the key characteristic of cancerlectins in tumor progression. These results will help to identify potential cancerlectin and provide clues for mechanism study of cancerlectin in tumor progression.
Collapse
Affiliation(s)
- Furong Tang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518000, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen 518172, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hailin Feng
- School of Information Engineering Zhejiang A&F University, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou, Zhejiang 311300, China
| |
Collapse
|
66
|
Nagarajan G, Dhinesh Babu LD. A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification. ACTA ACUST UNITED AC 2021; 10:39. [PMID: 34094808 PMCID: PMC8170065 DOI: 10.1007/s13721-021-00313-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 04/30/2021] [Accepted: 05/06/2021] [Indexed: 11/29/2022]
Abstract
Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.
Collapse
Affiliation(s)
- Gayathri Nagarajan
- School of Information Technology and Engineering, VIT university, Vellore, India
| | - L. D. Dhinesh Babu
- School of Information Technology and Engineering, VIT university, Vellore, India
| |
Collapse
|
67
|
Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage. Chem Biol Interact 2021; 345:109533. [PMID: 34051207 DOI: 10.1016/j.cbi.2021.109533] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 05/17/2021] [Accepted: 05/24/2021] [Indexed: 12/16/2022]
Abstract
In recent years, various AI-based methods have been developed in order to uncover chemico-biological interactions associated with DNA damage and oxidative stress. Various decision trees, bayesian networks, random forests, logistic regression models, support vector machines as well as deep learning tools, have great potential in the area of molecular biology and toxicology, and it is estimated that in the future, they will greatly contribute to our understanding of molecular and cellular mechanisms associated with DNA damage and repair. In this concise review, we discuss recent attempts to build machine learning tools for assessment of radiation - induced DNA damage as well as algorithms that can analyze the data from the most frequently used DNA damage assays in molecular biology. We also review recent works on the detection of antioxidant proteins with machine learning, and the use of AI-related methods for prediction and evaluation of noncoding DNA sequences. Finally, we discuss previously published research on the potential application of machine learning tools in aging research.
Collapse
|
68
|
ANOX: A robust computational model for predicting the antioxidant proteins based on multiple features. Anal Biochem 2021; 631:114257. [PMID: 34043981 DOI: 10.1016/j.ab.2021.114257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/12/2021] [Accepted: 05/14/2021] [Indexed: 11/20/2022]
Abstract
As an indispensable component of various living organisms, the antioxidant proteins have been studied for anti-aging and prevention of various diseases, such as altitude sickness, coronary heart disease, and even cancer. However, the traditional experimental methods for identifying the antioxidant proteins are very expensive and time-consuming. Thus, to address the challenge, a new predictor, named ANOX, was developed in this study. Multiple features, such as frequency matrix features (FRE), amino acid and dipeptide composition (AADP), evolutionary difference formula features (EEDP), k-separated bigrams (KSB), and PSI-PRED secondary structure (PRED), were extracted to generate the original feature space. To find the optimized feature subset, the Max-Relevance-Max-Distance (MRMD) algorithm was implemented for feature ranking and our model received the best performance with the top 1170 features. Rigorous tests were performed to evaluate the performance of ANOX, and the results showed that ANOX achieved a major improvement in the prediction accuracy of the antioxidant proteins (AUC:0.930 and 0.935 using 5-fold cross-validation or the jackknife test) compared to the state-of-the-art predictor AOPs-SVM (AUC:0.869 and 0.885). The dataset used in this study and the source code of ANOX are all available at https://github.com/NWAFU-LiuLab/ANOX.
Collapse
|
69
|
Younis H, Anwar MW, Khan MUG, Sikandar A, Bajwa UI. A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein-Protein Interaction Networks (PPINs). Interdiscip Sci 2021; 13:371-388. [PMID: 33959851 DOI: 10.1007/s12539-021-00433-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.
Collapse
Affiliation(s)
- Haseeb Younis
- School of Professional Advancement, University of Management and Technology, Lahore, Pakistan.,Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
| | | | - Muhammad Usman Ghani Khan
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Aisha Sikandar
- Govt. Girls Post Graduate College No.1 Abbottabad, Abbottabad, Pakistan
| | - Usama Ijaz Bajwa
- Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
| |
Collapse
|
70
|
An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9933873. [PMID: 33987446 PMCID: PMC8093043 DOI: 10.1155/2021/9933873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 11/24/2022]
Abstract
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.
Collapse
|
71
|
A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions. Genomics 2021; 113:1778-1789. [PMID: 33878365 DOI: 10.1016/j.ygeno.2021.04.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/14/2021] [Indexed: 01/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unraveled the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.
Collapse
|
72
|
Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
Collapse
Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| |
Collapse
|
73
|
Zhang ZM, Guan ZX, Wang F, Zhang D, Ding H. Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families. Med Chem 2021; 16:594-604. [PMID: 31584374 DOI: 10.2174/1573406415666191004125551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/18/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.
Collapse
Affiliation(s)
- Zi-Mei Zhang
- 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
| | - Zheng-Xing Guan
- 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
| | - Fang Wang
- 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
| | - Dan Zhang
- 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
| |
Collapse
|
74
|
Jing XY, Li FM. Predicting Cell Wall Lytic Enzymes Using Combined Features. Front Bioeng Biotechnol 2021; 8:627335. [PMID: 33585423 PMCID: PMC7874139 DOI: 10.3389/fbioe.2020.627335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
Collapse
Affiliation(s)
- Xiao-Yang Jing
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Feng-Min Li
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
| |
Collapse
|
75
|
He S, Guo F, Zou Q, HuiDing. MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200503030350] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
The study aims to find a way to reduce the dimensionality of the dataset.
Background:
Dimensionality reduction is the key issue of the machine learning process. It does
not only improve the prediction performance but also could recommend the intrinsic features and
help to explore the biological expression of the machine learning “black box”.
Objective:
A variety of feature selection algorithms are used to select data features to achieve
dimensionality reduction.
Methods:
First, MRMD2.0 integrated 7 different popular feature ranking algorithms with
PageRank strategy. Second, optimized dimensionality was detected with forward adding strategy.
Result:
We have achieved good results in our experiments.
Conclusion:
Several works have been tested with MRMD2.0. It showed well performance.
Otherwise, it also can draw the performance curves according to the feature dimensionality. If
users want to sacrifice accuracy for fewer features, they can select the dimensionality from the
performance curves.
Other:
We developed friendly python tools together with the web server. The users could upload
their csv, arff or libsvm format files. Then the webserver would help to rank features and find the
optimized dimensionality.
Collapse
Affiliation(s)
- Shida He
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - HuiDing
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
76
|
Recent Advances in Predicting Protein S-Nitrosylation Sites. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5542224. [PMID: 33628788 PMCID: PMC7892234 DOI: 10.1155/2021/5542224] [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: 01/07/2021] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 01/09/2023]
Abstract
Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.
Collapse
|
77
|
Using a low correlation high orthogonality feature set and machine learning methods to identify plant pentatricopeptide repeat coding gene/protein. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.02.079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
78
|
Shen Z, Zou Q. Basic polar and hydrophobic properties are the main characteristics that affect the binding of transcription factors to methylation sites. Bioinformatics 2021; 36:4263-4268. [PMID: 32399547 DOI: 10.1093/bioinformatics/btaa492] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/13/2020] [Accepted: 05/06/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Methylation and transcription factors (TFs) are part of the mechanisms regulating gene expression. However, the numerous mechanisms regulating the interactions between methylation and TFs remain unknown. We employ machine-learning techniques to discover the characteristics of TFs that bind to methylation sites. RESULTS The classical machine-learning analysis process focuses on improving the performance of the analysis method. Conversely, we focus on the functional properties of the TF sequences. We obtain the principal properties of TFs, namely, the basic polar and hydrophobic Ile amino acids affecting the interaction between TFs and methylated DNA. The recall of the positive instances is 0.878 when their basic polar value is >0.1743. Both basic polar and hydrophobic Ile amino acids distinguish 74% of TFs bound to methylation sites. Therefore, we infer that basic polar amino acids affect the interactions of TFs with methylation sites. Based on our results, the role of the hydrophobic Ile residue is consistent with that described in previous studies, and the basic polar amino acids may also be a key factor modulating the interactions between TFs and methylation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zijie Shen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
79
|
Tang X, Cai L, Meng Y, Xu J, Lu C, Yang J. Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19. Front Immunol 2021; 11:603615. [PMID: 33584672 PMCID: PMC7878370 DOI: 10.3389/fimmu.2020.603615] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/22/2020] [Indexed: 12/24/2022] Open
Abstract
A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.
Collapse
Affiliation(s)
- Xianfang Tang
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Lijun Cai
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Yajie Meng
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - JunLin Xu
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Changcheng Lu
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Jialiang Yang
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
- Academician Workstation, Changsha Medical University, Changsha, China
| |
Collapse
|
80
|
Yang L, Jiao X. Distinguishing Enzymes and Non-enzymes Based on Structural Information with an Alignment Free Approach. Curr Bioinform 2021. [DOI: 10.2174/1574893615666200324134037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Knowledge of protein functions is very crucial for the understanding of biological processes. Experimental methods for protein function prediction are powerless to treat the growing amount of protein sequence and structure data.
Objective:
To develop some computational techniques for the protein function prediction.
Method:
Based on the residue interaction network features and the motion mode information, an
SVM model was constructed and used as the predictor. The role of these features was analyzed
and some interesting results were obtained.
Results:
An alignment-free method for the classification of enzyme and non-enzyme is developed in this work. There is not any single feature that occupies a dominant position in the prediction process. The topological and the information-theoretic residue interaction network features have a better performance. The combination of the fast mode and the slow mode can get a better explanation for the classification result.
Conclusion:
The method proposed in this paper can act as a classifier for the enzymes and nonenzymes.
Collapse
Affiliation(s)
- Lifeng Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600,China
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030600,China
| |
Collapse
|
81
|
Lv Z, Wang P, Zou Q, Jiang Q. Identification of Sub-Golgi protein localization by use of deep representation learning features. Bioinformatics 2020; 36:5600-5609. [PMID: 33367627 PMCID: PMC8023683 DOI: 10.1093/bioinformatics/btaa1074] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/10/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022] Open
Abstract
Motivation The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology. Results we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction. Availabilityand implementation A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| |
Collapse
|
82
|
Zhang YP, Zou Q. PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics 2020; 36:3982-3987. [PMID: 32348463 DOI: 10.1093/bioinformatics/btaa275] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 03/31/2020] [Accepted: 04/22/2020] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning-based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high-quality generic prediction and informative physicochemical properties (IPPs) identification simultaneously. RESULTS In this work, Physicochemical Property-based Therapeutic Peptide Predictor (PPTPP), a Random Forest-based prediction method was presented to address this issue. A novel feature encoding and learning scheme were initiated to produce and rank physicochemical property-related features. Besides being capable of predicting multiple therapeutics peptides with high comparability to established predictors, the presented method is also able to identify peptides' informative IPP. Results presented in this work not only illustrated the soundness of its working capacity but also demonstrated its potential for investigating other therapeutic peptides. AVAILABILITY AND IMPLEMENTATION https://github.com/YPZ858/PPTPP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yu P Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
83
|
Relevance assignation feature selection method based on mutual information for machine learning. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106439] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
84
|
Wang R, Wang Z, Wang H, Pang Y, Lee TY. Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian. Sci Rep 2020; 10:20447. [PMID: 33235255 PMCID: PMC7686339 DOI: 10.1038/s41598-020-77173-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/03/2020] [Indexed: 12/14/2022] Open
Abstract
Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.
Collapse
Affiliation(s)
- Rulan Wang
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, Guangdong, People's Republic of China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, Guangdong, People's Republic of China.,School of Life Sciences, University of Science and Technology of China, Hefei, 230026, Anhui, People's Republic of China
| | - Hongfei Wang
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Yuxuan Pang
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, Guangdong, People's Republic of China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, Guangdong, People's Republic of China.
| |
Collapse
|
85
|
Li Y, Zhang Z, Teng Z, Liu X. PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8845133. [PMID: 33294004 PMCID: PMC7700051 DOI: 10.1155/2020/8845133] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/31/2020] [Indexed: 01/20/2023]
Abstract
Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.
Collapse
Affiliation(s)
- Yanjuan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zitong Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Xiaoyan Liu
- College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150040, China
| |
Collapse
|
86
|
Yang L, Gao H, Wu K, Zhang H, Li C, Tang L. Identification of Cancerlectins By Using Cascade Linear Discriminant Analysis and Optimal g-gap Tripeptide Composition. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190730103156] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background:
Lectins are a diverse group of glycoproteins or glycoconjugate proteins
that can be extracted from plants, invertebrates and higher animals. Cancerlectins, a kind of lectins,
which play a key role in the process of tumor cells interacting with each other and are being employed
as therapeutic agents. A full understanding of cancerlectins is significant because it provides
a tool for the future direction of cancer therapy.
Objective:
To develop an accurate and practically useful timesaving tool to identify cancerlectins.
A novel sequence-based method is proposed along with a correlative webserver to access the proposed
tool.
Methods:
Firstly, protein features were extracted in a newly feature building way termed, g-gap
tripeptide composition. After which a proposed cascade linear discriminant analysis (Cascade
LDA) is used to alleviate the high dimensional difficulties with the Analysis Of Variance (ANOVA)
as a feature importance criterion. Finally, Support Vector Machine (SVM) is used as the classifier
to identify cancerlectins.
Results:
The proposed method achieved an accuracy of 91.34% with sensitivity of 89.89%, specificity
of 92.48% and an 0.8318 Mathew’s correlation coefficient based on only 13 fusion features
in jackknife cross validation, the result of which is superior to other published methods in this domain.
Conclusion:
In this study, a new method based only on primary structure of protein is proposed
and experimental results show that it could be a promising tool to identify cancerlectins. An openaccess
webserver is made available in this work to facilitate other related works.
Collapse
Affiliation(s)
- Liangwei Yang
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Gao
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Keyu Wu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Haotian Zhang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Changyu Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lixia Tang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
87
|
Sequence based prediction of pattern recognition receptors by using feature selection technique. Int J Biol Macromol 2020; 162:931-934. [DOI: 10.1016/j.ijbiomac.2020.06.234] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 01/04/2023]
|
88
|
Ao C, Zhou W, Gao L, Dong B, Yu L. Prediction of antioxidant proteins using hybrid feature representation method and random forest. Genomics 2020; 112:4666-4674. [DOI: 10.1016/j.ygeno.2020.08.016] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/10/2020] [Accepted: 08/13/2020] [Indexed: 12/19/2022]
|
89
|
Meng C, Wu J, Guo F, Dong B, Xu L. CWLy-pred: A novel cell wall lytic enzyme identifier based on an improved MRMD feature selection method. Genomics 2020; 112:4715-4721. [DOI: 10.1016/j.ygeno.2020.08.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/04/2020] [Accepted: 08/13/2020] [Indexed: 10/25/2022]
|
90
|
Chakraborty R, Hasija Y. Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2183-2188. [PMID: 31443043 DOI: 10.1109/tcbb.2019.2936186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea is to extract the features from sequence of mRNA using CNN and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2-8 nucleotides starting at the 5' end and counting toward the 3' end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data, the model was able to predict on average 72 percent of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools. Codes are available at https://github.com/rajkumar1501/sequence-prediction-using-CNN-and-LSTMs.
Collapse
|
91
|
Zhai Y, Chen Y, Teng Z, Zhao Y. Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions. Front Cell Dev Biol 2020; 8:591487. [PMID: 33195258 PMCID: PMC7658297 DOI: 10.3389/fcell.2020.591487] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/18/2020] [Indexed: 12/13/2022] Open
Abstract
Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body’s oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model’s sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.
Collapse
Affiliation(s)
- Yixiao Zhai
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
| | - Yu Chen
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
| | - Zhixia Teng
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
| |
Collapse
|
92
|
A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8926750. [PMID: 33133228 PMCID: PMC7591939 DOI: 10.1155/2020/8926750] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 08/14/2020] [Accepted: 09/16/2020] [Indexed: 12/14/2022]
Abstract
With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.
Collapse
|
93
|
Srivani B, Sandhya N, Padmaja Rani B. Literature review and analysis on big data stream classification techniques. INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2020. [DOI: 10.3233/kes-200042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.
Collapse
Affiliation(s)
| | - N. Sandhya
- CSE Department, VNRVJIET, Hyderabad, Telangana, India
| | | |
Collapse
|
94
|
Chen T, Wang X, Chu Y, Wang Y, Jiang M, Wei DQ, Xiong Y. T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm. Front Microbiol 2020; 11:580382. [PMID: 33072049 PMCID: PMC7541839 DOI: 10.3389/fmicb.2020.580382] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/21/2020] [Indexed: 12/19/2022] Open
Abstract
Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.
Collapse
Affiliation(s)
- Tianhang Chen
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangeng Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Mingming Jiang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
95
|
Xu L, Liang G, Chen B, Tan X, Xiang H, Liao C. A Computational Method for the Identification of Endolysins and Autolysins. Protein Pept Lett 2020; 27:329-336. [PMID: 31577192 DOI: 10.2174/0929866526666191002104735] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/27/2019] [Accepted: 09/03/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. OBJECTIVE In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. METHODS We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. RESULTS Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. CONCLUSION The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.
Collapse
Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Baowen Chen
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Xu Tan
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| |
Collapse
|
96
|
Sun Z, Huang S, Zheng L, Liang P, Yang W, Zuo Y. ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors. Comput Biol Chem 2020; 89:107371. [PMID: 32950852 DOI: 10.1016/j.compbiolchem.2020.107371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 12/27/2022]
Abstract
Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.
Collapse
Affiliation(s)
- Zijie Sun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; School of Mathematical Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
| |
Collapse
|
97
|
Refahi MS, Mir A, Nasiri JA. A novel fusion based on the evolutionary features for protein fold recognition using support vector machines. Sci Rep 2020; 10:14368. [PMID: 32873824 PMCID: PMC7463267 DOI: 10.1038/s41598-020-71172-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 08/10/2020] [Indexed: 11/29/2022] Open
Abstract
Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance and Separated dimer evolutionary feature extraction methods. The results’ features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and EDD, the results of the support vector machine show more than 6\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\%$$\end{document}% improvement in accuracy on these benchmark datasets.
Collapse
Affiliation(s)
- Mohammad Saleh Refahi
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - A Mir
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
| | - Jalal A Nasiri
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran.
| |
Collapse
|
98
|
Zhang L, Xiao X, Xu ZC. iPromoter-5mC: A Novel Fusion Decision Predictor for the Identification of 5-Methylcytosine Sites in Genome-Wide DNA Promoters. Front Cell Dev Biol 2020; 8:614. [PMID: 32850787 PMCID: PMC7399635 DOI: 10.3389/fcell.2020.00614] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 06/22/2020] [Indexed: 11/29/2022] Open
Abstract
The hypomethylation of the whole cancer genome and the hypermethylation of the promoter of specific tumor suppressor genes are the important reasons for the rapid proliferation of cancer cells. Therefore, obtaining the distribution of 5-methylcytosine (5mC) in promoters is a key step to further understand the relationship between promoter methylation and mRNA gene expression regulation. Large-scale detection of DNA 5mC through wet experiments is still time-consuming and laborious. Therefore, it is urgent to design a method for identifying the 5mC site of genome-wide DNA promoters. Based on promoter methylation data of the small cell lung cancer (SCLC) from the database named cancer cell line Encyclopedia (CCLE), we built a fusion decision predictor called iPromoter-5mC for identifying methylation modification sites in promoters using deep neural network (DNN). One-Hot Encoding (One-hot) was used to encode the promoter samples for the classification. The method achieves average AUC of 0.957 on the independent testing dataset, indicating that our predictor is robust and reliable. A user-friendly web-server called iPromoter-5mC could be freely accessible at http://www.jci-bioinfo.cn/iPromoter-5mC, which will provide simple and effective means for users to study promoter 5mC modification. The source code of the proposed methods is freely available for academic research at https://github.com/zlwuxi/iPromoter-5mC.
Collapse
Affiliation(s)
- Lei Zhang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, China
| | - Zhao-Chun Xu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen, China
| |
Collapse
|
99
|
Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:1043-1050. [PMID: 33294291 PMCID: PMC7691157 DOI: 10.1016/j.omtn.2020.07.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022]
Abstract
Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.
Collapse
|
100
|
Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features. Genomics 2020; 112:4342-4347. [PMID: 32721444 DOI: 10.1016/j.ygeno.2020.07.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 07/18/2020] [Accepted: 07/22/2020] [Indexed: 12/14/2022]
Abstract
N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.
Collapse
|