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DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning. Molecules 2023; 28:molecules28052284. [PMID: 36903531 PMCID: PMC10005629 DOI: 10.3390/molecules28052284] [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: 12/06/2022] [Revised: 02/02/2023] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.
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2
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Cai J, Wang T, Deng X, Tang L, Liu L. GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning. BMC Genomics 2023; 24:52. [PMID: 36709266 PMCID: PMC9883864 DOI: 10.1186/s12864-022-09034-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/21/2022] [Indexed: 01/29/2023] Open
Abstract
In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .
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Affiliation(s)
- Junzhe Cai
- grid.410739.80000 0001 0723 6903School of Information, Yunnan Normal University, Kunming, Yunnan China
| | - Ting Wang
- grid.410739.80000 0001 0723 6903School of Information, Yunnan Normal University, Kunming, Yunnan China
| | - Xi Deng
- grid.410739.80000 0001 0723 6903School of Information, Yunnan Normal University, Kunming, Yunnan China
| | - Lin Tang
- grid.410739.80000 0001 0723 6903Key Laboratory of Educational Information for Nationalities Ministry of Education, Yunnan Normal University, Kunming, Yunnan China
| | - Lin Liu
- grid.410739.80000 0001 0723 6903School of Information, Yunnan Normal University, Kunming, Yunnan China
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3
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Shomali A, Vafaei Sadi MS, Bakhtiarizadeh MR, Aliniaeifard S, Trewavas A, Calvo P. Identification of intelligence-related proteins through a robust two-layer predictor. Commun Integr Biol 2022; 15:253-264. [DOI: 10.1080/19420889.2022.2143101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Aida Shomali
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | | | - Sasan Aliniaeifard
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Anthony Trewavas
- School of Biological Sciences, Institute of Molecular Plant Science, University of Edinburgh, UK
| | - Paco Calvo
- Minimal Intelligence Lab, University of Murcia, Spain
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4
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Hosen MF, Mahmud SH, Ahmed K, Chen W, Moni MA, Deng HW, Shoombuatong W, Hasan MM. DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins. Comput Biol Med 2022; 145:105433. [DOI: 10.1016/j.compbiomed.2022.105433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/11/2022] [Accepted: 03/20/2022] [Indexed: 11/03/2022]
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5
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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.
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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
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Meher PK, Rai A, Rao AR. mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net. BMC Bioinformatics 2021; 22:342. [PMID: 34167457 PMCID: PMC8223360 DOI: 10.1186/s12859-021-04264-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 06/11/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. RESULTS The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1-6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. CONCLUSIONS This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server "mLoc-mRNA" is accessible at http://cabgrid.res.in:8080/mlocmrna/ . The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.
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Affiliation(s)
- Prabina Kumar Meher
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
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7
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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.
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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
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8
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Mahmud SMH, Chen W, Liu Y, Awal MA, Ahmed K, Rahman MH, Moni MA. PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques. Brief Bioinform 2021; 22:6168499. [PMID: 33709119 PMCID: PMC7989622 DOI: 10.1093/bib/bbab046] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.
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Affiliation(s)
- S M Hasan Mahmud
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenyu Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yongsheng Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Md Abdul Awal
- Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Kawsar Ahmed
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia-7003, Bangladesh
| | - Mohammad Ali Moni
- UNSW Digital Health, WHO Center for eHealth, School of Public Health and Community Medicine, Faculty of Medicine, The University of New South Wales, Sydney, Australia
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9
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Ao C, Jin S, Ding H, Zou Q, Yu L. Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis. Curr Pharm Des 2021; 26:3069-3075. [PMID: 32228416 DOI: 10.2174/1381612826666200331091156] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
Abstract
With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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10
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Abstract
The elucidation of the subcellular localization of proteins is very important in order to deeply understand their functions. In fact, proteins activities are strictly correlated to the cellular compartment and microenvironment in which they are present.In recent years, several effective and reliable proteomics techniques and computational methods have been developed and implemented in order to identify the proteins subcellular localization. This process is often time-consuming and expensive, but the recent technological and bioinformatics progress allowed the development of more accurate and simple workflows to determine the localization, interactions, and functions of proteins.In the following chapter, a brief introduction on the importance of knowing subcellular localization of proteins will be presented. Then, sample preparation protocols, proteomic methods, data analysis strategies, and software for the prediction of proteins localization will be presented and discussed. Finally, the more recent and advanced spatial proteomics techniques will be shown.
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Affiliation(s)
- Elettra Barberis
- Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Diseases, CAAD, University of Piemonte Orientale, Novara, Italy
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy
- Center for Translational Research on Autoimmune and Allergic Diseases, CAAD, University of Piemonte Orientale, Novara, Italy
| | - Marcello Manfredi
- Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.
- Center for Translational Research on Autoimmune and Allergic Diseases, CAAD, University of Piemonte Orientale, Novara, Italy.
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11
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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: 13] [Impact Index Per Article: 3.3] [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.
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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
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12
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Yang XF, Zhou YK, Zhang L, Gao Y, Du PF. Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190902151038] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background:
Long non-coding RNAs (lncRNAs) are transcripts with a length more
than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown
that the biological functions of lncRNAs are intimately related to their subcellular localizations.
Therefore, it is very important to confirm the lncRNA subcellular localization.
Methods:
In this paper, we proposed a novel method to predict the subcellular localization of
lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer
nucleotide composition and sequence order correlated factors of lncRNA to formulate
lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of
Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support
vector machine (SVM) to perform the prediction.
Results:
The AUC value of the proposed method can reach 0.9695, which indicated the proposed
predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore,
the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross
validation, which clearly outperforms the existing state-of- the-art method.
Conclusion:
It is demonstrated that the proposed predictor is feasible and powerful for the prediction
of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the
source code at https://github.com/NicoleYXF/lncRNA.
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Affiliation(s)
- Xiao-Fei Yang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yuan-Ke Zhou
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Lin Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yang Gao
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
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Zhang S, Qiao H. KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization. Anal Biochem 2020; 610:113995. [PMID: 33080214 DOI: 10.1016/j.ab.2020.113995] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/07/2020] [Accepted: 10/12/2020] [Indexed: 12/18/2022]
Abstract
Long non-coding RNAs (lncRNAs) refer to functional RNA molecules with a length more than 200 nucleotides and have minimal or no function to encode proteins. In recent years, more studies show that lncRNAs subcellular localization has valuable clues for their biological functions. So it is count for much to identify lncRNAs subcellular localization. In this paper, a novel statistical model named KD-KLNMF is constructed to predict lncRNAs subcellular localization. Firstly, k-mer and dinucleotide-based spatial autocorrelation are incorporated as the feature vector. Then, Synthetic Minority Over-sampling Technique is used to deal with the imbalance dataset. Next, Kullback-Leibler divergence-based nonnegative matrix factorization is applied to select optimal features. And then we utilize support vector machine as the classifier after comparing with other classifiers. Finally, the jackknife test is performed to evaluate the model. The overall accuracies reach 97.24% and 92.86% on training dataset and independent dataset, respectively. The results are better than the previous methods, which indicate that our model will be a useful and feasible tool to identify lncRNAs subcellular localization. The datasets and source code are freely available at https://github.com/HuijuanQiao/KD-KLNMF.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Huijuan Qiao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion. Int J Mol Sci 2020; 21:ijms21197271. [PMID: 33019721 PMCID: PMC7582431 DOI: 10.3390/ijms21197271] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/27/2020] [Accepted: 09/28/2020] [Indexed: 12/13/2022] Open
Abstract
Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lack of experimental data, the number of lncRNAs is very limited, experimentally verified subcellular localization, and the numbers of lncRNAs located in different organelle are wildly imbalanced. The prediction of subcellular location of lncRNAs is actually a multi-classification small sample imbalance problem. The imbalance of data results in the poor recognition effect of machine learning models on small data subsets, which is a puzzling and challenging problem in the existing research. In this study, we integrate multi-source features to construct a sequence-based computational tool, lncLocation, to predict the subcellular location of lncRNAs. Autoencoder is used to enhance part of the features, and the binomial distribution-based filtering method and recursive feature elimination (RFE) are used to filter some of the features. It improves the representation ability of data and reduces the problem of unbalanced multi-classification data. By comprehensive experiments on different feature combinations and machine learning models, we select the optimal features and classifier model scheme to construct a subcellular location prediction tool, lncLocation. LncLocation can obtain an 87.78% accuracy using 5-fold cross validation on the benchmark data, which is higher than the state-of-the-art tools, and the classification performance, especially for small class sets, is improved significantly.
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15
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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.
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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
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16
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miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides. Sci Rep 2020; 10:14557. [PMID: 32884018 PMCID: PMC7471944 DOI: 10.1038/s41598-020-71381-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 07/07/2020] [Indexed: 12/20/2022] Open
Abstract
MicroRNAs (miRNAs) are one kind of non-coding RNA, play vital role in regulating several physiological and developmental processes. Subcellular localization of miRNAs and their abundance in the native cell are central for maintaining physiological homeostasis. Besides, RNA silencing activity of miRNAs is also influenced by their localization and stability. Thus, development of computational method for subcellular localization prediction of miRNAs is desired. In this work, we have proposed a computational method for predicting subcellular localizations of miRNAs based on principal component scores of thermodynamic, structural properties and pseudo compositions of di-nucleotides. Prediction accuracy was analyzed following fivefold cross validation, where ~ 63–71% of AUC-ROC and ~ 69–76% of AUC-PR were observed. While evaluated with independent test set, > 50% localizations were found to be correctly predicted. Besides, the developed computational model achieved higher accuracy than the existing methods. A user-friendly prediction server “miRNALoc” is freely accessible at https://cabgrid.res.in:8080/mirnaloc/, by which the user can predict localizations of miRNAs.
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17
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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18
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Li H, Du H, Wang X, Gao P, Liu Y, Lin W. Remarks on Computational Method for Identifying Acid and Alkaline Enzymes. Curr Pharm Des 2020; 26:3105-3114. [PMID: 32552636 DOI: 10.2174/1381612826666200617170826] [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: 01/01/2020] [Accepted: 05/07/2020] [Indexed: 11/22/2022]
Abstract
The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.
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Affiliation(s)
- Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Haoze Du
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, 27109, United States
| | - Xianfang Wang
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Weizhong Lin
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, United States
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19
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Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H. A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods. Curr Drug Targets 2020; 20:540-550. [PMID: 30277150 DOI: 10.2174/1389450119666181002143355] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/17/2018] [Accepted: 09/04/2018] [Indexed: 12/13/2022]
Abstract
Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.
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Affiliation(s)
- Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- 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
| | - Fu-Ying Dao
- 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
| | - Wei Chen
- 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.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.,Gordon Life Science Institute, Boston, MA 02478, United States
| | - 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
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20
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Shahbaaz M, Potemkin V, Bisetty K, Hassan MI, Hussien MA. Classification and functional analyses of putative virulence factors of Mycobacterium tuberculosis: A combined sequence and structure based study. Comput Biol Chem 2020; 87:107270. [PMID: 32438116 DOI: 10.1016/j.compbiolchem.2020.107270] [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: 09/06/2019] [Revised: 04/16/2020] [Accepted: 04/26/2020] [Indexed: 11/17/2022]
Abstract
The emergence of the drug-resistant mechanisms in Mycobacterium tuberculosis poses the biggest challenges to the current therapeutic measures, which necessitates the identification of new drug targets. The Hypothetical Proteins (HPs), a class of functionally uncharacterized proteins, may provide a new class of undiscovered therapeutic targets. The genome of M. tuberculosis contains 1000 HPs with their sequences were analyzed using a variety of bioinformatics tools and the functional annotations were performed. The functions of 662 HPs were successfully predicted and further classified 483 HPs as enzymes, 141 HPs were predicted to be involved in the diverse cellular mechanisms and 38 HPs may function as transporters and carriers proteins. Furthermore, 28 HPs were predicted to be virulent in nature. Amongst them, the HP P95201, HP P9WM79, HP I6WZ30, HP I6 × 9T8, HP P9WKP3, and HP P9WK89 showed the highest virulence scores. Therefore, these proteins were subjected to extensive structure analyses and dynamics of their conformations were investigated using the principles of molecular dynamics simulations, each for a 150 ns time scale. This study provides a deeper understanding of the undiscovered drug targets and the generated outputs will facilitate the process of drug design and discovery against the infection of M. tuberculosis.
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Affiliation(s)
- Mohd Shahbaaz
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute (SANBI), University of the Western Cape, Private Bag X17, Bellville 7535, Cape Town, South Africa; Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080 Chelyabinsk, Russia
| | - Vladimir Potemkin
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080 Chelyabinsk, Russia
| | - Krishna Bisetty
- Department of Chemistry, Durban University of Technology, Durban, 4000, South Africa
| | - Md Imtaiyaz Hassan
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, 110025, India
| | - Mostafa A Hussien
- Department of Chemistry, Faculty of Science, King Abdulaziz University, P.O. Box 80203 Jeddah 21589, Saudi Arabia; Department of Chemistry, Faculty of Science, Port Said University, Port Said, 42521, Egypt
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21
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Liu T, Tang H. A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite. Curr Pharm Des 2020; 26:3049-3058. [PMID: 32156226 DOI: 10.2174/1381612826666200310122324] [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: 01/01/2020] [Accepted: 02/10/2020] [Indexed: 11/22/2022]
Abstract
The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.
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Affiliation(s)
- Ting Liu
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou 646000, China
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22
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Ahmad A, Lin H, Shatabda S. Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions. Genomics 2020; 112:2583-2589. [PMID: 32068122 DOI: 10.1016/j.ygeno.2020.02.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 11/11/2019] [Accepted: 02/12/2020] [Indexed: 12/12/2022]
Abstract
Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.
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Affiliation(s)
- Ahsan Ahmad
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka 1212, Bangladesh.
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23
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Song X, Zhuang Y, Lan Y, Lin Y, Min X. Comprehensive Review and Comparison for Anticancer Peptides Identification Models. Curr Protein Pept Sci 2020; 22:CPPS-EPUB-103745. [PMID: 31957608 DOI: 10.2174/1389203721666200117162958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 05/16/2019] [Accepted: 05/30/2019] [Indexed: 11/22/2022]
Abstract
Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, we provide a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors. To evaluate current prediction tools, we conducted a comparative study and analyzed the existing ACPs predictor from 10 public literatures. The comparative results obtained suggest that Support Vector Machine-based model with features combination provided significant improvement in the overall performance, when compared to the other machine learning method-based prediction models.
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24
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Sun S, Wang C, Ding H, Zou Q. Machine learning and its applications in plant molecular studies. Brief Funct Genomics 2019; 19:40-48. [DOI: 10.1093/bfgp/elz036] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/06/2019] [Accepted: 09/15/2019] [Indexed: 01/16/2023] Open
Abstract
Abstract
The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.
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Affiliation(s)
- Shanwen Sun
- University of Bayreuth in Germany. He is now a postdoctoral fellow at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
| | - Chunyu Wang
- Harbin Institute of Technology in China. He is an associate professor in the School of Computer Science and Technology, Harbin Institute of Technology
| | - Hui Ding
- Inner Mongolia University in China. She is an associate professor in the Center for Informational Biology, University of Electronic Science and Technology of China
| | - Quan Zou
- Harbin Institute of Technology in China. He is a professor in the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
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25
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Zhong W, Zhong B, Zhang H, Chen Z, Chen Y. Identification of Anti-cancer Peptides Based on Multi-classifier System. Comb Chem High Throughput Screen 2019; 22:694-704. [PMID: 31793417 DOI: 10.2174/1386207322666191203141102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/18/2019] [Accepted: 07/30/2019] [Indexed: 01/01/2023]
Abstract
AIMS AND OBJECTIVE Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice. MATERIALS AND METHODS In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. RESULTS AND CONCLUSION The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.
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Affiliation(s)
- Wanben Zhong
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Bineng Zhong
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China.,Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Hongbo Zhang
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Ziyi Chen
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Yan Chen
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
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26
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Wang F, Guan ZX, Dao FY, Ding H. A Brief Review of the Computational Identification of Antifreeze Protein. CURR ORG CHEM 2019. [DOI: 10.2174/1385272823666190718145613] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi. These AFPs from fish, insects and plants displayed a high diversity. Thus, the identification of the AFPs is a challenging task in computational proteomics. With the accumulation of AFPs and development of machine meaning methods, it is possible to construct a high-throughput tool to timely identify the AFPs. In this review, we briefly reviewed the application of machine learning methods in antifreeze proteins identification from difference section, including published benchmark dataset, sequence descriptor, classification algorithms and published methods. We hope that this review will produce new ideas and directions for the researches in identifying antifreeze proteins.
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Affiliation(s)
- 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
| | - 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
| | - Fu-Ying Dao
- 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
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27
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Su ZD, Huang Y, Zhang ZY, Zhao YW, Wang D, Chen W, Chou KC, Lin H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 2019; 34:4196-4204. [PMID: 29931187 DOI: 10.1093/bioinformatics/bty508] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/19/2018] [Indexed: 12/20/2022] Open
Abstract
Motivation Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. Results We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests. Availability and implementation A user-friendly webserver has been established at http://lin-group.cn/server/iLoc-LncRNA, by which users can easily obtain their desired results. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhen-Dong Su
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhao-Yue 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, China
| | - Ya-Wei Zhao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dong 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, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Boston, MA, USA
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28
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Yang H, Yang W, Dao FY, Lv H, Ding H, Chen W, Lin H. A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae. Brief Bioinform 2019; 21:1568-1580. [PMID: 31633777 DOI: 10.1093/bib/bbz123] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 05/03/2019] [Accepted: 08/19/2019] [Indexed: 12/27/2022] Open
Abstract
Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.
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Affiliation(s)
- Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wuritu Yang
- Development and Planning Department, Inner Mongolia University, Hohhot 010021, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- 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
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Hao Lin
- Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China
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Li SH, Guan ZX, Zhang D, Zhang ZM, Huang J, Yang W, Lin H. Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods. Med Chem 2019; 16:605-619. [PMID: 31584379 DOI: 10.2174/1573406415666191004101913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 06/25/2019] [Accepted: 08/23/2019] [Indexed: 01/28/2023]
Abstract
Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.
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Affiliation(s)
- Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - 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, 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, China
| | - 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, China
| | - Jian Huang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Development and Planning Department, Inner Mongolia University, Hohhot, P.R. China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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30
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Sharma D, Sharma A, Singh B, Verma SK. Bioinformatic Exploration of Metal-Binding Proteome of Zoonotic Pathogen Orientia tsutsugamushi. Front Genet 2019; 10:797. [PMID: 31608099 PMCID: PMC6769048 DOI: 10.3389/fgene.2019.00797] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 07/30/2019] [Indexed: 12/21/2022] Open
Abstract
Metal ions are involved in many essential biological processes and are crucial for the survival of all organisms. Identification of metal-binding proteins (MBPs) of human affecting pathogens may provide the blueprint for understanding biological metal usage and their putative roles in pathogenesis. This study is focused on the analysis of MBPs from Orientia tsutsugamushi (Ott), a causal agent of scrub typhus in humans. A total of 321 proteins were predicted as putative MBPs, based on sequence search and three-dimensional structure analysis. Majority of proteins could bind with magnesium, and the order of metal binding was Mg > Ca > Zn > Mn > Fe > Cd > Ni > Co > Cu, respectively. The predicted MBPs were functionally classified into nine broad classes. Among them, gene expression and regulation, metabolism, cell signaling, and transport classes were dominant. It was noted that the putative MBPs were localized in all subcellular compartments of Ott, but majorly found in the cytoplasm. Additionally, it was revealed that out of 321 predicted MBPs 245 proteins were putative bacterial toxins and among them, 98 proteins were nonhomologous to human proteome. Sixty putative MBPs showed the ability to interact with drug or drug-like molecules, which indicate that they may be used as broad-spectrum drug targets. These predicted MBPs from Ott could play vital role(s) in various cellular activities and virulence, hence may serve as plausible therapeutic targets to design metal-based drugs to curtail its infection.
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Affiliation(s)
- Dixit Sharma
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Kangra, India
| | - Ankita Sharma
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Kangra, India
| | - Birbal Singh
- ICAR-Indian Veterinary Research Institute, Regional Station, Palampur, India
| | - Shailender Kumar Verma
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Kangra, India
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31
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Meng C, Jin S, Wang L, Guo F, Zou Q. AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine. Front Bioeng Biotechnol 2019; 7:224. [PMID: 31620433 PMCID: PMC6759716 DOI: 10.3389/fbioe.2019.00224] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 09/03/2019] [Indexed: 01/03/2023] Open
Abstract
Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,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
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32
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Chen W, Feng P, Song X, Lv H, Lin H. iRNA-m7G: Identifying N 7-methylguanosine Sites by Fusing Multiple Features. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:269-274. [PMID: 31581051 PMCID: PMC6796804 DOI: 10.1016/j.omtn.2019.08.022] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/07/2019] [Accepted: 08/19/2019] [Indexed: 11/18/2022]
Abstract
As an essential post-transcriptional modification, N7-methylguanosine (m7G) regulates nearly every step of the life cycle of mRNA. Accurate identification of the m7G site in the transcriptome will provide insights into its biological functions and mechanisms. Although the m7G-methylated RNA immunoprecipitation sequencing (MeRIP-seq) method has been proposed in this regard, it is still cost-ineffective for detecting the m7G site. Therefore, it is urgent to develop new methods to identify the m7G site. In this work, we developed the first computational predictor called iRNA-m7G to identify m7G sites in the human transcriptome. The feature fusion strategy was used to integrate both sequence- and structure-based features. In the jackknife test, iRNA-m7G obtained an accuracy of 89.88%. The superiority of iRNA-m7G for identifying m7G sites was also demonstrated by comparing with other methods. We hope that iRNA-m7G can become a useful tool to identify m7G sites. A user-friendly web server for iRNA-m7G is freely accessible at http://lin-group.cn/server/iRNA-m7G/.
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Affiliation(s)
- Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China; Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China.
| | - Pengmian Feng
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Xiaoming Song
- Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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33
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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34
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Meng C, Wei L, Zou Q. SecProMTB: Support Vector Machine‐Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied toMycobacterium tuberculosis. Proteomics 2019; 19:e1900007. [DOI: 10.1002/pmic.201900007] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/25/2019] [Indexed: 11/08/2022]
Affiliation(s)
- Chaolu Meng
- College of Intelligence and ComputingTianjin University 300350 Tianjin China
- College of Computer and Information EngineeringInner Mongolia Agricultural University 010018 Hohhot China
| | - Leyi Wei
- College of Intelligence and ComputingTianjin University 300350 Tianjin China
| | - Quan Zou
- College of Intelligence and ComputingTianjin University 300350 Tianjin China
- Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of China 610054 Chengdu China
- Center for Informational BiologyUniversity of Electronic Science and Technology of China 610054 Chengdu China
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35
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Yao Y, Li M, Xu H, Yan S, He P, Dai Q, Qi Z, Liao B. Protein Subcellular Localization Prediction based on PSI-BLAST Profile and Principal Component Analysis. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164616666190126155744] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Prediction of protein subcellular location is a meaningful task which attracts
much attention in recent years. Particularly, the number of new protein sequences yielded by the highthroughput
sequencing technology in the post genomic era has increased explosively.
Objective:
Protein subcellular localization prediction based solely on sequence data remains to be a
challenging problem of computational biology.
Methods:
In this paper, three sets of evolutionary features are derived from the position-specific scoring
matrix, which has shown great potential in other bioinformatics problems. A fusion model is built
up by the optimal parameters combination. Finally, principal component analysis and support vector
machine classifier is applied to predict protein subcellular localization on NNPSL dataset and Cell-
PLoc 2.0 dataset.
Results:
Our experimental results show that the proposed method remarkably improved the prediction
accuracy, and the features derived from PSI-BLAST profile only are appropriate for protein subcellular
localization prediction.
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Affiliation(s)
- Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Manzhi Li
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Huimin Xu
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Shoujiang Yan
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Pingan He
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Qi Dai
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Zhaohui Qi
- College of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
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36
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He B, Chen H, Huang J. PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition. PeerJ 2019; 7:e7131. [PMID: 31245183 PMCID: PMC6585900 DOI: 10.7717/peerj.7131] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 05/15/2019] [Indexed: 01/08/2023] Open
Abstract
Selection from phage display libraries empowers isolation of high-affinity ligands for various targets. However, this method also identifies propagation-related target-unrelated peptides (PrTUPs). These false positive hits appear because of their amplification advantages. In this report, we present PhD7Faster 2.0 for predicting fast-propagating clones from the Ph.D.-7 phage display library, which was developed based on the support vector machine. Feature selection was performed against PseAAC and tripeptide composition using the incremental feature selection method. Ten-fold cross-validation results show that PhD7Faster 2.0 succeeds a decent performance with the accuracy of 81.84%, the Matthews correlation coefficient of 0.64 and the area under the ROC curve of 0.90. The permutation test with 1,000 shuffles resulted in p < 0.001. We implemented PhD7Faster 2.0 into a publicly accessible web tool (http://i.uestc.edu.cn/sarotup3/cgi-bin/PhD7Faster.pl) and constructed standalone graphical user interface and command-line versions for different systems. The standalone PhD7Faster 2.0 is able to detect PrTUPs within small datasets as well as large-scale datasets. This makes PhD7Faster 2.0 an enhanced and powerful tool for scanning and reporting faster-growing clones from the Ph.D.-7 phage display library.
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Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang, Guizhou, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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37
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Lin Y, Cai Y, Liu J, Lin C, Liu X. An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies. BMC Bioinformatics 2019; 20:291. [PMID: 31182007 PMCID: PMC6557738 DOI: 10.1186/s12859-019-2766-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AMPs and their derivatives can greatly improve the immunity and breeding survival rate of aquatic products. It is highly desirable to develop computational tools for rapidly and accurately identifying AMPs and their functional types, for the purpose of helping design new and more effective antimicrobial agents. RESULTS In this study, we made an attempt to develop an advanced machine learning based computational approach, MAMPs-Pred, for identification of AMPs and its function types. Initially, SVM-prot 188-D features were extracted that were subsequently used as input to a two-layer multi-label classifier. In specific, the first layer is to identify whether it is an AMP by applying RF classifier, and the second layer addresses the multi-type problem by identifying the activites or function types of AMPs by applying PS-RF and LC-RF classifiers. To benchmark the methods,the MAMPs-Pred method is also compared with existing best-performing methods in literature and has shown an improved identification accuracy. CONCLUSIONS The results reported in this study indicate that the MAMP-Pred method achieves high performance for identifying AMPs and its functional types.The proposed approach is believed to supplement the tools and techniques that have been developed in the past for predicting AMPs and their function types.
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Affiliation(s)
- Yuan Lin
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005 China
- Sparebanken Vest, Jonsvollsgaten 2, 5011 Bergen, Bergen, 5058 Norway
| | - Yinyin Cai
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005 China
| | - Juan Liu
- Department of Instrumental and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361005 China
| | - Chen Lin
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005 China
| | - Xiangrong Liu
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005 China
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38
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Wang X, Li H, Gao P, Liu Y, Zeng W. Combining Support Vector Machine with Dual g-gap Dipeptides to Discriminate between Acidic and Alkaline Enzymes. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180925125912] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The catalytic activity of the enzyme is different from that of the inorganic catalyst. In a high-temperature, over-acid or over-alkaline environment, the structure of the enzyme is destroyed and then loses its activity. Although the biochemistry experiments can measure the optimal PH environment of the enzyme, these methods are inefficient and costly. In order to solve these problems, computational model could be established to determine the optimal acidic or alkaline environment of the enzyme. Firstly, in this paper, we introduced a new feature called dual g-gap dipeptide composition to formulate enzyme samples. Subsequently, the best feature was selected by using the F value calculated from analysis of variance. Finally, support vector machine was utilized to build prediction model for distinguishing acidic from alkaline enzyme. The overall accuracy of 95.9% was achieved with Jackknife cross-validation, which indicates that our method is professional and efficient in terms of acid and alkaline enzyme predictions. The feature proposed in this paper could also be applied in other fields of bioinformatics.
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Affiliation(s)
- Xianfang Wang
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Wenjing Zeng
- TianJiabing Middle School of Chengdu, Chengdu 610011, China
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Abstract
Background:DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.Objective:Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.Method:The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.Results:Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.Conclusion:Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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40
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Yang W, Zhu XJ, Huang J, Ding H, Lin H. A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization. Curr Bioinform 2019. [DOI: 10.2174/1574893613666181113131415] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background:The location of proteins in a cell can provide important clues to their functions in various biological processes. Thus, the application of machine learning method in the prediction of protein subcellular localization has become a hotspot in bioinformatics. As one of key organelles, the Golgi apparatus is in charge of protein storage, package, and distribution.Objective:The identification of protein location in Golgi apparatus will provide in-depth insights into their functions. Thus, the machine learning-based method of predicting protein location in Golgi apparatus has been extensively explored. The development of protein sub-Golgi apparatus localization prediction should be reviewed for providing a whole background for the fields.Method:The benchmark dataset, feature extraction, machine learning method and published results were summarized.Results:We briefly introduced the recent progresses in protein sub-Golgi apparatus localization prediction using machine learning methods and discussed their advantages and disadvantages.Conclusion:We pointed out the perspective of machine learning methods in protein sub-Golgi localization prediction.
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Affiliation(s)
- Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Xiao-Juan Zhu
- 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, Sichuan, 610054, China
| | - Jian Huang
- 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, Sichuan, 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, Sichuan, 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
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41
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Kabir M, Ahmad S, Iqbal M, Hayat M. iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics 2019; 112:276-285. [PMID: 30779939 DOI: 10.1016/j.ygeno.2019.02.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/09/2019] [Accepted: 02/07/2019] [Indexed: 12/25/2022]
Abstract
Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.
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Affiliation(s)
- Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
| | - Saeed Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
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42
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Qu K, Wei L, Yu J, Wang C. Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods. FRONTIERS IN PLANT SCIENCE 2019; 9:1961. [PMID: 30687359 PMCID: PMC6335366 DOI: 10.3389/fpls.2018.01961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/17/2018] [Indexed: 05/04/2023]
Abstract
Motivation: Pentatricopeptide repeat (PPR) is a triangular pentapeptide repeat domain that plays a vital role in plant growth. In this study, we seek to identify PPR coding genes and proteins using a mixture of feature extraction methods. We use four single feature extraction methods focusing on the sequence, physical, and chemical properties as well as the amino acid composition, and mix the features. The Max-Relevant-Max-Distance (MRMD) technique is applied to reduce the feature dimension. Classification uses the random forest, J48, and naïve Bayes with 10-fold cross-validation. Results: Combining two of the feature extraction methods with the random forest classifier produces the highest area under the curve of 0.9848. Using MRMD to reduce the dimension improves this metric for J48 and naïve Bayes, but has little effect on the random forest results. Availability and Implementation: The webserver is available at: http://server.malab.cn/MixedPPR/index.jsp.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jiantao Yu
- College of Information Engineering, North-West A&F University, Yangling, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
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43
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Zhu XJ, Feng CQ, Lai HY, Chen W, Hao L. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.10.007] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Dao FY, Lv H, Wang F, Feng CQ, Ding H, Chen W, Lin H. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics 2018; 35:2075-2083. [DOI: 10.1093/bioinformatics/bty943] [Citation(s) in RCA: 147] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/06/2018] [Accepted: 11/13/2018] [Indexed: 02/07/2023] Open
Affiliation(s)
- Fu-Ying Dao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lv
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fang Wang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao-Qin Feng
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Ding
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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45
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Ariaeenejad S, Mousivand M, Moradi Dezfouli P, Hashemi M, Kavousi K, Hosseini Salekdeh G. A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features. PLoS One 2018; 13:e0205796. [PMID: 30346964 PMCID: PMC6197662 DOI: 10.1371/journal.pone.0205796] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 10/02/2018] [Indexed: 01/09/2023] Open
Abstract
Xylanases are hydrolytic enzymes which based on physicochemical properties, structure, mode of action and substrate specificities are classified into various glycoside hydrolase (GH) families. The purpose of this study is to show that the activity of the members of the xylanase family in the specified pH and temperature conditions can be computationally predicted. The proposed computational regression model was trained and tested with the Pseudo Amino Acid Composition (PseAAC) features extracted solely from the amino acid sequences of enzymes. The xylanases with experimentally determined activities were used as the training dataset to adjust the model parameters. To develop the model, 41 strains of Bacillus subtilis isolated from field soil were screened. From them, 28 strains with the highest halo diameter were selected for further studies. The performance of the model for prediction of xylanase activity was evaluated in three different temperature and pH conditions using stratified cross-validation and jackknife methods. The trained model can be used for determining the activity of newly found xylanases in the specified condition. Such computational models help to scale down the experimental costs and save time by identifying enzymes with appropriate activity for scientific and industrial usage. Our methodology for activity prediction of xylanase enzymes can be potentially applied to the members of the other enzyme families. The availability of sufficient experimental data in specified pH and temperature conditions is a prerequisite for training the learning model and to achieve high accuracy.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Maryam Mousivand
- Department of Microbial Biotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Parinaz Moradi Dezfouli
- Department of Microbial Biotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Maryam Hashemi
- Department of Microbial Biotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
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46
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Shao L, Gao H, Liu Z, Feng J, Tang L, Lin H. Identification of Antioxidant Proteins With Deep Learning From Sequence Information. Front Pharmacol 2018; 9:1036. [PMID: 30294271 PMCID: PMC6158654 DOI: 10.3389/fphar.2018.01036] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Accepted: 08/27/2018] [Indexed: 01/26/2023] Open
Abstract
Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F 1 score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc.edu.cn/IDAod/.
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Affiliation(s)
- Lifen Shao
- 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
| | - Zhen Liu
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Juan Feng
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lixia Tang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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47
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Yang H, Lv H, Ding H, Chen W, Lin H. iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens. J Comput Biol 2018; 25:1266-1277. [PMID: 30113871 DOI: 10.1089/cmb.2018.0004] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.
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Affiliation(s)
- Hui Yang
- 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China
| | - Hao Lv
- 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China
| | - Hui Ding
- 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China
| | - Wei Chen
- 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China .,2 Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology , Tangshan, China
| | - Hao Lin
- 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China
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48
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Tan JX, Dao FY, Lv H, Feng PM, Ding H. Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods. Molecules 2018; 23:molecules23082000. [PMID: 30103458 PMCID: PMC6222849 DOI: 10.3390/molecules23082000] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 07/30/2018] [Accepted: 08/08/2018] [Indexed: 12/31/2022] Open
Abstract
Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.
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Affiliation(s)
- Jiu-Xin Tan
- 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.
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Hao Lv
- 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.
| | - Peng-Mian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China.
| | - Hui 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.
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49
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Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition. J Theor Biol 2018; 450:86-103. [DOI: 10.1016/j.jtbi.2018.04.026] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/10/2018] [Accepted: 04/16/2018] [Indexed: 01/16/2023]
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50
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Bakhtiarizadeh MR, Rahimi M, Mohammadi-Sangcheshmeh A, Shariati J V, Salami SA. PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach. Sci Rep 2018; 8:9025. [PMID: 29899414 PMCID: PMC5998058 DOI: 10.1038/s41598-018-27338-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/25/2018] [Indexed: 11/08/2022] Open
Abstract
Successful spermatogenesis and oogenesis are the two genetically independent processes preceding embryo development. To date, several fertility-related proteins have been described in mammalian species. Nevertheless, further studies are required to discover more proteins associated with the development of germ cells and embryogenesis in order to shed more light on the processes. This work builds on our previous software (OOgenesis_Pred), mainly focusing on algorithms beyond what was previously done, in particular new fertility-related proteins and their classes (embryogenesis, spermatogenesis and oogenesis) based on the support vector machine according to the concept of Chou's pseudo-amino acid composition features. The results of five-fold cross validation, as well as the independent test demonstrated that this method is capable of predicting the fertility-related proteins and their classes with accuracy of more than 80%. Moreover, by using feature selection methods, important properties of fertility-related proteins were identified that allowed for their accurate classification. Based on the proposed method, a two-layer classifier software, named as "PrESOgenesis" ( https://github.com/mrb20045/PrESOgenesis ) was developed. The tool identified a query sequence (protein or transcript) as fertility or non-fertility-related protein at the first layer and then classified the predicted fertility-related protein into different classes of embryogenesis, spermatogenesis or oogenesis at the second layer.
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Affiliation(s)
| | - Maryam Rahimi
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | - Vahid Shariati J
- Genome Center, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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