51
|
Peng J, Wang Y, Guan J, Li J, Han R, Hao J, Wei Z, Shang X. An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction. Brief Bioinform 2021; 22:6124914. [PMID: 33517357 DOI: 10.1093/bib/bbaa430] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/01/2020] [Accepted: 12/23/2020] [Indexed: 12/28/2022] Open
Abstract
Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI identification is still timeconsuming and expensive. Therefore, it is of great significance to develop effective computational methods for identifying DTIs. In this paper, we develop a novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end-to-end graph (EEG)-DTI. Given a heterogeneous network containing multiple types of biological entities (i.e. drug, protein, disease, side-effect), EEG-DTI learns the low-dimensional feature representation of drugs and targets using a graph convolutional networks-based model and predicts DTIs based on the learned features. During the training process, EEG-DTI learns the feature representation of nodes in an end-to-end mode. The evaluation test shows that EEG-DTI performs better than existing state-of-art methods. The data and source code are available at: https://github.com/MedicineBiology-AI/EEG-DTI.
Collapse
Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yuxian Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Jingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Ruijiang Han
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jianye Hao
- College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
| | - Zhongyu Wei
- School of Data Science, Fudan University, Shanghai 200433, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| |
Collapse
|
52
|
Yu L, Jing R, Liu F, Luo J, Li Y. DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm. MOLECULAR THERAPY-NUCLEIC ACIDS 2020; 22:862-870. [PMID: 33230481 PMCID: PMC7658571 DOI: 10.1016/j.omtn.2020.10.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/06/2020] [Indexed: 12/24/2022]
Abstract
Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.
Collapse
Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China
- Corresponding author: Lezheng Yu, School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China.
| | - Runyu Jing
- College of Cybersecurity, Sichuan University, Chengdu 610065, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, Sichuan, China
- Corresponding author: Jiesi Luo, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, Sichuan, China.
| | - Yizhou Li
- College of Cybersecurity, Sichuan University, Chengdu 610065, China
| |
Collapse
|
53
|
Chu Y, Shan X, Chen T, Jiang M, Wang Y, Wang Q, Salahub DR, Xiong Y, Wei DQ. DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Brief Bioinform 2020; 22:5910189. [PMID: 32964234 DOI: 10.1093/bib/bbaa205] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 12/20/2022] Open
Abstract
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods. The data sets and source code of this study are freely available at https://github.com/a96123155/DTI-MLCD.
Collapse
Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Xiaoqi Shan
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Tianhang Chen
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Mingming Jiang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| |
Collapse
|
54
|
Ji BY, You ZH, Jiang HJ, Guo ZH, Zheng K. Prediction of drug-target interactions from multi-molecular network based on LINE network representation method. J Transl Med 2020; 18:347. [PMID: 32894154 PMCID: PMC7487884 DOI: 10.1186/s12967-020-02490-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 08/20/2020] [Indexed: 12/28/2022] Open
Abstract
Background The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.
Collapse
Affiliation(s)
- Bo-Ya Ji
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Han-Jing Jiang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen-Hao Guo
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Zheng
- School of Computer Science and Engineering, Cen-tral South University, Changsha, 410083, China
| |
Collapse
|
55
|
Wang C, Wang W, Lu K, Zhang J, Chen P, Wang B. Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition. Int J Mol Sci 2020; 21:ijms21165694. [PMID: 32784497 PMCID: PMC7570185 DOI: 10.3390/ijms21165694] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 12/13/2022] Open
Abstract
The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.
Collapse
Affiliation(s)
- Cheng Wang
- Department of Computer Science & Technology, Tongji University, Shanghai 201804, China;
| | - Wenyan Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
- Key Laboratory of Power Electronics and Motion Control Anhui Education Department, Ma’anshan 243032, China
| | - Kun Lu
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
| | - Jun Zhang
- Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China;
| | - Peng Chen
- Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China;
- Correspondence: (P.C.); (B.W.)
| | - Bing Wang
- Department of Computer Science & Technology, Tongji University, Shanghai 201804, China;
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
- Key Laboratory of Power Electronics and Motion Control Anhui Education Department, Ma’anshan 243032, China
- Correspondence: (P.C.); (B.W.)
| |
Collapse
|
56
|
Hu Y, Zhou G, Zhang C, Zhang M, Chen Q, Zheng L, Niu B. Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach. Curr Alzheimer Res 2020; 16:193-208. [PMID: 30605059 DOI: 10.2174/1567205016666190103154855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 12/20/2018] [Accepted: 01/03/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer's drugs has become one of the most popular medical topics. METHODS In this study, in order to build a predicting model for Alzheimer's drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. RESULTS The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. CONCLUSION In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer's drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.
Collapse
Affiliation(s)
- Yan Hu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Guangya Zhou
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, China
| | - Mengying Zhang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Qin Chen
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Linfeng Zheng
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China.,Department of Radiology, Shanghai First People's Hospital, Baoshan Branch, Shanghai 200940, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| |
Collapse
|
57
|
Torkamanian-Afshar M, Lanjanian H, Nematzadeh S, Tabarzad M, Najafi A, Kiani F, Masoudi-Nejad A. RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions. Genomics 2020; 112:2623-2632. [DOI: 10.1016/j.ygeno.2020.02.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/04/2020] [Accepted: 02/13/2020] [Indexed: 12/12/2022]
|
58
|
D’Souza S, Prema K, Balaji S. Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discov Today 2020; 25:748-756. [DOI: 10.1016/j.drudis.2020.03.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/28/2020] [Accepted: 03/05/2020] [Indexed: 12/22/2022]
|
59
|
Bagherian M, Kim RB, Jiang C, Sartor MA, Derksen H, Najarian K. Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions. Brief Bioinform 2020; 22:2161-2171. [PMID: 32186716 PMCID: PMC7986629 DOI: 10.1093/bib/bbaa025] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/27/2020] [Accepted: 02/17/2020] [Indexed: 12/20/2022] Open
Abstract
Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug–target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed ‘Coupled Matrix–Matrix Completion’ (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug–drug similarities and target–target relationship, we then extend CMMC to ‘Coupled Tensor–Matrix Completion’ (CTMC) by considering drug–drug and target–target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$L_{2,1}$\end{document}-GRMF, NRLMF and NRLMF\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$\beta $\end{document}. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.
Collapse
Affiliation(s)
- Maryam Bagherian
- Corresponding author: Maryam Bagherian, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, USA.
| | | | | | | | | | | |
Collapse
|
60
|
Rayhan F, Ahmed S, Mousavian Z, Farid DM, Shatabda S. FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Heliyon 2020; 6:e03444. [PMID: 32154410 PMCID: PMC7052404 DOI: 10.1016/j.heliyon.2020.e03444] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 06/16/2019] [Accepted: 02/14/2020] [Indexed: 01/09/2023] Open
Abstract
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.
Collapse
Affiliation(s)
- Farshid Rayhan
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Sajid Ahmed
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Dewan Md Farid
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| |
Collapse
|
61
|
Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 172] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
Collapse
Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| |
Collapse
|
62
|
Chen ZH, You ZH, Li LP, Wang YB, Qiu Y, Hu PW. Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter. BMC Genomics 2019; 20:928. [PMID: 31881833 PMCID: PMC6933882 DOI: 10.1186/s12864-019-6301-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction. Results In this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches. Conclusion Experimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.
Collapse
Affiliation(s)
- Zhan-Heng Chen
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Li-Ping Li
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yan-Bin Wang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yu Qiu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | | |
Collapse
|
63
|
Shi Q, Chen W, Huang S, Wang Y, Xue Z. Deep learning for mining protein data. Brief Bioinform 2019; 22:194-218. [PMID: 31867611 DOI: 10.1093/bib/bbz156] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/21/2019] [Accepted: 11/07/2019] [Indexed: 01/16/2023] Open
Abstract
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
Collapse
Affiliation(s)
- Qiang Shi
- School of Software Engineering, Huazhong University of Science and Technology. His main interests cover machine learning especially deep learning, protein data analysis, and big data mining
| | - Weiya Chen
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, virtual reality, and data visualization
| | - Siqi Huang
- Software Engineering at Huazhong University of science and technology, focusing on Machine learning and data mining
| | - Yan Wang
- School of life, University of Science & Technology; her main interests cover protein structure and function prediction and big data mining
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, machine learning, and image processing
| |
Collapse
|
64
|
Wang R, Li S, Cheng L, Wong MH, Leung KS. Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning. BMC Bioinformatics 2019; 20:628. [PMID: 31839008 PMCID: PMC6912989 DOI: 10.1186/s12859-019-3283-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.
Collapse
Affiliation(s)
- Ran Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Shuai Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People’s Hospital, The Second Clinical Medicine College of Ji’nan University, Shenzhen, China
| | - Man Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Kwong Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
65
|
Li M, Lian S, Wang F, Zhou Y, Chen B, Guan L, Wu Y. Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm. Sci Rep 2019; 9:17261. [PMID: 31754116 PMCID: PMC6872818 DOI: 10.1038/s41598-019-53206-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 10/29/2019] [Indexed: 01/24/2023] Open
Abstract
As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value Ec of absorption energy (B3LYP/STO-3G), molecular electron number Ne, oscillator strength Os, number of double bonds Ndb, total number of atoms Na, number of hydrogen atoms Nh, number of carbon atoms Nc, and number of nitrogen atoms NN; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc.
Collapse
Affiliation(s)
- Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Suyun Lian
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Fan Wang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yanying Zhou
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Bingsheng Chen
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| |
Collapse
|
66
|
Jiang HJ, You ZH, Huang YA. Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks. J Transl Med 2019; 17:382. [PMID: 31747915 PMCID: PMC6868698 DOI: 10.1186/s12967-019-2127-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/05/2019] [Indexed: 12/02/2022] Open
Abstract
Background In the process of drug development, computational drug repositioning is effective and resource-saving with regards to its important functions on identifying new drug–disease associations. Recent years have witnessed a great progression in the field of data mining with the advent of deep learning. An increasing number of deep learning-based techniques have been proposed to develop computational tools in bioinformatics. Methods Along this promising direction, we here propose a drug repositioning computational method combining the techniques of Sigmoid Kernel and Convolutional Neural Network (SKCNN) which is able to learn new features effectively representing drug–disease associations via its hidden layers. Specifically, we first construct similarity metric of drugs using drug sigmoid similarity and drug structural similarity, and that of disease using disease sigmoid similarity and disease semantic similarity. Based on the combined similarities of drugs and diseases, we then use SKCNN to learn hidden representations for each drug-disease pair whose labels are finally predicted by a classifier based on random forest. Results A series of experiments were implemented for performance evaluation and their results show that the proposed SKCNN improves the prediction accuracy compared with other state-of-the-art approaches. Case studies of two selected disease are also conducted through which we prove the superior performance of our method in terms of the actual discovery of potential drug indications. Conclusion The aim of this study was to establish an effective predictive model for finding new drug–disease associations. These experimental results show that SKCNN can effectively predict the association between drugs and diseases.
Collapse
Affiliation(s)
- Han-Jing Jiang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China.
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, HungHom, Hong Kong.
| |
Collapse
|
67
|
Wang W, Li K, Lv H, Zhang H, Wang S, Huang J. SmoPSI: Analysis and Prediction of Small Molecule Binding Sites Based on Protein Sequence Information. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:1926156. [PMID: 31814842 PMCID: PMC6877956 DOI: 10.1155/2019/1926156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/16/2019] [Accepted: 09/26/2019] [Indexed: 11/20/2022]
Abstract
The analysis and prediction of small molecule binding sites is very important for drug discovery and drug design. The traditional experimental methods for detecting small molecule binding sites are usually expensive and time consuming, and the tools for single species small molecule research are equally inefficient. In recent years, some algorithms for predicting binding sites of protein-small molecules have been developed based on the geometric and sequence characteristics of proteins. In this paper, we have proposed SmoPSI, a classification model based on the XGBoost algorithm for predicting the binding sites of small molecules, using protein sequence information. The model achieved better results with an AUC of 0.918 and an ACC of 0.913. The experimental results demonstrate that our method achieves high performances and outperforms many existing predictors. In addition, we also analyzed the binding residues and nonbinding residues and finally found the PSSM; hydrophilicity, hydrophobicity, charge, and hydrogen bonding have obviously different effects on the binding-site predictions.
Collapse
Affiliation(s)
- Wei Wang
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, Henan Province, China
- Laboratory of Computation Intelligence and Information Processing, Engineering Technology Research Center for Computing Intelligence and Data Mining, 453007 Xinxiang, Henan Province, China
| | - Keliang Li
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, Henan Province, China
| | - Hehe Lv
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, Henan Province, China
| | - Hongjun Zhang
- School of Aviation Engineering, Anyang University, 455000 Anyang, Henan Province, China
| | - Shixun Wang
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, Henan Province, China
| | - Junwei Huang
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, Henan Province, China
| |
Collapse
|
68
|
Shi C, Chen J, Kang X, Zhao G, Lao X, Zheng H. Deep Learning in the Study of Protein-Related Interactions. Protein Pept Lett 2019; 27:359-369. [PMID: 31538879 DOI: 10.2174/0929866526666190723114142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 03/13/2019] [Accepted: 04/05/2019] [Indexed: 11/22/2022]
Abstract
Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein- drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.
Collapse
Affiliation(s)
- Cheng Shi
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Jiaxing Chen
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Xinyue Kang
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Guiling Zhao
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Xingzhen Lao
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| |
Collapse
|
69
|
Tsuchiya Y, Taneishi K, Yonezawa Y. Autoencoder-Based Detection of Dynamic Allostery Triggered by Ligand Binding Based on Molecular Dynamics. J Chem Inf Model 2019; 59:4043-4051. [PMID: 31386362 DOI: 10.1021/acs.jcim.9b00426] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Dynamic allostery on proteins, triggered by regulator binding or chemical modifications, transmits information from the binding site to distant regions, dramatically altering protein function. It is accompanied by subtle changes in side-chain conformations of the protein, indicating that the changes in dynamics, and not rigid or large conformational changes, are essential to understand regulation of protein function. Although a lot of experimental and theoretical studies have been dedicated to investigate this issue, the regulation mechanism of protein function is still being debated. Here, we propose an autoencoder-based method that can detect dynamic allostery. The method is based on the comparison of time fluctuations of protein structures, in the form of distance matrices, obtained from molecular dynamics simulations in ligand-bound and -unbound forms. Our method detected that the changes in dynamics by ligand binding in the PDZ2 domain led to the reorganization of correlative fluctuation motions among residue pairs, which revealed a different view of the correlated motions from the PCA and DCCM. In addition, other correlative motions were also found as a result of the dynamic perturbation from the ligand binding, which may lead to dynamic allostery. This autoencoder-based method would be usefully applied to the signal transduction and mutagenesis systems involved in protein functions and severe diseases.
Collapse
Affiliation(s)
- Yuko Tsuchiya
- Artificial Intelligence Research Center , National Institute of Advanced Industrial Science and Technology , 2-4-7 Aomi , Koto-ku , Tokyo 135-0064 , Japan
| | - Kei Taneishi
- Cluster for Science, Technology and Innovation Hub , RIKEN , 6-7-3 Minatojima-minamimachi , Chuo-ku, Kobe , Hyogo 650-0047 , Japan
| | - Yasushige Yonezawa
- High Pressure Protein Research Center, Institute of Advanced Technology , Kindai University , 930 Nishimitani , Kinokawa , Wakayama 649-6493 , Japan
| |
Collapse
|
70
|
Wang L, Wang HF, Liu SR, Yan X, Song KJ. Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest. Sci Rep 2019; 9:9848. [PMID: 31285519 PMCID: PMC6614364 DOI: 10.1038/s41598-019-46369-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/10/2019] [Indexed: 01/09/2023] Open
Abstract
Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions.
Collapse
Affiliation(s)
- Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China. .,Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, P.R. China.
| | - Hai-Feng Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China
| | - San-Rong Liu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China
| | - Xin Yan
- School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China.
| | - Ke-Jian Song
- School of information engineering, JiangXi University of Science and Technology, Ganzhou, Jiangxi, 341000, P.R. China
| |
Collapse
|
71
|
Zhou L, Li Z, Yang J, Tian G, Liu F, Wen H, Peng L, Chen M, Xiang J, Peng L. Revealing Drug-Target Interactions with Computational Models and Algorithms. Molecules 2019; 24:molecules24091714. [PMID: 31052598 PMCID: PMC6540161 DOI: 10.3390/molecules24091714] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 04/24/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
Collapse
Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | | | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing 100102, China.
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Hong Wen
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Li Peng
- School of Computer Science, University of Science and Technology of Hunan, Xiangtan 411201, Hunan, China.
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
- Neuroscience Research Center, Department of Basic Medical Sciences, Changsha Medical University, Changsha 410219, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| |
Collapse
|
72
|
Xie G, Wu C, Sun Y, Fan Z, Liu J. LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm. Front Genet 2019; 10:343. [PMID: 31057602 PMCID: PMC6482170 DOI: 10.3389/fgene.2019.00343] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 03/29/2019] [Indexed: 12/26/2022] Open
Abstract
According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.
Collapse
Affiliation(s)
- Guobo Xie
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Cuiming Wu
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Zhiliang Fan
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Jianghui Liu
- Department of Emergency, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
73
|
Wu Q, Ke H, Li D, Wang Q, Fang J, Zhou J. Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery. Curr Top Med Chem 2019; 19:4-16. [DOI: 10.2174/1568026619666190122151634] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/14/2018] [Accepted: 11/16/2018] [Indexed: 12/25/2022]
Abstract
Over the past decades, peptide as a therapeutic candidate has received increasing attention in
drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory
peptides (AIPs). It is considered that the peptides can regulate various complex diseases
which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives
the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide-
based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in
the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with
traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly
machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the
peptide activity. In this review, we document the recent progress in machine learning-based prediction
of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.
Collapse
Affiliation(s)
- Qihui Wu
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Hanzhong Ke
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61802, United States
| | - Dongli Li
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jingwei Zhou
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| |
Collapse
|
74
|
Yang Q, Jia C, Li T. Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier. Math Biosci 2019; 311:103-108. [PMID: 30880100 DOI: 10.1016/j.mbs.2019.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 10/27/2022]
Abstract
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub- support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.
Collapse
Affiliation(s)
- Qing Yang
- Institute of Environmental Systems Biology, College of Environmental and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
| | - Taoying Li
- Department of Maritime Economics and Management, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
| |
Collapse
|
75
|
Chatterjee D, Kaur G, Muradia S, Singh B, Agrewala JN. ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity. Sci Rep 2019; 9:3092. [PMID: 30816123 PMCID: PMC6395627 DOI: 10.1038/s41598-018-36179-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/15/2018] [Indexed: 10/31/2022] Open
Abstract
Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .
Collapse
Affiliation(s)
| | - Gurkirat Kaur
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Shilpa Muradia
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Balvinder Singh
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India.
| | | |
Collapse
|
76
|
Ezzat A, Wu M, Li X, Kwoh CK. Computational Prediction of Drug-Target Interactions via Ensemble Learning. Methods Mol Biol 2019; 1903:239-254. [PMID: 30547446 DOI: 10.1007/978-1-4939-8955-3_14] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is of great importance. Drug-target interaction prediction is especially relevant in the case of drug repositioning where attempts are made to repurpose old drugs for new indications. While experimental wet-lab techniques exist for predicting such interactions, they are tedious and time-consuming. On the other hand, computational methods also exist for predicting interactions, and they do so with reasonable accuracy. In addition, computational methods can help guide their wet-lab counterparts by recommending interactions for further validation. In this chapter, a computational method for predicting drug-target interactions is presented. Specifically, we describe a machine learning method that utilizes ensemble learning to perform predictions. We also mention details pertaining to the preparation of the data required for the prediction effort and demonstrate how to evaluate and improve prediction performance.
Collapse
Affiliation(s)
- Ali Ezzat
- Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
| | - Min Wu
- Data Analytics Department, Institute for Infocomm Research, A-Star, Singapore, Singapore
| | - Xiaoli Li
- Data Analytics Department, Institute for Infocomm Research, A-Star, Singapore, Singapore
| | - Chee-Keong Kwoh
- Division of Software and Information Systems, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|
77
|
Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-3956-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
78
|
Shi H, Liu S, Chen J, Li X, Ma Q, Yu B. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics 2018; 111:1839-1852. [PMID: 30550813 DOI: 10.1016/j.ygeno.2018.12.007] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/06/2018] [Accepted: 12/07/2018] [Indexed: 01/01/2023]
Abstract
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.
Collapse
Affiliation(s)
- Han Shi
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Simin Liu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Junqi Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Xuan Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.
| |
Collapse
|
79
|
Pejčić A, Janković SM, Opančina V, Babić G, Milosavljević M. Drug-drug interactions in patients receiving hematopoietic stem cell transplantation. Expert Opin Drug Metab Toxicol 2018; 15:49-59. [PMID: 30479183 DOI: 10.1080/17425255.2019.1552256] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Recipients of hematopoietic stem cell transplantation (HSCT) are exposed to numerous drugs in both pre- and post-transplantation period, which creates an opportunity for drug-drug interactions (DDIs); if clinically relevant DDIs happen, the risk of adverse treatment outcomes is increased. Areas covered: This review is focused on DDIs in recipients of HSCT that were observed and published as clinical trials, case series or case reports. Relevant publications were found by the systematic search of the following online databases: MEDLINE, SCOPUS, EBSCO, and SCINDEX. Expert opinion: The most important DDIs involve cytostatic or immunosuppressant drug on one side, and antimicrobial drugs on the other. The majority of clinically relevant interactions have pharmacokinetic character, involving drug metabolizing enzymes in the liver. Antifungal azoles inhibit metabolism of many cytostatic and immunosuppressant drugs at cytochromes and increase their plasma concentrations. Macrolide antibiotics and fluoroqunolones should be avoided in HSCT recipients, as they have much larger potential for DDIs than other antibiotic groups. HSCT recipients increasingly receive new immunomodulating drugs, and further observational studies are needed to reveal unsuspected DDIs with clinical relevance.
Collapse
Affiliation(s)
- Ana Pejčić
- a Faculty of Medical Sciences , University of Kragujevac , Kragujevac , Serbia
| | - Slobodan M Janković
- a Faculty of Medical Sciences , University of Kragujevac , Kragujevac , Serbia
| | - Valentina Opančina
- a Faculty of Medical Sciences , University of Kragujevac , Kragujevac , Serbia
| | - Goran Babić
- a Faculty of Medical Sciences , University of Kragujevac , Kragujevac , Serbia
| | - Miloš Milosavljević
- a Faculty of Medical Sciences , University of Kragujevac , Kragujevac , Serbia
| |
Collapse
|
80
|
Abstract
Motivation The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction. Availability and implementation https://github.com/hkmztrk/DeepDTA. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Hakime Öztürk
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Bogazici University, Istanbul, Turkey
| | - Elif Ozkirimli
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
| |
Collapse
|
81
|
Modeling association detection in order to discover compounds to inhibit oral cancer. J Biomed Inform 2018; 84:159-163. [PMID: 30004020 DOI: 10.1016/j.jbi.2018.07.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 07/02/2018] [Accepted: 07/07/2018] [Indexed: 11/24/2022]
Abstract
In the past, algorithms exploiting varying semantics in interactions between biological objects such as genes and diseases have been used in bioinformatics to uncover latent relationships within biological datasets. In this paper, we consider the algorithm Medusa in parallel with binary classification in order to find potential compounds to inhibit oral cancer. Oral cancer affects the mouth and pharynx and has a high mortality rate due to its late discovery. Current methods of oral cancer treatment, such as chemoradiation and surgery, fail to provide better chances for survival, warranting an alternative approach. By running Medusa on a data fusion graph consisting of biological objects, we incorporate binary classification to model the algorithm's association detection to discover compounds with the potential to mitigate the effects of oral cancer.
Collapse
|
82
|
Bansal A, Srivastava PA, Singh TR. An integrative approach to develop computational pipeline for drug-target interaction network analysis. Sci Rep 2018; 8:10238. [PMID: 29980766 PMCID: PMC6035197 DOI: 10.1038/s41598-018-28577-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/26/2018] [Indexed: 11/25/2022] Open
Abstract
Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis. Several of the existing approaches in this direction, however, are data-driven and thus lack potential to be generalized and extrapolated to different species. In this paper, we demonstrate a systems pharmacology pipeline and discuss how the network theory, along with gene ontology (GO) analysis, co-expression analysis, module re-construction, pathway mapping and structure level analysis can be used to decipher important properties of biological networks with the aim to propose lead molecule for the therapeutic interventions of various diseases.
Collapse
Affiliation(s)
- Ankush Bansal
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, 173234, Solan, HP, India
| | - Pulkit Anupam Srivastava
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, 173234, Solan, HP, India
| | - Tiratha Raj Singh
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, 173234, Solan, HP, India.
| |
Collapse
|