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Khojasteh H, Pirgazi J, Ghanbari Sorkhi A. Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques. PLoS One 2023; 18:e0288173. [PMID: 37535616 PMCID: PMC10399861 DOI: 10.1371/journal.pone.0288173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/21/2023] [Indexed: 08/05/2023] Open
Abstract
Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI.
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Affiliation(s)
- Hakimeh Khojasteh
- Department of Computer Engineering, University of Zanjan, Zanjan, Iran
- School of Biological Sciences Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Jamshid Pirgazi
- School of Biological Sciences Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
| | - Ali Ghanbari Sorkhi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
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2
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Rafiei F, Zeraati H, Abbasi K, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics 2023; 39:btad438. [PMID: 37467066 PMCID: PMC10397534 DOI: 10.1093/bioinformatics/btad438] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
MOTIVATION Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligent in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 1571914911, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 1417614411, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London W21PG, United Kingdom
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
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Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 2023; 24:38. [PMID: 36737694 PMCID: PMC9896467 DOI: 10.1186/s12859-023-05153-y] [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: 10/03/2022] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.
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Affiliation(s)
- Lingzhi Hu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Chengzhou Fu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Zhonglu Ren
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Yongming Cai
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Jin Yang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Siwen Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Wenhua Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Deyu Tang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,grid.79703.3a0000 0004 1764 3838School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
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4
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Peng Y, Zhao S, Zeng Z, Hu X, Yin Z. LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions. Front Microbiol 2023; 13:1092467. [PMID: 36687573 PMCID: PMC9849804 DOI: 10.3389/fmicb.2022.1092467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.
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Vora DS, Kalakoti Y, Sundar D. Computational Methods and Deep Learning for Elucidating Protein Interaction Networks. Methods Mol Biol 2023; 2553:285-323. [PMID: 36227550 DOI: 10.1007/978-1-0716-2617-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.
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Affiliation(s)
- Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Yogesh Kalakoti
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
- School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
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6
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Moon C, Kim D. Prediction of drug-target interactions through multi-task learning. Sci Rep 2022; 12:18323. [PMID: 36316405 PMCID: PMC9622881 DOI: 10.1038/s41598-022-23203-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/26/2022] [Indexed: 12/31/2022] Open
Abstract
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. However, multi-task learning sometimes worsens the overall performance or generates a trade-off between individual task's performance. In this study, we propose a general multi-task learning scheme that not only increases the average performance but also minimizes individual performance degradation, through group selection and knowledge distillation. The groups are selected on the basis of chemical similarity between ligand sets of targets, and the similar targets in the same groups are trained together. During training, we apply knowledge distillation with teacher annealing. The multi-task learning models are guided by the predictions of the single-task learning models. This method results in higher average performance than that from single-task learning and classic multi-task learning. Further analysis reveals that multi-task learning is particularly effective for low performance tasks, and knowledge distillation helps the model avoid the degradation in individual task performance in multi-task learning.
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Affiliation(s)
- Chaeyoung Moon
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Dongsup Kim
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
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7
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El-Behery H, Attia AF, El-Fishawy N, Torkey H. An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing. J Biol Eng 2022; 16:21. [PMID: 35941686 PMCID: PMC9361677 DOI: 10.1186/s13036-022-00296-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately. Methods This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions. Results Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.
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Affiliation(s)
- Heba El-Behery
- Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh, Egypt.
| | - Abdel-Fattah Attia
- Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh, Egypt
| | - Nawal El-Fishawy
- Computer Science & Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Hanaa Torkey
- Computer Science & Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
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8
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UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning. Molecules 2022; 27:molecules27092980. [PMID: 35566330 PMCID: PMC9100109 DOI: 10.3390/molecules27092980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 01/27/2023] Open
Abstract
Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.
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9
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Mohammadi A, Zahiri J, Mohammadi S, Khodarahmi M, Arab SS. PSSMCOOL: A Comprehensive R Package for Generating Evolutionary-based Descriptors of Protein Sequences from PSSM Profiles. BIOLOGY METHODS AND PROTOCOLS 2022; 7:bpac008. [PMID: 35388370 PMCID: PMC8977839 DOI: 10.1093/biomethods/bpac008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 01/21/2022] [Indexed: 11/14/2022]
Abstract
Position-specific scoring matrix (PSSM), also called profile, is broadly used for representing the evolutionary history of a given protein sequence. Several investigations reported that the PSSM-based feature descriptors can improve the prediction of various protein attributes such as interaction, function, subcellular localization, secondary structure, disorder regions, and accessible surface area. While plenty of algorithms have been suggested for extracting evolutionary features from PSSM in recent years, there is not any integrated standalone tool for providing these descriptors. Here, we introduce PSSMCOOL, a flexible comprehensive R package that generates 38 PSSM-based feature vectors. To our best knowledge, PSSMCOOL is the first PSSM-based feature extraction tool implemented in R. With the growing demand for exploiting machine-learning algorithms in computational biology, this package would be a practical tool for machine-learning predictions.
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Affiliation(s)
- Alireza Mohammadi
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Javad Zahiri
- Department of Neuroscience, University of California San Diego, California, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Saber Mohammadi
- Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohsen Khodarahmi
- Department of Radiology, Shahid Madani Hospital, Karaj, Iran
- Bahar Medical Imaging Center, Karaj, Iran
- Dr. Khodarahmi Medical Imaging Center, Karaj, Iran
| | - Seyed Shahriar Arab
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
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10
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Ding Y, Tang J, Guo F, Zou Q. Identification of drug-target interactions via multiple kernel-based triple collaborative matrix factorization. Brief Bioinform 2022; 23:6520305. [PMID: 35134117 DOI: 10.1093/bib/bbab582] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/02/2021] [Accepted: 12/19/2021] [Indexed: 12/15/2022] Open
Abstract
Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug-target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets.
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Affiliation(s)
- Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, P.R.China
| | - Jijun Tang
- Department of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, P.R.China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P.R.China
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11
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The Discovery of New Drug-Target Interactions for Breast Cancer Treatment. Molecules 2021; 26:molecules26247474. [PMID: 34946556 PMCID: PMC8704452 DOI: 10.3390/molecules26247474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/07/2021] [Accepted: 12/07/2021] [Indexed: 01/09/2023] Open
Abstract
Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.
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12
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Zhang Y, Jiang Z, Chen C, Wei Q, Gu H, Yu B. DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier. Interdiscip Sci 2021; 14:311-330. [PMID: 34731411 DOI: 10.1007/s12539-021-00488-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Accurate prediction of drug-target interactions (DTIs), which is often used in the fields of drug discovery and drug repositioning, is regarded a key challenge in the study of drug science. In this paper, a new method called DeepStack-DTIs is proposed to predict DTIs. First, for the target protein, pseudo-position specific score matrix, pseudo amino acid composition and SPIDER3 are used to extract the different feature information of the target protein. Meanwhile, the path-based fingerprint features of each drug are extracted. Then, the synthetic minority oversampling technique (SMOTE) and light gradient boosting machine (LightGBM) are used for data balancing and feature selection, respectively. Finally, the processed features are input to the deep-stacked ensemble classifier composed of gated recurrent unit (GRU), deep neural network (DNN), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and logistic regression (LR) to predict DTIs. Under the five-fold cross-validation and compared with existing methods, the proposed method achieves higher prediction accuracy on the gold standard dataset. To evaluate the predictive power of DeepStack-DTIs, we validate the method on another dataset and predict the drug-target interaction network. The results indicate that DeepStack-DTIs has excellent predictive ability than the other methods, and provides novel insights for the prediction of DTIs. A novel method DeepStack-DTIs for drug-target interactions prediction. PsePSSM, PseAAC, SPIDER3 and FP2 are fused to convert protein sequence and drug molecule information into digital information, respectively. The SMOTE algorithm is used to balance the dataset and LightGBM feature selection algorithm is employed to remove redundant and irrelevant features to select the optimal feature subset. This optimal feature subset is inputted into the deep-stacked ensemble classifier to predict drug-target interactions. The experimental results show DeepStack-DTIs method can significantly improve the prediction accuracy of drug-target interactions.
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Affiliation(s)
- Yan Zhang
- College of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China.,College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Zhiwen Jiang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Cheng Chen
- School of Computer Science and Technology, Shandong University, Qingdao, 266237, China
| | - Qinqin Wei
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Haiming Gu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China. .,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China. .,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, China.
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13
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Identification of drug-target interactions via multi-view graph regularized link propagation model. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.100] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Predicting Drug-Target Interactions Based on the Ensemble Models of Multiple Feature Pairs. Int J Mol Sci 2021; 22:ijms22126598. [PMID: 34202954 PMCID: PMC8234024 DOI: 10.3390/ijms22126598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/09/2021] [Accepted: 06/16/2021] [Indexed: 11/30/2022] Open
Abstract
Backgroud: The prediction of drug–target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug–target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs.
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15
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Yang Z, Zhong W, Zhao L, Chen CYC. ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction. J Phys Chem Lett 2021; 12:4247-4261. [PMID: 33904745 DOI: 10.1021/acs.jpclett.1c00867] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Deep learning (DL) provides opportunities for the identification of drug-target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and target encoder as two independent modules without considering the relationship between them. In this study, we propose a mutual learning mechanism to bridge the gap between the two encoders. We formulated the DTI problem from a global perspective by inserting mutual learning layers between the two encoders. The mutual learning layer was achieved by multihead attention and position-aware attention. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model. We evaluated our approach using three benchmark kinase data sets under different experimental settings and compared the proposed method to three baseline models. We found that the four methods yielded similar results in the random split setting (training and test sets share common drugs and targets), while the proposed method increases the predictive performance significantly in the orphan-target and orphan-drug split setting (training and test sets share only targets or drugs). The experimental results demonstrated that the proposed method improved the generalization and interpretation capability of DTI modeling.
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Affiliation(s)
- Ziduo Yang
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China
| | - Weihe Zhong
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China
| | - Lu Zhao
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China
- Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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16
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An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9933873. [PMID: 33987446 PMCID: PMC8093043 DOI: 10.1155/2021/9933873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 11/24/2022]
Abstract
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.
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17
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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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18
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Abbasi K, Razzaghi P, Poso A, Amanlou M, Ghasemi JB, Masoudi-Nejad A. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics 2021; 36:4633-4642. [PMID: 32462178 DOI: 10.1093/bioinformatics/btaa544] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 04/29/2020] [Accepted: 05/22/2020] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION An essential part of drug discovery is the accurate prediction of the binding affinity of new compound-protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the training phase. However, in real-world situations, the test and training data are sampled from different domains with different distributions. To cope with this challenge, we propose a deep learning-based approach that consists of three steps. In the first step, the training encoder network learns a novel representation of compounds and proteins. To this end, we combine convolutional layers and long-short-term memory layers so that the occurrence patterns of local substructures through a protein and a compound sequence are learned. Also, to encode the interaction strength of the protein and compound substructures, we propose a two-sided attention mechanism. In the second phase, to deal with the different distributions of the training and test domains, a feature encoder network is learned for the test domain by utilizing an adversarial domain adaptation approach. In the third phase, the learned test encoder network is applied to new compound-protein pairs to predict their binding affinity. RESULTS To evaluate the proposed approach, we applied it to KIBA, Davis and BindingDB datasets. The results show that the proposed method learns a more reliable model for the test domain in more challenging situations. AVAILABILITY AND IMPLEMENTATION https://github.com/LBBSoft/DeepCDA.
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Affiliation(s)
- Karim Abbasi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
| | - Parvin Razzaghi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 4513766731, Iran
| | - Antti Poso
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio 80100, Finland
| | - Massoud Amanlou
- Department of Medicinal Chemistry, Drug Design and Development Research Center, Tehran University of Medical Sciences, Tehran 1416753955, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Sciences, University of Tehran, Tehran 1417614418, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
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19
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Hasan Mahmud SM, Chen W, Jahan H, Dai B, Din SU, Dzisoo AM. DeepACTION: A deep learning-based method for predicting novel drug-target interactions. Anal Biochem 2020; 610:113978. [PMID: 33035462 DOI: 10.1016/j.ab.2020.113978] [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: 01/04/2020] [Revised: 09/23/2020] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs) and accelerate the process of drug repurposing. However, a vast number of interactions remain undiscovered; therefore, we proposed a deep learning-based method (deepACTION) for predicting potential or unknown DTIs. Here, each drug chemical structure and protein sequence are transformed according to structural and sequence information using different descriptors to represent their features correctly. There have been some challenges, such as the high dimensionality and class imbalance of data during the prediction process. To address these problems, we developed the MMIB technique to balance the majority and minority instances in the dataset and utilized a LASSO model to handle the high dimensionality of the data. In addition, we trained the convolutional neural network algorithm with balanced and reduced features for accurate prediction of DTIs. In this study, the AUC is considered a primary evaluation metric for comparing the performance of the deep ACTION model with that of existing methods by a 5-fold cross-validation test. Our experiential dataset obtained from the DrugBank database and our deepACTION model achieved an AUC of 0.9836 for this dataset. The experimental results ensured that the model can predict significant numbers of new DTIs and provide complete information to motivate scientists to develop drugs.
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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.
| | - Hosney Jahan
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Bo Dai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Salah Ud Din
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Anthony Mackitz Dzisoo
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China
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20
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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.
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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
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21
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Sharma R, Kumar S, Tsunoda T, Kumarevel T, Sharma A. Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles. Anal Biochem 2020; 612:113954. [PMID: 32946833 DOI: 10.1016/j.ab.2020.113954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/26/2020] [Accepted: 09/10/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND DNA-binding proteins perform important roles in cellular processes and are involved in many biological activities. These proteins include crucial protein-DNA binding domains and can interact with single-stranded or double-stranded DNA, and accordingly classified as single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Computational prediction of SSBs and DSBs helps in annotating protein functions and understanding of protein-binding domains. RESULTS Performance is reported using the DNA-binding protein dataset that was recently introduced by Wang et al., [1]. The proposed method achieved a sensitivity of 0.600, specificity of 0.792, AUC of 0.758, MCC of 0.369, accuracy of 0.744, and F-measure of 0.536, on the independent test set. CONCLUSION The proposed method with the hidden Markov model (HMM) profiles for feature extraction, outperformed the benchmark method in the literature and achieved an overall improvement of approximately 3%. The source code and supplementary information of the proposed method is available at https://github.com/roneshsharma/Predict-DNA-binding-proteins/wiki.
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Affiliation(s)
- Ronesh Sharma
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; Laboratory of Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan.
| | - Thirumananseri Kumarevel
- Laboratory for Transcription Structural Biology, RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
| | - Alok Sharma
- Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; School of Engineering and Physics, The University of the South Pacific, Suva, Fiji; Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia.
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22
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Identification of Drug–Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106254] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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23
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Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4516250. [PMID: 32908888 PMCID: PMC7463380 DOI: 10.1155/2020/4516250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/02/2020] [Accepted: 08/10/2020] [Indexed: 12/02/2022]
Abstract
Identifying the drug-target interactions (DTIs) plays an essential role in new drug development. However, there still has the limited knowledge of DTIs and a significant number of unknown DTI pairs. Moreover, the traditional experimental methods have inevitable disadvantages such as high cost and time-consuming. Therefore, developing computational methods for predicting DTIs is attracting more and more attention. In this study, we report a novel computational approach for predicting DTI using GIST feature, position-specific scoring matrix (PSSM), and rotation forest (RF). Specifically, each target protein is first converted into a PSSM for retaining evolutionary information. Then, the GIST feature is extracted from PSSM and substructure fingerprint information is adopted to extract the feature of the drug. Finally, combining each protein and drug features to form a new drug-target pair, which is employed as input feature for RF classifier. In the experiment, the proposed method achieves high average accuracies of 89.25%, 85.93%, 82.36%, and 73.89% on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively. For further evaluating the prediction performance of the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the same golden standard dataset. These promising results illustrate that the proposed method is more effective and stable than other methods. We expect the proposed method to be a useful tool for predicting large-scale DTIs.
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24
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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.
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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.)
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25
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Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
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Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
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26
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Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions. Sci Rep 2020; 10:6641. [PMID: 32313024 PMCID: PMC7171114 DOI: 10.1038/s41598-020-62891-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/12/2020] [Indexed: 01/29/2023] Open
Abstract
Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.
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27
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Pournoor E, Mousavian Z, Dalini AN, Masoudi-Nejad A. Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework. Sci Rep 2020; 10:4991. [PMID: 32193399 PMCID: PMC7081269 DOI: 10.1038/s41598-020-59605-z] [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: 08/06/2019] [Accepted: 01/31/2020] [Indexed: 12/21/2022] Open
Abstract
Complexity of cascading interrelations between molecular cell components at different levels from genome to metabolome ordains a massive difficulty in comprehending biological happenings. However, considering these complications in the systematic modelings will result in realistic and reliable outputs. The multilayer networks approach is a relatively innovative concept that could be applied for multiple omics datasets as an integrative methodology to overcome heterogeneity difficulties. Herein, we employed the multilayer framework to rehabilitate colon adenocarcinoma network by observing co-expression correlations, regulatory relations, and physical binding interactions. Hub nodes in this three-layer network were selected using a heterogeneous random walk with random jump procedure. We exploited local composite modules around the hub nodes having high overlay with cancer-specific pathways, and investigated their genes showing a different expressional pattern in the tumor progression. These genes were examined for survival effects on the patient's lifespan, and those with significant impacts were selected as potential candidate biomarkers. Results suggest that identified genes indicate noteworthy importance in the carcinogenesis of the colon.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Abbas Nowzari Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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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/.
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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
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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: 161] [Impact Index Per Article: 40.3] [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.
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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
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30
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Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform 2019; 22:451-462. [PMID: 31885041 DOI: 10.1093/bib/bbz152] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Xiangeng Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Wei Wang
- Mathematical Sciences, Shanghai Jiao Tong University
| | - Yufang Zhang
- 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
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31
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Zhang W, Lin W, Zhang D, Wang S, Shi J, Niu Y. Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction. Curr Drug Metab 2019; 20:194-202. [PMID: 30129407 DOI: 10.2174/1389200219666180821094047] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 01/18/2018] [Accepted: 03/19/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. RESULTS In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. CONCLUSION This study provides the guide to the development of computational methods for the drug-target interaction prediction.
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Affiliation(s)
- Wen Zhang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Weiran Lin
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Ding Zhang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Siman Wang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Jingwen Shi
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Yanqing Niu
- School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China
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32
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Mahmud SMH, Chen W, Meng H, Jahan H, Liu Y, Hasan SMM. Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting. Anal Biochem 2019; 589:113507. [PMID: 31734254 DOI: 10.1016/j.ab.2019.113507] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/05/2019] [Accepted: 11/08/2019] [Indexed: 12/29/2022]
Abstract
Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.
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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.
| | - Han Meng
- School of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Hosney Jahan
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
| | - Yongsheng Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - S M Mamun Hasan
- Department of Internal Medicine, Rangpur Medical College, Rangpur, 5400, Bangladesh.
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33
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Abbasi K, Poso A, Ghasemi J, Amanlou M, Masoudi-Nejad A. Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery. J Chem Inf Model 2019; 59:4528-4539. [PMID: 31661955 DOI: 10.1021/acs.jcim.9b00626] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The main problem of small molecule-based drug discovery is to find a candidate molecule with increased pharmacological activity, proper ADME, and low toxicity. Recently, machine learning has driven a significant contribution to drug discovery. However, many machine learning methods, such as deep learning-based approaches, require a large amount of training data to form accurate predictions for unseen data. In lead optimization step, the amount of available biological data on small molecule compounds is low, which makes it a challenging problem to apply machine learning methods. The main goal of this study is to design a new approach to handle these situations. To this end, source assay (auxiliary assay) knowledge is utilized to learn a better model to predict the property of new compounds in the target assay. Up to now, the current approaches did not consider that source and target assays are adapted to different target groups with different compounds distribution. In this paper, we propose a new architecture by utilizing graph convolutional network and adversarial domain adaptation network to tackle this issue. To evaluate the proposed approach, we applied it to Tox21, ToxCast, SIDER, HIV, and BACE collections. The results showed the effectiveness of the proposed approach in transferring the related knowledge from source to target data set.
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Affiliation(s)
- Karim Abbasi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics , University of Tehran , Tehran 1417614411 , Iran
| | - Antti Poso
- School of Pharmacy, Faculty of Health Sciences , University of Eastern Finland , Kuopio 80100 , Finland
| | - Jahanbakhsh Ghasemi
- Chemistry Department, Faculty of Sciences , University of Tehran , Tehran 1417614418 , Iran
| | - Massoud Amanlou
- Drug Design and Development Research Center, Department of Medicinal Chemistry , Tehran University of Medical Sciences , Tehran 1416753955 , Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics , University of Tehran , Tehran 1417614411 , Iran
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34
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Ding Y, Tang J, Guo F. Identification of drug–target interactions via fuzzy bipartite local model. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04569-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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35
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Durán C, Daminelli S, Thomas JM, Haupt VJ, Schroeder M, Cannistraci CV. Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. Brief Bioinform 2019; 19:1183-1202. [PMID: 28453640 PMCID: PMC6291778 DOI: 10.1093/bib/bbx041] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Indexed: 01/03/2023] Open
Abstract
The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.
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Affiliation(s)
| | - Simone Daminelli
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | | | | | - Michael Schroeder
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | - Carlo Vittorio Cannistraci
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
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Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics 2019; 34:1850-1858. [PMID: 29360926 DOI: 10.1093/bioinformatics/bty032] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/17/2018] [Indexed: 12/15/2022] Open
Abstract
Motivation Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. Results OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. Contact ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ronesh Sharma
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.,School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji
| | - Gaurav Raicar
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Tatsuhiko Tsunoda
- Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan.,CREST, JST, Tokyo 113-8510, Japan
| | - Ashwini Patil
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan
| | - Alok Sharma
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.,Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan.,CREST, JST, Tokyo 113-8510, Japan.,Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia
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37
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CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction. J Theor Biol 2019; 464:1-8. [DOI: 10.1016/j.jtbi.2018.12.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 12/13/2018] [Accepted: 12/18/2018] [Indexed: 01/12/2023]
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38
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Disease global behavior: A systematic study of the human interactome network reveals conserved topological features among categories of diseases. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100249] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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39
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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.
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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.
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40
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iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting. Sci Rep 2017; 7:17731. [PMID: 29255285 PMCID: PMC5735173 DOI: 10.1038/s41598-017-18025-2] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 12/05/2017] [Indexed: 02/07/2023] Open
Abstract
Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.
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Ding Y, Tang J, Guo F. Identification of drug-target interactions via multiple information integration. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.08.045] [Citation(s) in RCA: 138] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Cheng T, Hao M, Takeda T, Bryant SH, Wang Y. Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. AAPS J 2017; 19:1264-1275. [PMID: 28577120 PMCID: PMC11097213 DOI: 10.1208/s12248-017-0092-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 04/25/2017] [Indexed: 11/30/2022] Open
Abstract
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.
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Affiliation(s)
- Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Takako Takeda
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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Peng L, Zhu W, Liao B, Duan Y, Chen M, Chen Y, Yang J. Screening drug-target interactions with positive-unlabeled learning. Sci Rep 2017; 7:8087. [PMID: 28808275 PMCID: PMC5556112 DOI: 10.1038/s41598-017-08079-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 07/04/2017] [Indexed: 02/03/2023] Open
Abstract
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.
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Affiliation(s)
- Lihong Peng
- Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
- College of Information Engineering, Changsha Medical University, Changsha Hunan, 410219, China
| | - Wen Zhu
- Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
| | - Bo Liao
- Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China.
| | - Yu Duan
- Hunan University of Commerce, Changsha Hunan, 410205, China
| | - Min Chen
- Key Laboratory for Embedded and Network Computing of Hunan Province, College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
| | - Yi Chen
- College of Drug, Changsha Medical University, Changsha Hunan, 410219, China
| | - Jialiang Yang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, NY, 10029, USA
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Sharma R, Kumar S, Tsunoda T, Patil A, Sharma A. Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics 2016; 17:504. [PMID: 28155710 PMCID: PMC5259822 DOI: 10.1186/s12859-016-1375-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task. METHODS In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others). RESULTS To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors. CONCLUSIONS Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.
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Affiliation(s)
- Ronesh Sharma
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
| | - Tatsuhiko Tsunoda
- CREST, JST, Yokohama, 230-0045, Japan.,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan.,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
| | - Ashwini Patil
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
| | - Alok Sharma
- School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. .,CREST, JST, Yokohama, 230-0045, Japan. .,RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan. .,Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
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