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Chen D, Li S, Chen Y. ISTRF: Identification of sucrose transporter using random forest. Front Genet 2022; 13:1012828. [PMID: 36171889 PMCID: PMC9511101 DOI: 10.3389/fgene.2022.1012828] [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: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 12/05/2022] Open
Abstract
Sucrose transporter (SUT) is a type of transmembrane protein that exists widely in plants and plays a significant role in the transportation of sucrose and the specific signal sensing process of sucrose. Therefore, identifying sucrose transporter is significant to the study of seed development and plant flowering and growth. In this study, a random forest-based model named ISTRF was proposed to identify sucrose transporter. First, a database containing 382 SUT proteins and 911 non-SUT proteins was constructed based on the UniProt and PFAM databases. Second, k-separated-bigrams-PSSM was exploited to represent protein sequence. Third, to overcome the influence of imbalance of samples on identification performance, the Borderline-SMOTE algorithm was used to overcome the shortcoming of imbalance training data. Finally, the random forest algorithm was used to train the identification model. It was proved by 10-fold cross-validation results that k-separated-bigrams-PSSM was the most distinguishable feature for identifying sucrose transporters. The Borderline-SMOTE algorithm can improve the performance of the identification model. Furthermore, random forest was superior to other classifiers on almost all indicators. Compared with other identification models, ISTRF has the best general performance and makes great improvements in identifying sucrose transporter proteins.
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Affiliation(s)
- Dong Chen
- College of Electrical and Information Engineering, Qu Zhou University, Quzhou, China
| | - Sai Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yu Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence. BIOLOGY 2022; 11:biology11070995. [PMID: 36101379 PMCID: PMC9311754 DOI: 10.3390/biology11070995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022]
Abstract
Simple Summary Due to most traditional high-throughput experiments are tedious and laborious in identifying potential protein–protein interaction. To better improve accuracy prediction in protein–protein interactions. We proposed a novel computational method that can identify unknown protein–protein interaction efficiently and hope this method can provide a helpful idea and tool for proteomics research. Abstract Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research.
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Wang Y, Wang LL, Wong L, Li Y, Wang L, You ZH. SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks. Biomedicines 2022; 10:biomedicines10071543. [PMID: 35884848 PMCID: PMC9313220 DOI: 10.3390/biomedicines10071543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important protein interaction, self-interacting protein (SIP) has a critical role. The fast growth of high-throughput experimental techniques among biomolecules has led to a massive influx of available SIP data. How to conduct scientific research using the massive amount of SIP data has become a new challenge that is being faced in related research fields such as biology and medicine. In this work, we design an SIP prediction method SIPGCN using a deep learning graph convolutional network (GCN) based on protein sequences. First, protein sequences are characterized using a position-specific scoring matrix, which is able to describe the biological evolutionary message, then their hidden features are extracted by the deep learning method GCN, and, finally, the random forest is utilized to predict whether there are interrelationships between proteins. In the cross-validation experiment, SIPGCN achieved 93.65% accuracy and 99.64% specificity in the human data set. SIPGCN achieved 90.69% and 99.08% of these two indicators in the yeast data set, respectively. Compared with other feature models and previous methods, SIPGCN showed excellent results. These outcomes suggest that SIPGCN may be a suitable instrument for predicting SIP and may be a reliable candidate for future wet experiments.
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Affiliation(s)
- Ying Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
| | - Lin-Lin Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
- Correspondence: (L.-L.W.); (L.W.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
- Correspondence: (L.-L.W.); (L.W.)
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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Yi HC, You ZH, Guo ZH, Huang DS, Chan KCC. Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2546-2554. [PMID: 32070992 DOI: 10.1109/tcbb.2020.2973091] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A key aim of post-genomic biomedical research is to systematically understand molecules and their interactions in human cells. Multiple biomolecules coordinate to sustain life activities, and interactions between various biomolecules are interconnected. However, existing studies usually only focusing on associations between two or very limited types of molecules. In this study, we propose a network representation learning based computational framework MAN-SDNE to predict any intermolecular associations. More specifically, we constructed a large-scale molecular association network of multiple biomolecules in human by integrating associations among long non-coding RNA, microRNA, protein, drug, and disease, containing 6,528 molecular nodes, 9 kind of,105,546 associations. And then, the feature of each node is represented by its network proximity and attribute features. Furthermore, these features are used to train Random Forest classifier to predict intermolecular associations. MAN-SDNE achieves a remarkable performance with an AUC of 0.9552 and an AUPR of 0.9338 under five-fold cross-validation. To indicate the ability to predict specific types of interactions, a case study for predicting lncRNA-protein interactions using MAN-SDNE is also executed. Experimental results demonstrate this work offers a systematic insight for understanding the synergistic associations between molecules and complex diseases and provides a network-based computational tool to systematically explore intermolecular interactions.
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Zheng K, You ZH, Wang L, Li YR, Zhou JR, Zeng HT. MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1733-1742. [PMID: 32749964 DOI: 10.1109/tcbb.2020.3013837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounter training issues such as sensitivity to hyperparameter tuning and "catastrophic forgetting" when adding new data. However, with the development of biomedicine and the accumulation of biological data, new predictive models are required to face the challenge of adapting to change. To this end, we propose a computational approach based on Broad learning system (BLS) to predict potential disease-associated miRNAs that retain the ability to distinguish prior training associations when new data need to be adapted. In particular, we are introducing incremental learning to the field of biological association prediction for the first time and proposed a new method for quantifying sequence similarity. In the performance evaluation, the AUC in the 5-fold cross-validation was 0.9400 +/- 0.0041. To better assess the effectiveness of MISSIM, we compared it with various classifiers and former prediction models. Its performance is superior to the previous method. Besides, the case study on identifying miRNAs associated with breast neoplasms, lung neoplasms and esophageal neoplasms show that 34, 36 and 35 out of the top 40 associations predicted by MISSIM are confirmed by recent biomedical resources. These results provide ample convincing evidence of this approach have potential value and prospect in promoting biomedical research productivity.
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Jia LN, Yan X, You ZH, Zhou X, Li LP, Wang L, Song KJ. NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information. Evol Bioinform Online 2020; 16:1176934320984171. [PMID: 33488064 PMCID: PMC7768313 DOI: 10.1177/1176934320984171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 12/01/2020] [Indexed: 12/13/2022] Open
Abstract
The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.
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Affiliation(s)
- Li-Na Jia
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Xin Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
- School of Foreign Languages, Zaozhuang University, Zaozhuang, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Xi Zhou
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Li-Ping Li
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- Lei Wang, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
| | - Ke-Jian Song
- School of information engineering, Jiangxi University of Science and Technology, Ganzhou, China
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Chen H, Li F, Wang L, Jin Y, Chi CH, Kurgan L, Song J, Shen J. Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions. Brief Bioinform 2020; 22:5847611. [PMID: 32459334 DOI: 10.1093/bib/bbaa068] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 12/11/2022] Open
Abstract
In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.
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Qu J, Zhao Y, Zhang L, Cai SB, Ming Z, Wang CC. Computational Models for Self-Interacting Proteins Prediction. Protein Pept Lett 2019; 27:392-399. [PMID: 31880240 DOI: 10.2174/0929866527666191227141713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 11/22/2022]
Abstract
Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to understand its functions. Previous studies on SIPs have focused on their structures and functions, while their whole properties are less emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical research, which will help to understanding the function and mechanism of proteins. It is worth noting that high throughput methods can be used for SIPs prediction, but can be costly, time consuming and challenging. Therefore, it is urgent to design computational models for the identification of SIPs. In this review, the concept and function of SIPs were introduced in detail. We further introduced SIPs data and some excellent computational models that have been designed for SIPs prediction. Specially, the most existing approaches were developed based on machine learning through carrying out different extract feature methods. Finally, we discussed several difficult problems in developing computational models for SIPs prediction.
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Affiliation(s)
- Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Shu-Bin Cai
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhong Ming
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
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Zhan ZH, Jia LN, Zhou Y, Li LP, Yi HC. BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information. Int J Mol Sci 2019; 20:E978. [PMID: 30813451 PMCID: PMC6412311 DOI: 10.3390/ijms20040978] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 02/19/2019] [Accepted: 02/20/2019] [Indexed: 11/26/2022] Open
Abstract
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.
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Affiliation(s)
- Zhao-Hui Zhan
- China University of Mining and Technology, Xuzhou 221116, China.
| | - Li-Na Jia
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China.
| | - Yong Zhou
- China University of Mining and Technology, Xuzhou 221116, China.
| | - Li-Ping Li
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
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Chen ZH, You ZH, Li LP, Wang YB, Wong L, Yi HC. Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform. Int J Mol Sci 2019; 20:ijms20040930. [PMID: 30795499 PMCID: PMC6412412 DOI: 10.3390/ijms20040930] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 01/06/2019] [Accepted: 01/07/2019] [Indexed: 12/30/2022] Open
Abstract
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.
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Affiliation(s)
- Zhan-Heng Chen
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Li-Ping Li
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
| | - Yan-Bin Wang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
| | - Leon Wong
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Hai-Cheng Yi
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
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Lin H, Peng S, Huang J. Special issue on Computational Resources and Methods in Biological Sciences. Int J Biol Sci 2018; 14:807-810. [PMID: 29989106 PMCID: PMC6036761 DOI: 10.7150/ijbs.27554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/03/2018] [Indexed: 12/11/2022] Open
Abstract
This special issue covers a wide range of topics in computational biology, such as database construction, sequence analysis and function prediction with machine learning methods, disease-related diagnosis, drug-target and drug discovery, and electronic health record system construction.
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Affiliation(s)
- Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Shaoliang Peng
- School of Computer Science, National University of Defense Technology, Changsha 410073, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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