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Zhang W, Liu B. iSnoDi-MDRF: Identifying snoRNA-Disease Associations Based on Multiple Biological Data by Ranking Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3013-3019. [PMID: 37030816 DOI: 10.1109/tcbb.2023.3258448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Accumulating evidence indicates that the dysregulation of small nucleolar RNAs (snoRNAs) is relevant with diseases. Identifying snoRNA-disease associations by computational methods is desired for biologists, which can save considerable costs and time compared biological experiments. However, it still faces some challenges as followings: (i) Many snoRNAs are detected in recent years, but only a few snoRNAs have been proved to be associated with diseases; (ii) Computational predictors trained with only a few known snoRNA-disease associations fail to accurately identify the snoRNA-disease associations. In this study, we propose a ranking framework, called iSnoDi-MDRF, to identify potential snoRNA-disease associations based on multiple biological data, which has the following highlights: (i) iSnoDi-MDRF integrates ranking framework, which is not only able to identify potential associations between known snoRNAs and diseases, but also can identify diseases associated with new snoRNAs. (ii) Known gene-disease associations are employed to help train a mature model for predicting snoRNA-disease association. Experimental results illustrate that iSnoDi-MDRF is very suitable for identifying potential snoRNA-disease associations. The web server of iSnoDi-MDRF predictor is freely available at http://bliulab.net/iSnoDi-MDRF/.
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Wu D, Fang X, Luan K, Xu Q, Lin S, Sun S, Yang J, Dong B, Manavalan B, Liao Z. Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method. Comput Biol Med 2023; 162:107065. [PMID: 37267826 DOI: 10.1016/j.compbiomed.2023.107065] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.
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Affiliation(s)
- Duanzhi Wu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Xin Fang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350012, China
| | - Kai Luan
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qijin Xu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Shiqi Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Shiying Sun
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Jiaying Yang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Bingying Dong
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
| | - Zhijun Liao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
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Xu Y, Cui X, Zhang L, Zhao T, Wang Y. Metastasis-related gene identification by compound constrained NMF and a semisupervised cluster approach using pancancer multiomics features. Comput Biol Med 2022; 151:106263. [PMID: 36371902 DOI: 10.1016/j.compbiomed.2022.106263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/16/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In recent years, with the gradual increase in pancancer-related research, more attention has been given to the field of pancancer metastasis. However, the molecular mechanism of pancancer metastasis is very unclear, and identification methods for pancancer metastasis-related genes are still lacking. In view of this research status, we developed a novel pipeline to identify pancancer metastasis-related genes based on compound constrained nonnegative matrix factorization (CCNMF). To solve the above problems, the following modules were designed. A correntropy operator and feature similarity fusion (FSF) were first adopted to process the multiomics features of genes; thus, the influences caused by irrelevant biomolecular patterns, manifested as non-Gaussian noise, were minimized. CCNMF was then adopted to handle the above features with compound constraints consisting of a gene relation network and a "metastasis-related" gene set, which maximizes the biological interpretability of the metafeatures generated by NMF. Since a negative set of pancancer "metastasis-related" genes could hardly be obtained, semisupervised analyses were performed on gene features acquired by each step in our pipeline to examine our method's effect. 83% of the 236 candidates identified by the above method were associated with the metastasis of one or more cancers, 71.9% candidates were identified immune-related in pancancer in addition to the hallmark genes. Our study provides an effective and interpretable method for identifying metastasis-related as well as immune-related genes, and the method is successfully applied to TCGA pancancer data.
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Affiliation(s)
- Yining Xu
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Xinran Cui
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Liyuan Zhang
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Tianyi Zhao
- School of medicine and Health, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, 92 Xidazhi Street,TIB #20, Harbin, 150000, Hei Long Jiang, China.
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Li H, Pang Y, Liu B, Yu L. MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning. Front Pharmacol 2022; 13:856417. [PMID: 35350759 PMCID: PMC8957949 DOI: 10.3389/fphar.2022.856417] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/14/2022] [Indexed: 01/13/2023] Open
Abstract
Intrinsically disordered regions (IDRs) without stable structure are important for protein structures and functions. Some IDRs can be combined with molecular fragments to make itself completed the transition from disordered to ordered, which are called molecular recognition features (MoRFs). There are five main functions of MoRFs: molecular recognition assembler (MoR_assembler), molecular recognition chaperone (MoR_chaperone), molecular recognition display sites (MoR_display_sites), molecular recognition effector (MoR_effector), and molecular recognition scavenger (MoR_scavenger). Researches on functions of molecular recognition features are important for pharmaceutical and disease pathogenesis. However, the existing computational methods can only predict the MoRFs in proteins, failing to distinguish their different functions. In this paper, we treat MoRF function prediction as a multi-label learning task and solve it with the Binary Relevance (BR) strategy. Finally, we use Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as basic models to construct MoRF-FUNCpred through ensemble learning. Experimental results show that MoRF-FUNCpred performs well for MoRF function prediction. To the best knowledge of ours, MoRF-FUNCpred is the first predictor for predicting the functions of MoRFs. Availability and Implementation: The stand alone package of MoRF-FUNCpred can be accessed from https://github.com/LiangYu-Xidian/MoRF-FUNCpred.
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Affiliation(s)
- Haozheng Li
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yihe Pang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.,Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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Ahmed Z, Zulfiqar H, Khan AA, Gul I, Dao FY, Zhang ZY, Yu XL, Tang L. iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy. Front Microbiol 2022; 13:790063. [PMID: 35273581 PMCID: PMC8902591 DOI: 10.3389/fmicb.2022.790063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/10/2022] [Indexed: 01/20/2023] Open
Abstract
Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.
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Affiliation(s)
- Zahoor Ahmed
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Abdullah Aman Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Sichuan Artificial Intelligence Research Institute, Yibin, China
| | - Ijaz Gul
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Tsinghua Shenzhen International Graduate School, Institute of Biopharmaceutical and Health Engineering, Tsinghua University, Shenzhen, China
| | - Fu-Ying Dao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou, China
| | - Lixia Tang
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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