1
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Clark AJ, Lillard JW. A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology. Genes (Basel) 2024; 15:1036. [PMID: 39202397 PMCID: PMC11353282 DOI: 10.3390/genes15081036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 09/03/2024] Open
Abstract
The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.
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Affiliation(s)
| | - James W. Lillard
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA;
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2
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Zhang M, Zhang L, Liu T, Feng H, He Z, Li F, Zhao J, Liu H. CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning. Sci Rep 2024; 14:17549. [PMID: 39080344 PMCID: PMC11289117 DOI: 10.1038/s41598-024-68750-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 07/26/2024] [Indexed: 08/02/2024] Open
Abstract
Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral-host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition-transition-distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein-human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage.
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Affiliation(s)
- Man Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, 110036, China
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Ting Liu
- School of Life Science, Liaoning University, Shenyang, 110036, China
- China Medical University-Queen's University Belfast Joint College, China Medical University, Shenyang, 110036, China
| | - Huawei Feng
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, 110036, China
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
- School of Pharmacy, Liaoning University, No. 66, Chongshan Zhonglu, Shenyang, 110036, Liaoning, China
| | - Zhe He
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Feng Li
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Jian Zhao
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Hongsheng Liu
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, 110036, China.
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China.
- School of Pharmacy, Liaoning University, No. 66, Chongshan Zhonglu, Shenyang, 110036, Liaoning, China.
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3
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Prabhu H, Bhosale H, Sane A, Dhadwal R, Ramakrishnan V, Valadi J. Protein feature engineering framework for AMPylation site prediction. Sci Rep 2024; 14:8695. [PMID: 38622194 PMCID: PMC11369087 DOI: 10.1038/s41598-024-58450-8] [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] [Received: 11/13/2023] [Accepted: 03/29/2024] [Indexed: 04/17/2024] Open
Abstract
AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.
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Affiliation(s)
- Hardik Prabhu
- Computing and Data Sciences, FLAME University, Pune, 412115, India
- Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science, Bengaluru, 560012, India
| | | | - Aamod Sane
- Computing and Data Sciences, FLAME University, Pune, 412115, India
| | - Renu Dhadwal
- Computing and Data Sciences, FLAME University, Pune, 412115, India
| | - Vigneshwar Ramakrishnan
- Bioinformatics Center, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, 613401, India
| | - Jayaraman Valadi
- Computing and Data Sciences, FLAME University, Pune, 412115, India.
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4
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Sun J, Qu J, Zhao C, Zhang X, Liu X, Wang J, Wei C, Liu X, Wang M, Zeng P, Tang X, Ling X, Qing L, Jiang S, Chen J, Chen TSR, Kuang Y, Gao J, Zeng X, Huang D, Yuan Y, Fan L, Yu H, Ding J. Precise prediction of phase-separation key residues by machine learning. Nat Commun 2024; 15:2662. [PMID: 38531854 DOI: 10.1038/s41467-024-46901-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.
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Affiliation(s)
- Jun Sun
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiale Qu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Cai Zhao
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyao Zhang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyu Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jia Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Chao Wei
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyi Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Mulan Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengguihang Zeng
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiuxiao Tang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoru Ling
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Li Qing
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shaoshuai Jiang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiahao Chen
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tara S R Chen
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yalan Kuang
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Jinhang Gao
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Dongfeng Huang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yong Yuan
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Lili Fan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Jinan University, Guangzhou, Guangdong, China.
| | - Haopeng Yu
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Junjun Ding
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China.
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5
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Zhang Y, Lang M, Jiang J, Gao Z, Xu F, Litfin T, Chen K, Singh J, Huang X, Song G, Tian Y, Zhan J, Chen J, Zhou Y. Multiple sequence alignment-based RNA language model and its application to structural inference. Nucleic Acids Res 2024; 52:e3. [PMID: 37941140 PMCID: PMC10783488 DOI: 10.1093/nar/gkad1031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/21/2023] [Indexed: 11/10/2023] Open
Abstract
Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.
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Affiliation(s)
- Yikun Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzen 518055, China
| | - Mei Lang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jiuhong Jiang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Zhiqiang Gao
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Fan Xu
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Thomas Litfin
- Institute for Glycomics, Griffith University, Parklands Dr, Southport, QLD 4215, Australia
| | - Ke Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jaswinder Singh
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | | | - Guoli Song
- Peng Cheng Laboratory, Shenzhen 518066, China
| | | | - Jian Zhan
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jie Chen
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Yaoqi Zhou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
- Institute for Glycomics, Griffith University, Parklands Dr, Southport, QLD 4215, Australia
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6
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Qiu S, Liu R, Liang Y. GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network. Comput Biol Med 2023; 163:107202. [PMID: 37450964 DOI: 10.1016/j.compbiomed.2023.107202] [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] [Received: 05/08/2023] [Revised: 06/14/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023]
Abstract
RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional modification on the surface of the molecule, which frequently plays a crucial role in biological processes. Biological experimental methods to identify m6A have been studied and implemented in recent years, but they cannot be promoted widely due to drawbacks such as the time and cost of reagents and equipment. Therefore, researchers have proposed computational strategies for identifying m6A sites, but these strategies do not account for the mechanism of methylation occurrence or the structure of RNA molecules. This study, therefore, proposed a novel deep learning model for predicting m6A sites, GR-m6A, which predicts m6A sites by extracting features from the physicochemical properties and spatial structure of molecules via residual networks. In GR-m6A, each RNA base string is represented by SMILES as two matrices comprising topology structural information and node attributes with molecular physicochemical characteristics. The feature encoding matrix was then obtained by fusing the topology matrix and the node matrix in accordance with the graphical convolutional network principle. Correspondingly, the more discriminative features were extracted from the encoding matrix using the residual neural network and predicted using a multilayer perceptron. As evident from the 5-fold cross-validation and independent validation, the GR-m6A model outperformed other existing methods. Thus, we hope that GR-m6A can aid researchers in predicting mammalian m6A loci. The source code and database are available at https://github.com/YingLiangjxau/GR-m6A.
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Affiliation(s)
- Shi Qiu
- College of Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
| | - Renxin Liu
- College of Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
| | - Ying Liang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
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7
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Wei MM, Yu CQ, Li LP, You ZH, Ren ZH, Guan YJ, Wang XF, Li YC. LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model. Front Genet 2023; 14:1122909. [PMID: 36845392 PMCID: PMC9950107 DOI: 10.3389/fgene.2023.1122909] [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: 12/13/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.
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Affiliation(s)
- Meng-Meng Wei
- School of Information Engineering, Xijing University, Xi’an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an, China,*Correspondence: Chang-Qing Yu, ; Li-Ping Li,
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi’an, China,College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, China,*Correspondence: Chang-Qing Yu, ; Li-Ping Li,
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an, China
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an, China
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8
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Zhao J, Sun J, Shuai SC, Zhao Q, Shuai J. Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. Brief Bioinform 2023; 24:6896030. [PMID: 36515153 DOI: 10.1093/bib/bbac527] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/23/2022] [Accepted: 11/06/2022] [Indexed: 12/15/2022] Open
Abstract
Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.
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Affiliation(s)
- Jingxuan Zhao
- University of Science and Technology Liaoning, 66459, Anshan, China
| | | | - Stella C Shuai
- Northwestern University, 3270, Evanston, IllinoisUnited States
| | - Qi Zhao
- University of Science and Technology Liaoning, 66459, Anshan, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China
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9
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Nguyen MT, Nguyen T, Tran T. Learning to discover medicines. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022; 16:1-16. [PMID: 36440369 PMCID: PMC9676887 DOI: 10.1007/s41060-022-00371-8] [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: 06/09/2022] [Accepted: 11/05/2022] [Indexed: 11/19/2022]
Abstract
Discovering new medicines is the hallmark of the human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper, we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature on AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.
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Affiliation(s)
- Minh-Tri Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC Australia
| | - Thin Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC Australia
| | - Truyen Tran
- Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC Australia
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10
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Li W, Wang S, Xu J, Xiang J. Inferring Latent MicroRNA-Disease Associations on a Gene-Mediated Tripartite Heterogeneous Multiplexing Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3190-3201. [PMID: 35041612 DOI: 10.1109/tcbb.2022.3143770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.
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11
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Pepe G, Appierdo R, Carrino C, Ballesio F, Helmer-Citterich M, Gherardini PF. Artificial intelligence methods enhance the discovery of RNA interactions. Front Mol Biosci 2022; 9:1000205. [PMID: 36275611 PMCID: PMC9585310 DOI: 10.3389/fmolb.2022.1000205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.
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Affiliation(s)
- G Pepe
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
- *Correspondence: G Pepe, ; M Helmer-Citterich,
| | - R Appierdo
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - C Carrino
- PhD Program in Cellular and Molecular Biology, Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - F Ballesio
- PhD Program in Cellular and Molecular Biology, Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - M Helmer-Citterich
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
- *Correspondence: G Pepe, ; M Helmer-Citterich,
| | - PF Gherardini
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
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12
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Sun Y, Xiong F, Sun Y, Zhao Y, Cao Y. A miRNA Target Prediction Model Based on Distributed Representation Learning and Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4490154. [PMID: 35924115 PMCID: PMC9343202 DOI: 10.1155/2022/4490154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/16/2022] [Accepted: 06/07/2022] [Indexed: 11/18/2022]
Abstract
MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the nucleotide sequence. In this paper, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site-level prediction, which is inspired by the similarity between natural language and biological sequences. First, the word2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the embedding is extracted automatically via the stacked bidirectional long short-term memory (BiLSTM) network. By testing, our method can effectively improve the accuracy, sensitivity, specificity, and F-measure of other methods. Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem.
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Affiliation(s)
- Yuzhuo Sun
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
| | - Fei Xiong
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
| | - Yongke Sun
- College of Material Science and Engineering, Southwest Forestry University, Kunming, China
| | - Youjie Zhao
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
| | - Yong Cao
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
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13
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Ren ZH, Yu CQ, Li LP, You ZH, Guan YJ, Li YC, Pan J. SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information. Front Genet 2022; 13:839540. [PMID: 35360836 PMCID: PMC8963817 DOI: 10.3389/fgene.2022.839540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Non-coding RNAs (ncRNAs) take essential effects on biological processes, like gene regulation. One critical way of ncRNA executing biological functions is interactions between ncRNA and RNA binding proteins (RBPs). Identifying proteins, involving ncRNA-protein interactions, can well understand the function ncRNA. Many high-throughput experiment have been applied to recognize the interactions. As a consequence of these approaches are time- and labor-consuming, currently, a great number of computational methods have been developed to improve and advance the ncRNA-protein interactions research. However, these methods may be not available to all RNAs and proteins, particularly processing new RNAs and proteins. Additionally, most of them cannot process well with long sequence. In this work, a computational method SAWRPI is proposed to make prediction of ncRNA-protein through sequence information. More specifically, the raw features of protein and ncRNA are firstly extracted through the k-mer sparse matrix with SVD reduction and learning nucleic acid symbols by natural language processing with local fusion strategy, respectively. Then, to classify easily, Hilbert Transformation is exploited to transform raw feature data to the new feature space. Finally, stacking ensemble strategy is adopted to learn high-level abstraction features automatically and generate final prediction results. To confirm the robustness and stability, three different datasets containing two kinds of interactions are utilized. In comparison with state-of-the-art methods and other results classifying or feature extracting strategies, SAWRPI achieved high performance on three datasets, containing two kinds of lncRNA-protein interactions. Upon our finding, SAWRPI is a trustworthy, robust, yet simple and can be used as a beneficial supplement to the task of predicting ncRNA-protein interactions.
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Affiliation(s)
- Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an, China
- *Correspondence: Li-Ping Li, ; Chang-Qing Yu,
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi’an, China
- *Correspondence: Li-Ping Li, ; Chang-Qing Yu,
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an, China
| | - Yue-Chao Li
- School of Information Engineering, Xijing University, Xi’an, China
| | - Jie Pan
- School of Information Engineering, Xijing University, Xi’an, China
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14
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Wang X, Wang C, Li L, Ma Q, Ma A, Liu B. DESSO-DB: A web database for sequence and shape motif analyses and identification. Comput Struct Biotechnol J 2022; 20:3053-3058. [PMID: 35782725 PMCID: PMC9233226 DOI: 10.1016/j.csbj.2022.06.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Xiaoying Wang
- Department of Mathematics, Shandong University, Shandong, China
| | - Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Corresponding authors.
| | - Bingqiang Liu
- Department of Mathematics, Shandong University, Shandong, China
- Corresponding authors.
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15
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LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification. BMC Bioinformatics 2021; 22:568. [PMID: 34836494 PMCID: PMC8620196 DOI: 10.1186/s12859-021-04485-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/09/2021] [Indexed: 12/03/2022] Open
Abstract
Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04485-x.
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16
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Yu H, Shen ZA, Du PF. NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder. IEEE J Biomed Health Inform 2021; 26:1861-1871. [PMID: 34699377 DOI: 10.1109/jbhi.2021.3122527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).
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17
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Yu H, Shen ZA, Zhou YK, Du PF. Recent advances in predicting protein-lncRNA interactions using machine learning methods. Curr Gene Ther 2021; 22:228-244. [PMID: 34254917 DOI: 10.2174/1566523221666210712190718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/01/2021] [Accepted: 05/31/2021] [Indexed: 11/22/2022]
Abstract
Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.
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Affiliation(s)
- Han Yu
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Zi-Ang Shen
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yuan-Ke Zhou
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
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18
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Yi HC, You ZH, Wang L, Su XR, Zhou X, Jiang TH. In silico drug repositioning using deep learning and comprehensive similarity measures. BMC Bioinformatics 2021; 22:293. [PMID: 34074242 PMCID: PMC8170943 DOI: 10.1186/s12859-020-03882-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. METHODS In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions. RESULTS The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. CONCLUSION The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.
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Affiliation(s)
- 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
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
| | - Lei Wang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Tong-Hai Jiang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
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19
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Chen YZ, Wang ZZ, Wang Y, Ying G, Chen Z, Song J. nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning. Brief Bioinform 2021; 22:6277413. [PMID: 34002774 DOI: 10.1093/bib/bbab146] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/18/2021] [Accepted: 03/25/2021] [Indexed: 12/20/2022] Open
Abstract
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.
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Affiliation(s)
- Yong-Zi Chen
- Laboratory of Tumor Cell Biology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | | | | | - Guoguang Ying
- Laboratory of Tumor Cell Biology in Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Australia
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20
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Li Y, Sun H, Feng S, Zhang Q, Han S, Du W. Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network. BMC Bioinformatics 2021; 22:246. [PMID: 33985444 PMCID: PMC8120853 DOI: 10.1186/s12859-021-04171-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 05/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA-protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. RESULTS We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. CONCLUSIONS This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.
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Affiliation(s)
- Ying Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Hang Sun
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Shiyao Feng
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Qi Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Siyu Han
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
- Department of Computer Science, Faculty of Engineering, University of Bristol, Bristol, BS8 1UB, UK
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China.
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21
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Fan BL, Jiang Z, Sun J, Liu R. Systematic characterization and prediction of coenzyme A-associated proteins using sequence and network information. Brief Bioinform 2020; 22:6012866. [PMID: 33253385 DOI: 10.1093/bib/bbaa308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/08/2020] [Accepted: 10/12/2020] [Indexed: 01/11/2023] Open
Abstract
Coenzyme A-associated proteins (CAPs) are a category of functionally important proteins involved in multiple biological processes through interactions with coenzyme A (CoA). To date, unfortunately, the specific differences between CAPs and other proteins have yet to be systemically investigated. Moreover, there are no computational methods that can be used specifically to predict these proteins. Herein, we characterized CAPs from multifaceted viewpoints and revealed their specific preferences. Compared with other proteins, CAPs were more likely to possess binding regions for CoA and its derivatives, were evolutionarily highly conserved, exhibited ordered and hydrophobic structural conformations, and tended to be densely located in protein-protein interaction networks. Based on these biological insights, we built seven classifiers using predicted CoA-binding residue distributions, word embedding vectors, remote homolog numbers, evolutionary conservation, amino acid composition, predicted structural features and network properties. These classifiers could effectively identify CAPs in Homo sapiens, Mus musculus and Arabidopsis thaliana. The complementarity among the individual classifiers prompted us to build a two-layer stacking model named CAPE for improving prediction performance. We applied CAPE to identify some high-confidence candidates in the three species, which were tightly associated with the known functions of CAPs. Finally, we extended our algorithm to cross-species prediction, thereby developing a generic CAP prediction model. In summary, this work provides a comprehensive survey and an effective predictor for CAPs, which can help uncover the interplay between CoA and functionally relevant proteins.
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Affiliation(s)
- Bing-Liang Fan
- College of Informatics, Huazhong Agricultural University
| | - Zheng Jiang
- College of Informatics, Huazhong Agricultural University
| | - Jun Sun
- College of Informatics, Huazhong Agricultural University
| | - Rong Liu
- College of Informatics, Huazhong Agricultural University
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Li J, Shi X, You ZH, Yi HC, Chen Z, Lin Q, Fang M. Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1546-1554. [PMID: 31940546 DOI: 10.1109/tcbb.2020.2965919] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost and time-consuming. To solve the above problems, computational methods are developed to predict PPIs. Thus, in this paper, we present a method to predict PPIs using protein sequences. First, protein sequences are transformed into Position Weight Matrix (PWM), in which Scale-Invariant Feature Transform (SIFT) algorithm is used to extract features. Then Principal Component Analysis (PCA) is applied to reduce the dimension of features. At last, Weighted Extreme Learning Machine (WELM) classifier is employed to predict PPIs and a series of evaluation results are obtained. In our method, since SIFT and WELM are used to extract features and classify respectively, we called the proposed method SIFT-WELM. When applying the proposed method on three well-known PPIs datasets of Yeast, Human and Helicobacter.pylori, the average accuracies of our method using five-fold cross validation are obtained as high as 94.83, 97.60 and 83.64 percent, respectively. In order to evaluate the proposed approach properly, we compare it with Support Vector Machine (SVM) classifier and other recent-developed methods in different aspects. Moreover, the training time of our method is greatly shortened, which is obviously superior to the previous methods, such as SVM, ACC, PCVMZM and so on.
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Qi FF, Yang Y, Zhang H, Chen H. Long non-coding RNAs: Key regulators in oxaliplatin resistance of colorectal cancer. Biomed Pharmacother 2020; 128:110329. [PMID: 32502843 DOI: 10.1016/j.biopha.2020.110329] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/19/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies in the world with high relapse and mortality rates. Although oxaliplatin (OXA), a platinum-based anticancer drug, is widely used in CRC treatment, the resulting chemoresistance dramatically attenuates the drug efficacy and increases the failure rate of this therapy. Thus, the study on OXA-induced chemoresistance is extremely urgent. In recent years, emerging evidence has shown that lncRNAs play irreplaceable roles in drug resistance. However, we only have a limited knowledge of the lncRNAs that are closely related to oxaliplatin resistance in CRC. In present study, we identify and characterize these lncRNAs, including their functions, underlying mechanisms and possible applications.
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Affiliation(s)
- Fang-Fang Qi
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Yunyao Yang
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Haowen Zhang
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Hongping Chen
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Jiangxi Key Laboratory of Experimental Animals, Nanchang University, Nanchang, Jiangxi 330006, PR China.
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Yi HC, You ZH, Wang MN, Guo ZH, Wang YB, Zhou JR. RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. BMC Bioinformatics 2020; 21:60. [PMID: 32070279 PMCID: PMC7029608 DOI: 10.1186/s12859-020-3406-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 02/11/2020] [Indexed: 01/03/2023] Open
Abstract
Background The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. Results In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. Conclusions The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It’s anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.
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Affiliation(s)
- Hai-Cheng Yi
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Mei-Neng Wang
- School of Mathematics and Computer Science, Yichun University, Yichun, 336000, China
| | - Zhen-Hao Guo
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yan-Bin Wang
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Ji-Ren Zhou
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
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