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Chen Y, Du Z, Ren X, Pan C, Zhu Y, Li Z, Meng T, Yao X. mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization. Methods 2024; 227:17-26. [PMID: 38705502 DOI: 10.1016/j.ymeth.2024.04.018] [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: 12/22/2023] [Revised: 03/30/2024] [Accepted: 04/28/2024] [Indexed: 05/07/2024] Open
Abstract
Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.
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Affiliation(s)
- Yifan Chen
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Xuanbai Ren
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Chu Pan
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Yangbin Zhu
- Manufacturing and Electronic Engineering, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Zhen Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Tao Meng
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao.
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Li J, Sun L, Liu L, Li Z. MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism. Front Genet 2024; 15:1381997. [PMID: 38770418 PMCID: PMC11102998 DOI: 10.3389/fgene.2024.1381997] [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: 02/04/2024] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.
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Affiliation(s)
- Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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Zhong B, Dai Y, Chen L, Xu X, Lan Y, Deng L, Ren L, Luo N, Ning L. ncRS: A resource of non-coding RNAs in sepsis. Comput Biol Med 2024; 172:108256. [PMID: 38489989 DOI: 10.1016/j.compbiomed.2024.108256] [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: 11/18/2023] [Revised: 02/10/2024] [Accepted: 03/06/2024] [Indexed: 03/17/2024]
Abstract
Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.
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Affiliation(s)
- Baocai Zhong
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China
| | - Yongfang Dai
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China
| | - Li Chen
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China.
| | - Xinying Xu
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Yuxi Lan
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Leyao Deng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Nanchao Luo
- School of Computer Science and Technology, A Ba Teachers University, Wenchuan, China.
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
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Zhou Z, Zhuo L, Fu X, Lv J, Zou Q, Qi R. Joint masking and self-supervised strategies for inferring small molecule-miRNA associations. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102103. [PMID: 38261851 PMCID: PMC10794920 DOI: 10.1016/j.omtn.2023.102103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.
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Affiliation(s)
- Zhecheng Zhou
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325027, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325027, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
| | - Juan Lv
- College of Traditional Chinese Medicine, Changsha Medical University, Changsha 410000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611730, China
| | - Ren Qi
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Peng L, Yang Y, Yang C, Li Z, Cheong N. HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4814-4834. [PMID: 38872515 DOI: 10.3934/mbe.2024212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.
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Affiliation(s)
- Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
- Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yujie Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Cheng Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Ngai Cheong
- Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China
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Zhuo L, Wang R, Fu X, Yao X. StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning. BMC Genomics 2023; 24:742. [PMID: 38053026 DOI: 10.1186/s12864-023-09802-7] [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: 07/26/2023] [Accepted: 11/11/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a more precise DNA methylation prediction. However, issues related to the stability and generalization performance of these models persist. RESULTS In this study, we introduce an efficient and stable DNA methylation prediction model. This model incorporates a feature fusion approach, adaptive feature correction technology, and a contrastive learning strategy. The proposed model presents several advantages. First, DNA sequences are encoded at four levels to comprehensively capture intricate information across multi-scale and low-span features. Second, we design a sequence-specific feature correction module that adaptively adjusts the weights of sequence features. This improvement enhances the model's stability and scalability, or its generality. Third, our contrastive learning strategy mitigates the instability issues resulting from sparse data. To validate our model, we conducted multiple sets of experiments on commonly used datasets, demonstrating the model's robustness and stability. Simultaneously, we amalgamate various datasets into a single, unified dataset. The experimental outcomes from this combined dataset substantiate the model's robust adaptability. CONCLUSIONS Our research findings affirm that the StableDNAm model is a general, stable, and effective instrument for DNA methylation prediction. It holds substantial promise for providing invaluable assistance in future methylation-related research and analyses.
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Affiliation(s)
- Linlin Zhuo
- College of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China
| | - Rui Wang
- College of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China.
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
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Fu X, Chen Y, Tian S. DlncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20648-20667. [PMID: 38124569 DOI: 10.3934/mbe.2023913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The prediction of long non-coding RNA (lncRNA) subcellular localization is essential to the understanding of its function and involvement in cellular regulation. Traditional biological experimental methods are costly and time-consuming, making computational methods the preferred approach for predicting lncRNA subcellular localization (LSL). However, existing computational methods have limitations due to the structural characteristics of lncRNAs and the uneven distribution of data across subcellular compartments. We propose a discrete wavelet transform (DWT)-based model for predicting LSL, called DlncRNALoc. We construct a physicochemical property matrix of a 2-tuple bases based on lncRNA sequences, and we introduce a DWT lncRNA feature extraction method. We use the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling and the local fisher discriminant analysis (LFDA) algorithm to optimize feature information. The optimized feature vectors are fed into support vector machine (SVM) to construct a predictive model. DlncRNALoc has been applied for a five-fold cross-validation on the three sets of benchmark datasets. Extensive experiments have demonstrated the superiority and effectiveness of the DlncRNALoc model in predicting LSL.
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Affiliation(s)
- Xiangzheng Fu
- Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, China
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
- Department of Basic Biology, Changsha Medical College, Changsha, Hunan, China
| | - Yifan Chen
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
- Department of Basic Biology, Changsha Medical College, Changsha, Hunan, China
| | - Sha Tian
- Department of Internal Medicine, College of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
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