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Yao D, Zhang B, Zhan X, Zhang B, Li XK. Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration. ACS OMEGA 2024; 9:35100-35112. [PMID: 39157140 PMCID: PMC11325412 DOI: 10.1021/acsomega.4c05365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/04/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024]
Abstract
Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.
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Affiliation(s)
- Dengju Yao
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Binbin Zhang
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Xiaojuan Zhan
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
- College
of Computer Science and Technology, Heilongjiang
Institute of Technology, Harbin 150050, China
| | - Bo Zhang
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Xiang Kui Li
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
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Wang XF, Yu CQ, You ZH, Wang Y, Huang L, Qiao Y, Wang L, Li ZW. BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks. BMC Bioinformatics 2024; 25:264. [PMID: 39127625 DOI: 10.1186/s12859-024-05891-7] [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: 08/10/2023] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.
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Affiliation(s)
- Xin-Fei Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
- School of Artificial Intelligence, Jilin University, Changchun, China.
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yan Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang, China
| | - Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
- Guangxi Academy of Sciences, Nanning, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
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Si Y, Huang Z, Fang Z, Yuan Z, Huang Z, Li Y, Wei Y, Wu F, Yao YF. Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks. Brief Bioinform 2024; 25:bbae443. [PMID: 39256197 PMCID: PMC11387071 DOI: 10.1093/bib/bbae443] [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: 03/03/2024] [Revised: 08/11/2024] [Accepted: 08/30/2024] [Indexed: 09/12/2024] Open
Abstract
Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.
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Affiliation(s)
- Yuxuan Si
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road, 310016 Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Zihan Huang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Zhengqing Fang
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road, 310016 Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Zhouhang Yuan
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road, 310016 Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Yingming Li
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Ying Wei
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Fei Wu
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027 Zhejiang, China
| | - Yu-Feng Yao
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road, 310016 Zhejiang, China
- Department of Ophthalmology, The Fourth Affiliated Hospital of Soochow University, 215000 Suzhou, China
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Diao B, Luo J, Guo Y. A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs. Brief Funct Genomics 2024; 23:314-324. [PMID: 38576205 DOI: 10.1093/bfgp/elae010] [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: 12/06/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.
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Affiliation(s)
- Biyu Diao
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Jin Luo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Yu Guo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
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Peng L, Ren M, Huang L, Chen M. GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network. Interdiscip Sci 2024; 16:418-438. [PMID: 38733474 DOI: 10.1007/s12539-024-00619-w] [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: 11/18/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 05/13/2024]
Abstract
Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.
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Affiliation(s)
- Lihong Peng
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Mengnan Ren
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Liangliang Huang
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, China.
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Chen Q, Qiu J, Lan W, Cao J. Similarity-guided graph contrastive learning for lncRNA-disease association prediction. J Mol Biol 2024:168609. [PMID: 38750722 DOI: 10.1016/j.jmb.2024.168609] [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: 03/26/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.
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Affiliation(s)
- Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junlai Qiu
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Wei Lan
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junyue Cao
- College of Life Science and Technology, Guangxi University, Nanning 530004, Guangxi, China.
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Yao D, Li B, Zhan X, Zhan X, Yu L. GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations. BMC Bioinformatics 2024; 25:5. [PMID: 38166659 PMCID: PMC10763317 DOI: 10.1186/s12859-023-05625-1] [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: 10/22/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs. METHOD In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations. RESULTS The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer. CONCLUSIONS The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Bailin Li
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
| | - Xiaojuan Zhan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, 150050, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South, University of Science and Technology, Shenzhen, 518055, China
| | - Liyang Yu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
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