1
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Ma Y, Ma Y. Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations. PLoS Comput Biol 2024; 20:e1012287. [PMID: 38976761 PMCID: PMC11257412 DOI: 10.1371/journal.pcbi.1012287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 07/18/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.
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Affiliation(s)
- Yingjun Ma
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China
| | - Yuanyuan Ma
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China
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2
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [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: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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3
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He J, Li M, Qiu J, Pu X, Guo Y. HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network. J Chem Inf Model 2024; 64:2863-2877. [PMID: 37604142 DOI: 10.1021/acs.jcim.3c00856] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.
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Affiliation(s)
- Jian He
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Jiangguo Qiu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
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4
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Zhang Y, Chu Y, Lin S, Xiong Y, Wei DQ. ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler. Brief Bioinform 2024; 25:bbae103. [PMID: 38517693 PMCID: PMC10959163 DOI: 10.1093/bib/bbae103] [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: 11/07/2023] [Revised: 02/04/2024] [Accepted: 02/23/2024] [Indexed: 03/24/2024] Open
Abstract
Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.
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Affiliation(s)
- Yufang Zhang
- School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
| | - Yanyi Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
| | - Dong-Qing Wei
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
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5
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Gogoshin G, Rodin AS. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends. Cancers (Basel) 2023; 15:5858. [PMID: 38136405 PMCID: PMC10742144 DOI: 10.3390/cancers15245858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.
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Affiliation(s)
- Grigoriy Gogoshin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Andrei S. Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
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6
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Hu H, Zhao H, Zhong T, Dong X, Wang L, Han P, Li Z. Adaptive deep propagation graph neural network for predicting miRNA-disease associations. Brief Funct Genomics 2023; 22:453-462. [PMID: 37078739 DOI: 10.1093/bfgp/elad010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/13/2023] [Accepted: 03/09/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations. RESULTS In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs. CONCLUSION Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.
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Affiliation(s)
- Hua Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
| | - Huan Zhao
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China
| | - Tangbo Zhong
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China
| | - Xishang Dong
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 541006, China
| | - Pengyong Han
- Central Lab, Changzhi Medical College, Changzhi 046012, China
| | - Zhengwei Li
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 541006, China
- KUNPAND Communications (Kunshan) Co., Ltd., Suzhou 215300, China
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7
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Ma Z, Kuang Z, Deng L. NGCICM: A Novel Deep Learning-Based Method for Predicting circRNA-miRNA Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3080-3092. [PMID: 37027645 DOI: 10.1109/tcbb.2023.3248787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The circRNAs and miRNAs play an important role in the development of human diseases, and they can be widely used as biomarkers of diseases for disease diagnosis. In particular, circRNAs can act as sponge adsorbers for miRNAs and act together in certain diseases. However, the associations between the vast majority of circRNAs and diseases and between miRNAs and diseases remain unclear. Computational-based approaches are urgently needed to discover the unknown interactions between circRNAs and miRNAs. In this paper, we propose a novel deep learning algorithm based on Node2vec and Graph ATtention network (GAT), Conditional Random Field (CRF) layer and Inductive Matrix Completion (IMC) to predict circRNAs and miRNAs interactions (NGCICM). We construct a GAT-based encoder for deep feature learning by fusing the talking-heads attention mechanism and the CRF layer. The IMC-based decoder is also constructed to obtain interaction scores. The Area Under the receiver operating characteristic Curve (AUC) of the NGCICM method is 0.9697, 0.9932 and 0.9980, and the Area Under the Precision-Recall curve (AUPR) is 0.9671, 0.9935 and 0.9981, respectively, using 2-fold, 5-fold and 10-fold Cross-Validation (CV) as the benchmark. The experimental results confirm the effectiveness of the NGCICM algorithm in predicting the interactions between circRNAs and miRNAs.
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8
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He Q, Qiao W, Fang H, Bao Y. Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks. Brief Bioinform 2023; 24:bbad203. [PMID: 37287133 DOI: 10.1093/bib/bbad203] [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: 02/13/2023] [Revised: 04/24/2023] [Accepted: 05/10/2023] [Indexed: 06/09/2023] Open
Abstract
MicroRNAs (miRNAs) are a family of non-coding RNA molecules with vital roles in regulating gene expression. Although researchers have recognized the importance of miRNAs in the development of human diseases, it is very resource-consuming to use experimental methods for identifying which dysregulated miRNA is associated with a specific disease. To reduce the cost of human effort, a growing body of studies has leveraged computational methods for predicting the potential miRNA-disease associations. However, the extant computational methods usually ignore the crucial mediating role of genes and suffer from the data sparsity problem. To address this limitation, we introduce the multi-task learning technique and develop a new model called MTLMDA (Multi-Task Learning model for predicting potential MicroRNA-Disease Associations). Different from existing models that only learn from the miRNA-disease network, our MTLMDA model exploits both miRNA-disease and gene-disease networks for improving the identification of miRNA-disease associations. To evaluate model performance, we compare our model with competitive baselines on a real-world dataset of experimentally supported miRNA-disease associations. Empirical results show that our model performs best using various performance metrics. We also examine the effectiveness of model components via ablation study and further showcase the predictive power of our model for six types of common cancers. The data and source code are available from https://github.com/qwslle/MTLMDA.
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Affiliation(s)
- Qiang He
- College of Medicine and Biological Information Engineering, Northeastern University, 110169 Shenyang, China
| | - Wei Qiao
- College of Medicine and Biological Information Engineering, Northeastern University, 110169 Shenyang, China
| | - Hui Fang
- Research Institute for Interdisciplinary Science and School of Information Management and Engineering, Shanghai University of Finance and Economics, 200434 Shanghai, China
| | - Yang Bao
- Antai College of Economics and Management, Shanghai Jiao Tong University, 200030 Shanghai, China
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Bai T, Yan K, Liu B. DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks. Brief Bioinform 2023:bbad212. [PMID: 37332057 DOI: 10.1093/bib/bbad212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 06/20/2023] Open
Abstract
MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA-disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA-disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA-disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA-disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet.
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Affiliation(s)
- Tao Bai
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
- School of Mathematics & Computer Science, Yan'an University, Shaanxi 716000, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
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10
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Brief Bioinform 2022; 23:6712303. [PMID: 36151749 DOI: 10.1093/bib/bbac407] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/11/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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11
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Li L, Gao Z, Zheng CH, Qi R, Wang YT, Ni JC. Predicting miRNA-Disease Association Based on Improved Graph Regression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3604-3613. [PMID: 34757912 DOI: 10.1109/tcbb.2021.3127017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.
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12
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23:6686738. [PMID: 36056743 DOI: 10.1093/bib/bbac358] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/24/2022] [Accepted: 07/30/2022] [Indexed: 12/12/2022] Open
Abstract
Since the problem proposed in late 2000s, microRNA-disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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13
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Li G, Fang T, Zhang Y, Liang C, Xiao Q, Luo J. Predicting miRNA-disease associations based on graph attention network with multi-source information. BMC Bioinformatics 2022; 23:244. [PMID: 35729531 PMCID: PMC9215044 DOI: 10.1186/s12859-022-04796-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is a growing body of evidence from biological experiments suggesting that microRNAs (miRNAs) play a significant regulatory role in both diverse cellular activities and pathological processes. Exploring miRNA-disease associations not only can decipher pathogenic mechanisms but also provide treatment solutions for diseases. As it is inefficient to identify undiscovered relationships between diseases and miRNAs using biotechnology, an explosion of computational methods have been advanced. However, the prediction accuracy of existing models is hampered by the sparsity of known association network and single-category feature, which is hard to model the complicated relationships between diseases and miRNAs. RESULTS In this study, we advance a new computational framework (GATMDA) to discover unknown miRNA-disease associations based on graph attention network with multi-source information, which effectively fuses linear and non-linear features. In our method, the linear features of diseases and miRNAs are constructed by disease-lncRNA correlation profiles and miRNA-lncRNA correlation profiles, respectively. Then, the graph attention network is employed to extract the non-linear features of diseases and miRNAs by aggregating information of each neighbor with different weights. Finally, the random forest algorithm is applied to infer the disease-miRNA correlation pairs through fusing linear and non-linear features of diseases and miRNAs. As a result, GATMDA achieves impressive performance: an average AUC of 0.9566 with five-fold cross validation, which is superior to other previous models. In addition, case studies conducted on breast cancer, colon cancer and lymphoma indicate that 50, 50 and 48 out of the top fifty prioritized candidates are verified by biological experiments. CONCLUSIONS The extensive experimental results justify the accuracy and utility of GATMDA and we could anticipate that it may regard as a utility tool for identifying unobserved disease-miRNA relationships.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China.
| | - Tao Fang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Yuejin Zhang
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
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Ni J, Li L, Wang Y, Ji C, Zheng C. MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA–Disease Association Prediction. Genes (Basel) 2022; 13:genes13061021. [PMID: 35741782 PMCID: PMC9223216 DOI: 10.3390/genes13061021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/16/2022] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that are related to a number of complicated biological processes, and numerous studies have demonstrated that miRNAs are closely associated with many human diseases. In this study, we present a matrix decomposition and similarity-constrained matrix factorization (MDSCMF) to predict potential miRNA–disease associations. First of all, we utilized a matrix decomposition (MD) algorithm to get rid of outliers from the miRNA–disease association matrix. Then, miRNA similarity was determined by utilizing similarity kernel fusion (SKF) to integrate miRNA function similarity and Gaussian interaction profile (GIP) kernel similarity, and disease similarity was determined by utilizing SKF to integrate disease semantic similarity and GIP kernel similarity. Furthermore, we added L2 regularization terms and similarity constraint terms to non-negative matrix factorization to form a similarity-constrained matrix factorization (SCMF) algorithm, which was applied to make prediction. MDSCMF achieved AUC values of 0.9488, 0.9540, and 0.8672 based on fivefold cross-validation (5-CV), global leave-one-out cross-validation (global LOOCV), and local leave-one-out cross-validation (local LOOCV), respectively. Case studies on three common human diseases were also implemented to demonstrate the prediction ability of MDSCMF. All experimental results confirmed that MDSCMF was effective in predicting underlying associations between miRNAs and diseases.
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Affiliation(s)
- Jiancheng Ni
- Network Information Center, Qufu Normal University, Qufu 273165, China;
| | - Lei Li
- School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, China; (Y.W.); (C.J.)
- Correspondence: (L.L.); (C.Z.)
| | - Yutian Wang
- School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, China; (Y.W.); (C.J.)
| | - Cunmei Ji
- School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, China; (Y.W.); (C.J.)
| | - Chunhou Zheng
- School of Artifial Intelligence, Anhui University, Hefei 230601, China
- Correspondence: (L.L.); (C.Z.)
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15
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Li Z, Zhong T, Huang D, You ZH, Nie R. Hierarchical graph attention network for miRNA-disease association prediction. Mol Ther 2022; 30:1775-1786. [PMID: 35121109 PMCID: PMC9077381 DOI: 10.1016/j.ymthe.2022.01.041] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/29/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022] Open
Abstract
Many biological studies show that the mutation and abnormal expression of microRNAs (miRNAs) could cause a variety of diseases. As an important biomarker for disease diagnosis, miRNA is helpful to understand pathogenesis, and could promote the identification, diagnosis and treatment of diseases. However, the pathogenic mechanism how miRNAs affect these diseases has not been fully understood. Therefore, predicting the potential miRNA-disease associations is of great importance for the development of clinical medicine and drug research. In this study, we proposed a novel deep learning model based on hierarchical graph attention network for predicting miRNA-disease associations (HGANMDA). Firstly, we constructed a miRNA-disease-lncRNA heterogeneous graph based on known miRNA-disease associations, miRNA-lncRNA associations and disease-lncRNA associations. Secondly, the node-layer attention was applied to learn the importance of neighbor nodes based on different meta-paths. Thirdly, the semantic-layer attention was applied to learn the importance of different meta-paths. Finally, a bilinear decoder was employed to reconstruct the connections between miRNAs and diseases. The extensive experimental results indicated that our model achieved good performance and satisfactory results in predicting miRNA-disease associations.
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16
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Wang YT, Li L, Ji CM, Zheng CH, Ni JC. ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation. Front Genet 2021; 12:743665. [PMID: 34659364 PMCID: PMC8514753 DOI: 10.3389/fgene.2021.743665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 08/30/2021] [Indexed: 12/21/2022] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.
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Affiliation(s)
- Yu-Tian Wang
- School of Cyber Science and Engineering, Qufu Normal University, Qufu, China
| | - Lei Li
- School of Cyber Science and Engineering, Qufu Normal University, Qufu, China
| | - Cun-Mei Ji
- School of Cyber Science and Engineering, Qufu Normal University, Qufu, China
| | - Chun-Hou Zheng
- School of Artificial Intelligence, Anhui University, Hefei, China
| | - Jian-Cheng Ni
- School of Cyber Science and Engineering, Qufu Normal University, Qufu, China
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