1
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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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2
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Huang ZA, Liu R, Zhu Z, Tan KC. Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:8161-8175. [PMID: 36459608 DOI: 10.1109/tnnls.2022.3225179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.
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3
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Li YC, You ZH, Yu CQ, Wang L, Hu L, Hu PW, Qiao Y, Wang XF, Huang YA. DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. Brief Funct Genomics 2024; 23:276-285. [PMID: 37539561 DOI: 10.1093/bfgp/elad030] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/25/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023] Open
Abstract
Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.
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Affiliation(s)
- Yue-Chao Li
- School of Information Engineering, Xijing University, Xi'an, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, China
| | - Lei Wang
- Guangxi Academy of Sciences, Nanning, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Yan Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Xin-Fei Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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4
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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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5
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Han S, Li Y, Gao J. Peripheral blood MicroRNAs as biomarkers of schizophrenia: expectations from a meta-analysis that combines deep learning methods. World J Biol Psychiatry 2024; 25:65-81. [PMID: 37703215 DOI: 10.1080/15622975.2023.2258975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/13/2023] [Accepted: 09/11/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVES This study aimed at identifying reliable differentially expressed miRNAs (DEMs) for schizophrenia in blood via meta-analyses combined with deep learning methods. METHODS First, we meta-analysed published DEMs. Then, we enriched the pool of schizophrenia-associated miRNAs by applying two computational learning methods to identify candidate biomarkers and verified the results in external datasets. RESULTS In total, 27 DEMs were found to be statistically significant (p < .05). Ten candidate schizophrenia-associated miRNAs were identified through computational learning methods. The diagnostic efficiency was verified on a blood-miRNA dataset (GSE54578) with a random forest (RF) model and achieved an area under the curve (AUC) of 0.83 ± 0.14. Moreover, 855 experimentally validated target genes for these candidate miRNAs were retrieved, and 11 hub genes were identified. Enrichment analysis revealed that the main functions in which the target genes were enriched were those related to cell signalling, prenatal infections, cancers, cell deaths, oxidative stress, endocrine disorders, transcription regulation, and kinase activities. The diagnostic ability of the hub genes was reflected in a comparably good average AUC of 0.77 ± 0.09 for an external dataset (GSE38484). CONCLUSIONS A meta-analysis that combines computational and mathematical methods provides a reliable tool for identifying candidate biomarkers of schizophrenia.
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Affiliation(s)
- Shiyuan Han
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yongning Li
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of International Medical Service, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jun Gao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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6
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Biyu H, GuangWen T, Ming Z, Lixin G, Mengshan L. A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks. Heliyon 2023; 9:e17726. [PMID: 37539215 PMCID: PMC10395133 DOI: 10.1016/j.heliyon.2023.e17726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/13/2023] [Accepted: 06/26/2023] [Indexed: 08/05/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been shown to play a regulatory role in various processes of human diseases. However, lncRNA experiments are inefficient, time-consuming and highly subjective, so that the number of experimentally verified associations between lncRNA and diseases is limited. In the era of big data, numerous machine learning methods have been proposed to predict the potential association between lncRNA and diseases, but the characteristics of the associated data were seldom explored. In these methods, negative samples are randomly selected for model training and the model is prone to learn the potential positive association error, thus affecting the prediction accuracy. In this paper, we proposed a cyclic optimization model of predicting lncRNA-disease associations (COPTLDA in short). In COPTLDA, the two-step training strategy is adopted to search for the samples with the greater probability of being negative examples from unlabeled samples and the determined samples are treated as negative samples, which are combined together with known positive samples to train the model. The searching and training steps are repeated until the best model is obtained as the final prediction model. In order to evaluate the performance of the model, 30% of the known positive samples are used to calculate the model accuracy and 10% of positive samples are used to calculate the recall rate of the model. The sampling strategy used in this paper can improve the accuracy and the AUC value reaches 0.9348. The results of case studies showed that the model could predict the potential associations between lncRNA and malignant tumors such as colorectal cancer, gastric cancer, and breast cancer. The predicted top 20 associated lncRNAs included 10 colorectal cancer lncRNAs, 2 gastric cancer lncRNAs, and 8 breast cancer lncRNAs.
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Affiliation(s)
| | | | | | | | - Li Mengshan
- Corresponding author. Gannan Normal University, China.
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7
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Kim Y, Lee M. Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments. Int J Mol Sci 2023; 24:10299. [PMID: 37373445 DOI: 10.3390/ijms241210299] [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: 05/24/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 06/29/2023] Open
Abstract
This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.
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Affiliation(s)
- Yoojoong Kim
- School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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8
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Zhang N, Song GY, Hu YJ, Wang X, Chao TZ, Wu YY, Xu P. Analysis of lncRNA-miRNA-mRNA Expression in the Troxerutin-Mediated Prevention of Radiation-Induced Lung Injury in Mice. J Inflamm Res 2023; 16:2387-2399. [PMID: 37292381 PMCID: PMC10246569 DOI: 10.2147/jir.s397327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 03/14/2023] [Indexed: 06/10/2023] Open
Abstract
Background Radiation-induced lung injury (RILI) is a critical factor that leads to pulmonary fibrosis and other diseases. LncRNAs and miRNAs contribute to normal tissue damage caused by ionizing radiation. Troxerutin offers protection against radiation; however, its underlying mechanism remains largely undetermined. Methods We established a model of RILI in mice pretreated with troxerutin. The lung tissue was extracted for RNA sequencing, and an RNA library was constructed. Next, we estimated the target miRNAs of differentially expressed (DE) lncRNAs, and the target mRNAs of DE miRNAs. Then, functional annotations of these target mRNAs were performed using GO and KEGG. Results Compared to the control group, 150 lncRNA, 43 miRNA, and 184 mRNA were significantly up-regulated, whereas, 189 lncRNA, 15 miRNA, and 146 mRNA were markedly down-regulated following troxerutin pretreatment. Our results revealed that the Wnt, cAMP, and tumor-related signaling pathways played an essential role in RILI prevention via troxerutin using lncRNA-miRNA-mRNA network. Conclusion These evidences revealed that the abnormal regulation of RNA potentially leads to pulmonary fibrosis. Therefore, targeting lncRNA and miRNA, along with a closer examination of competitive endogenous RNA (ceRNA) networks are of great significance to the identification of troxerutin targets that can protect against RILI.
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Affiliation(s)
- Nan Zhang
- School of Food and Biomedicine, Zaozhuang University, Zaozhuang, Shandong, 277160, People’s Republic of China
| | - Gui-yuan Song
- School of Public Health, Weifang Medical University, Weifang, Shandong, 261000, People’s Republic of China
- Radiology Laboratory, Central Laboratory, Rizhao People’s Hospital, Rizhao, Shandong, 276800, People’s Republic of China
| | - Yong-jian Hu
- Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan, 453003, People’s Republic of China
| | - Xia Wang
- Henan Key Laboratory of Medical Tissue Regeneration, Xinxiang Medical University, Xinxiang, Henan, 453003, People’s Republic of China
| | - Tian-zhu Chao
- School of Food and Biomedicine, Zaozhuang University, Zaozhuang, Shandong, 277160, People’s Republic of China
| | - Yao-yao Wu
- School of Food and Biomedicine, Zaozhuang University, Zaozhuang, Shandong, 277160, People’s Republic of China
| | - Ping Xu
- School of Food and Biomedicine, Zaozhuang University, Zaozhuang, Shandong, 277160, People’s Republic of China
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9
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Wang MN, Li Y, Lei LL, Ding DW, Xie XJ. Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion. Front Pharmacol 2023; 14:1132012. [PMID: 36817132 PMCID: PMC9931722 DOI: 10.3389/fphar.2023.1132012] [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: 12/26/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.
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Affiliation(s)
- Mei-Neng Wang
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Yu Li
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China,*Correspondence: Yu Li,
| | - Li-Lan Lei
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - De-Wu Ding
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Xue-Jun Xie
- School of Mathematics and Computer Science, Yichun University, Yichun, China
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10
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Li Y, Li F, Sun Z, Li J. A review of literature: role of long noncoding RNA TPT1-AS1 in human diseases. CLINICAL & TRANSLATIONAL ONCOLOGY : OFFICIAL PUBLICATION OF THE FEDERATION OF SPANISH ONCOLOGY SOCIETIES AND OF THE NATIONAL CANCER INSTITUTE OF MEXICO 2023; 25:306-315. [PMID: 36112261 DOI: 10.1007/s12094-022-02947-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/01/2022] [Indexed: 01/27/2023]
Abstract
Human diseases are multifactorial processes mainly driven by the intricate interactions of genetic and environmental factors. Long noncoding RNAs (lncRNAs) represent a type of non-coding RNAs with more than 200 nucleotides. Multiple studies have demonstrated that the dysregulation of lncRNAs is associated with complex biological as well as pathological processes through various mechanism, especially the regulation of gene transcription and related signal transduction pathways. Moreover, an increasing number of studies have explored lncRNA-based clinical applications in different diseases. For instance, the lncRNA Tumor Protein Translationally Controlled 1 (TPT1) Antisense RNA 1 (TPT1-AS1) was found to be dysregulated in several types of disease and strongly associated with patient prognosis and diverse clinical features. Recent studies have also documented that TPT1-AS1 modulates numerous biological processes through multiple mechanisms, including cell proliferation, apoptosis, autophagy, invasion, migration, radiosensitivity, chemosensitivity, stemness, and extracellular matrix (ECM) synthesis. Furthermore, TPT1-AS1 was regarded as a promising biomarker for the diagnosis, prognosis and treatment of several human diseases. In this review, we summarize the role of TPT1-AS1 in human diseases with the aspects of its expression, relevant clinical characteristics, molecular mechanisms, biological functions, and subsequent clinical applications.
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Affiliation(s)
- Yi Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Fulei Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Zongzong Sun
- Department of Obstetrics and Gynaecology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Juan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China.
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11
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Wang W, Chen H. Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks. Brief Bioinform 2023; 24:6918743. [PMID: 36526276 DOI: 10.1093/bib/bbac495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 12/23/2022] Open
Abstract
Increasing studies have proved that microRNAs (miRNAs) are critical biomarkers in the development of human complex diseases. Identifying disease-related miRNAs is beneficial to disease prevention, diagnosis and remedy. Based on the assumption that similar miRNAs tend to associate with similar diseases, various computational methods have been developed to predict novel miRNA-disease associations (MDAs). However, selecting proper features for similarity calculation is a challenging task because of data deficiencies in biomedical science. In this study, we propose a deep learning-based computational method named MAGCN to predict potential MDAs without using any similarity measurements. Our method predicts novel MDAs based on known lncRNA-miRNA interactions via graph convolution networks with multichannel attention mechanism and convolutional neural network combiner. Extensive experiments show that the average area under the receiver operating characteristic values obtained by our method under 2-fold, 5-fold and 10-fold cross-validations are 0.8994, 0.9032 and 0.9044, respectively. When compared with five state-of-the-art methods, MAGCN shows improvement in terms of prediction accuracy. In addition, we conduct case studies on three diseases to discover their related miRNAs, and find that all the top 50 predictions for all the three diseases have been supported by established databases. The comprehensive results demonstrate that our method is a reliable tool in detecting new disease-related miRNAs.
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12
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinform 2022; 23:6696143. [PMID: 36094095 DOI: 10.1093/bib/bbac397] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.
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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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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: 27] [Impact Index Per Article: 13.5] [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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14
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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: 34] [Impact Index Per Article: 17.0] [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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15
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Sun Z, Huang Q, Yang Y, Li S, Lv H, Zhang Y, Lin H, Ning L. PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization. Brief Bioinform 2022; 23:6640008. [PMID: 35817303 DOI: 10.1093/bib/bbac240] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/16/2022] [Accepted: 05/24/2022] [Indexed: 12/19/2022] Open
Abstract
Many studies have proved that small nucleolar RNAs (snoRNAs) play critical roles in the development of various human complex diseases. Discovering the associations between snoRNAs and diseases is an important step toward understanding the pathogenesis and characteristics of diseases. However, uncovering associations via traditional experimental approaches is costly and time-consuming. This study proposed a bounded nuclear norm regularization-based method, called PSnoD, to predict snoRNA-disease associations. Benchmark experiments showed that compared with the state-of-the-art methods, PSnoD achieved a superior performance in the 5-fold stratified shuffle split. PSnoD produced a robust performance with an area under receiver-operating characteristic of 0.90 and an area under precision-recall of 0.55, highlighting the effectiveness of our proposed method. In addition, the computational efficiency of PSnoD was also demonstrated by comparison with other matrix completion techniques. More importantly, the case study further elucidated the ability of PSnoD to screen potential snoRNA-disease associations. The code of PSnoD has been uploaded to https://github.com/linDing-groups/PSnoD. Based on PSnoD, we established a web server that is freely accessed via http://psnod.lin-group.cn/.
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Affiliation(s)
- Zijie Sun
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| | - Qinlai Huang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| | - Yuhe Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shihao Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hao Lv
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Hao Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
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16
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Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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17
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Wang B, Dai Z, Kong D, Yu L, Zheng J, Li P. Boosting semi-supervised network representation learning with pseudo-multitasking. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02844-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Pan J, You ZH, Li LP, Huang WZ, Guo JX, Yu CQ, Wang LP, Zhao ZY. DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network. Front Bioeng Biotechnol 2022; 10:807522. [PMID: 35387292 PMCID: PMC8978800 DOI: 10.3389/fbioe.2022.807522] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/25/2022] [Indexed: 12/30/2022] Open
Abstract
The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.
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Affiliation(s)
- Jie Pan
- School of Information Engineering, Xijing University, Xi’an, China
| | - Zhu-Hong You
- School of Information Engineering, Xijing University, Xi’an, China
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi’an, China
- College of Grassland and Environment Science, Xinjiang Agricultural University, Urumqi, China
- *Correspondence: Li-Ping Li, ; Chang-Qing Yu,
| | - Wen-Zhun Huang
- School of Information Engineering, Xijing University, Xi’an, China
| | - Jian-Xin Guo
- 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 Wang
- School of Information Engineering, Xijing University, Xi’an, China
| | - Zheng-Yang Zhao
- School of Information Engineering, Xijing University, Xi’an, China
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19
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Yang L, Li LP, Yi HC. DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph. BMC Bioinformatics 2022; 22:621. [PMID: 35216549 PMCID: PMC8875942 DOI: 10.1186/s12859-022-04579-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/18/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. RESULTS In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. CONCLUSIONS The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.
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Affiliation(s)
- Long Yang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li-Ping Li
- College of Grassland and Environmental Science, Xinjiang Agricultural University, Urumqi, 830052, China.
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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20
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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21
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Hu P, Huang YA, Mei J, Leung H, Chen ZH, Kuang ZM, You ZH, Hu L. Learning from low-rank multimodal representations for predicting disease-drug associations. BMC Med Inform Decis Mak 2021; 21:308. [PMID: 34736437 PMCID: PMC8567544 DOI: 10.1186/s12911-021-01648-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 10/06/2021] [Indexed: 12/15/2022] Open
Abstract
Background Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs.
Results In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models. Conclusions The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.
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Affiliation(s)
- Pengwei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Yu-An Huang
- The Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Henry Leung
- Electrical and Computer Engineering, University of Calgary, Calgary, Canada
| | - Zhan-Heng Chen
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Ze-Min Kuang
- Beijing Anzhen Hospital of Capital Medical University, Beijing, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
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22
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Que Y, Cao M, He J, Zhang Q, Chen Q, Yan C, Lin A, Yang L, Wu Z, Zhu D, Chen F, Chen Z, Xiao C, Hou K, Zhang B. Gut Bacterial Characteristics of Patients With Type 2 Diabetes Mellitus and the Application Potential. Front Immunol 2021; 12:722206. [PMID: 34484230 PMCID: PMC8415158 DOI: 10.3389/fimmu.2021.722206] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a complex disorder comprehensively influenced by genetic and environmental risk, and research increasingly has indicated the role of microbial dysbiosis in T2DM pathogenesis. However, studies comparing the microbiome characteristics between T2DM and healthy controls have reported inconsistent results. To further identify and describe the characteristics of the intestinal flora of T2DM patients, we performed a systematic review and meta-analysis of stool microbial profiles to discern and describe microbial dysbiosis in T2DM and to explore heterogeneity among 7 studies (600 T2DM cases, 543 controls, 1143 samples in total). Using a random effects model and a fixed effects model, we observed significant differences in beta diversity, but not alpha diversity, between individuals with T2DM and controls. We identified various operational taxonomic unit (OTUs) and bacterial genera with significant odds ratios for T2DM. The T2DM signatures derived from a single study by stepwise feature selection could be applied in other studies. By training on multiple studies, we improved the detection accuracy and disease specificity for T2DM. We also discuss the relationship between T2DM-enriched or T2DM-depleted genera and probiotics and provide new ideas for diabetes prevention and improvement.
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Affiliation(s)
- Yanyan Que
- Department of Endocrinology, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Man Cao
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China
| | - Jianquan He
- Department of Rehabilitation, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Qiang Zhang
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Qiongyun Chen
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Changsheng Yan
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Aiqiang Lin
- Department of Research and Development, Xiamen Treatgut Biotechnology Co., Ltd., Xiamen, China
| | - Luxi Yang
- School of Medicine, Xiamen University, Xiamen, China
| | - Zezhen Wu
- Department of Endocrine and Metabolic Diseases, Longhu Hospital, The First Affiliated Hospital of Medical College of Shantou University, Shantou, China.,Graduate School, Medical College of Shantou University, Shantou, China
| | - Dan Zhu
- Department of Endocrine and Metabolic Diseases, Longhu Hospital, The First Affiliated Hospital of Medical College of Shantou University, Shantou, China
| | - Fengwu Chen
- Department of Endocrine and Metabolic Diseases, Longhu Hospital, The First Affiliated Hospital of Medical College of Shantou University, Shantou, China
| | - Zhangran Chen
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Chuanxing Xiao
- School of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,School of Medicine, Xiamen University, Xiamen, China
| | - Kaijian Hou
- Department of Endocrine and Metabolic Diseases, Longhu Hospital, The First Affiliated Hospital of Medical College of Shantou University, Shantou, China
| | - Bangzhou Zhang
- Department of Gastroenterology, Zhongshan Hospital Xiamen University, Xiamen, China.,School of Medicine, Xiamen University, Xiamen, China
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23
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Wu Z, Jiang D, Wang J, Hsieh CY, Cao D, Hou T. Mining Toxicity Information from Large Amounts of Toxicity Data. J Med Chem 2021; 64:6924-6936. [PMID: 33961429 DOI: 10.1021/acs.jmedchem.1c00421] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.
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Affiliation(s)
- Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Tencent, Shenzhen 518057 Guangdong, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004 Hunan, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
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