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Chen M, Deng Y, Li Z, Ye Y, He Z. KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection. BMC Bioinformatics 2023; 24:229. [PMID: 37268893 DOI: 10.1186/s12859-023-05365-2] [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: 11/27/2022] [Accepted: 05/26/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments. RESULTS In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. CONCLUSION A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.
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Affiliation(s)
- Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Yingwei Deng
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China.
| | - Zejun Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Yifan Ye
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
| | - Ziyi He
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, 421002, China
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Zhang Y, Ye F, Gao X. MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2907-2919. [PMID: 34283719 DOI: 10.1109/tcbb.2021.3098126] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.
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Brasil S, Neves CJ, Rijoff T, Falcão M, Valadão G, Videira PA, Dos Reis Ferreira V. Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases. Front Mol Biosci 2021; 8:648012. [PMID: 34026829 PMCID: PMC8131862 DOI: 10.3389/fmolb.2021.648012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Cátia José Neves
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Tatiana Rijoff
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Marta Falcão
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, Lisbon, Portugal.,Departamento de Ciências e Tecnologias, Autónoma Techlab - Universidade Autónoma de Lisboa, Lisbon, Portugal.,Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal
| | - Paula A Videira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal.,UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Vanessa Dos Reis Ferreira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
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Lei X, Zhang C. Predicting metabolite-disease associations based on KATZ model. BioData Min 2019; 12:19. [PMID: 31673292 PMCID: PMC6815005 DOI: 10.1186/s13040-019-0206-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Accepted: 09/12/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Increasing numbers of evidences have illuminated that metabolites can respond to pathological changes. However, identifying the diseases-related metabolites is a magnificent challenge in the field of biology and medicine. Traditional medical equipment not only has the limitation of its accuracy but also is expensive and time-consuming. Therefore, it's necessary to take advantage of computational methods for predicting potential associations between metabolites and diseases. RESULTS In this study, we develop a computational method based on KATZ algorithm to predict metabolite-disease associations (KATZMDA). Firstly, we extract data about metabolite-disease pairs from the latest version of HMDB database for the materials of prediction. Then we take advantage of disease semantic similarity and the improved disease Gaussian Interaction Profile (GIP) kernel similarity to obtain more reliable disease similarity and enhance the predictive performance of our proposed computational method. Simultaneously, KATZ algorithm is applied in the domains of metabolomics for the first time. CONCLUSIONS According to three kinds of cross validations and case studies of three common diseases, KATZMDA is worth serving as an impactful measuring tool for predicting the potential associations between metabolites and diseases.
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Affiliation(s)
- Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi’an, 710119 Shaanxi China
| | - Cheng Zhang
- School of Computer Science, Shaanxi Normal University, Xi’an, 710119 Shaanxi China
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