An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning Article Swipe
Qingquan Liao
,
Xiangzheng Fu
,
Linlin Zhuo
,
Hao Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2023.1325001
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmicb.2023.1325001
Multiple studies have demonstrated that microRNA (miRNA) can be deeply involved in the regulatory mechanism of human microbiota, thereby inducing disease. Developing effective methods to infer potential associations between microRNAs (miRNAs) and diseases can aid early diagnosis and treatment. Recent methods utilize machine learning or deep learning to predict miRNA-disease associations (MDAs), achieving state-of-the-art performance. However, the problem of sparse neighborhoods of nodes due to lack of data has not been well solved. To this end, we propose a new model named MTCL-MDA, which integrates multiple-types of contrastive learning strategies into a graph collaborative filtering model to predict potential MDAs. The model adopts a contrastive learning strategy based on topology, which alleviates the damage to model performance caused by sparse neighborhoods. In addition, the model also adopts a semantic-based contrastive learning strategy, which not only reduces the impact of noise introduced by topology-based contrastive learning, but also enhances the semantic information of nodes. Experimental results show that our model outperforms existing models on all evaluation metrics. Case analysis shows that our model can more accurately identify potential MDA, which is of great significance for the screening and diagnosis of real-life diseases. Our data and code are publicly available at: https://github.com/Lqingquan/MTCL-MDA .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmicb.2023.1325001
- https://www.frontiersin.org/articles/10.3389/fmicb.2023.1325001/pdf?isPublishedV2=False
- OA Status
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- Cited By
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- References
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- OpenAlex ID
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https://openalex.org/W4389729396Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmicb.2023.1325001Digital Object Identifier
- Title
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An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-14Full publication date if available
- Authors
-
Qingquan Liao, Xiangzheng Fu, Linlin Zhuo, Hao ChenList of authors in order
- Landing page
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https://doi.org/10.3389/fmicb.2023.1325001Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fmicb.2023.1325001/pdf?isPublishedV2=FalseDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fmicb.2023.1325001/pdf?isPublishedV2=FalseDirect OA link when available
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Computer science, Artificial intelligence, Machine learning, Mechanism (biology), Graph, Theoretical computer science, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6798028545, https://openalex.org/W2014946489, https://openalex.org/W2169234352, https://openalex.org/W3111392271, https://openalex.org/W1990061381, https://openalex.org/W2395114866, https://openalex.org/W2895927009, https://openalex.org/W2410390867, https://openalex.org/W2022732522, https://openalex.org/W2962928958, https://openalex.org/W2005532281, https://openalex.org/W2159444732, https://openalex.org/W3048858802, https://openalex.org/W2026570544, https://openalex.org/W2548805615, https://openalex.org/W4384664737, https://openalex.org/W2886514991, https://openalex.org/W3158158224, https://openalex.org/W2109483324, https://openalex.org/W4246271082, https://openalex.org/W3094371038, https://openalex.org/W2816757187, https://openalex.org/W2126619650, https://openalex.org/W2128768066, https://openalex.org/W4232023503, https://openalex.org/W2064214040, https://openalex.org/W3176269371, https://openalex.org/W4293812148, https://openalex.org/W3093996932, https://openalex.org/W4210548518, https://openalex.org/W4225537122, https://openalex.org/W1979104937, https://openalex.org/W2151319884, https://openalex.org/W2096253076, https://openalex.org/W2161426724, https://openalex.org/W2588714305, https://openalex.org/W4226097900, https://openalex.org/W2125826054, https://openalex.org/W2902246085, https://openalex.org/W6680830989, https://openalex.org/W2001911979, https://openalex.org/W2047535851, https://openalex.org/W4312084649, https://openalex.org/W4311823641, https://openalex.org/W4307502334, https://openalex.org/W2111663113, https://openalex.org/W1928264125, https://openalex.org/W3095707208, https://openalex.org/W4281553978, https://openalex.org/W2601934706, https://openalex.org/W2902400385, https://openalex.org/W2258129851, https://openalex.org/W2972020367, https://openalex.org/W2789567770, https://openalex.org/W3001440478, https://openalex.org/W2124756133, https://openalex.org/W3178103610, https://openalex.org/W2158135353, https://openalex.org/W2140310134 |
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