Recent advances in machine learning methods for predicting LncRNA and disease associations
Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationsh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2022.1071972/full |
| _version_ | 1797986729519480832 |
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| author | Jianjun Tan Xiaoyi Li Lu Zhang Zhaolan Du |
| author_facet | Jianjun Tan Xiaoyi Li Lu Zhang Zhaolan Du |
| author_sort | Jianjun Tan |
| collection | DOAJ |
| description | Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists’ understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models. |
| first_indexed | 2024-04-11T07:36:40Z |
| format | Article |
| id | doaj.art-89d17f6eed164465a3d88edcae661177 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-04-11T07:36:40Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-89d17f6eed164465a3d88edcae6611772022-12-22T04:36:43ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882022-11-011210.3389/fcimb.2022.10719721071972Recent advances in machine learning methods for predicting LncRNA and disease associationsJianjun TanXiaoyi LiLu ZhangZhaolan DuLong non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists’ understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.https://www.frontiersin.org/articles/10.3389/fcimb.2022.1071972/fulllncRNAhuman diseaseslncRNA-disease associationsmachine learning methodspredictive models |
| spellingShingle | Jianjun Tan Xiaoyi Li Lu Zhang Zhaolan Du Recent advances in machine learning methods for predicting LncRNA and disease associations Frontiers in Cellular and Infection Microbiology lncRNA human diseases lncRNA-disease associations machine learning methods predictive models |
| title | Recent advances in machine learning methods for predicting LncRNA and disease associations |
| title_full | Recent advances in machine learning methods for predicting LncRNA and disease associations |
| title_fullStr | Recent advances in machine learning methods for predicting LncRNA and disease associations |
| title_full_unstemmed | Recent advances in machine learning methods for predicting LncRNA and disease associations |
| title_short | Recent advances in machine learning methods for predicting LncRNA and disease associations |
| title_sort | recent advances in machine learning methods for predicting lncrna and disease associations |
| topic | lncRNA human diseases lncRNA-disease associations machine learning methods predictive models |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2022.1071972/full |
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