Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis
| Autor: | Yixuan Mo, Joseph Adu-Amankwaah, Wenjie Qin, Tan Gao, Xiaoqing Hou, Mengying Fan, Xuemei Liao, Liwei Jia, Jinming Zhao, Jinxiang Yuan, Rubin Tan |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Annals of Medicine, Vol 55, Iss 2 (2023) |
| Druh dokumentu: | article |
| ISSN: | 07853890 1365-2060 0785-3890 |
| DOI: | 10.1080/07853890.2023.2279748 |
| Popis: | The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells’ proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes. |
| Databáze: | Directory of Open Access Journals |
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