A meta-learning approach for genomic survival analysis
| Autor: | Heather M. Selby, Hong Zheng, Olivier Gevaert, Arnout Devos, Yeping Lina Qiu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
tumors Computer science Science Sequencing data General Physics and Astronomy Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Article Cancer prognosis Machine Learning 03 medical and health sciences 0302 clinical medicine Neoplasms Cancer genomics Leverage (statistics) Humans Computational models Survival analysis risk Multidisciplinary Artificial neural network business.industry pathway fungi Computational Biology food and beverages Cancer survival General Chemistry Genomics Prognosis Survival Analysis 030104 developmental biology ComputingMethodologies_PATTERNRECOGNITION Sample size determination 030220 oncology & carcinogenesis lung-cancer Artificial intelligence progression Neural Networks Computer business computer Algorithms |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020) Nature Communications volume 11, Article number: 6350 Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer. RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples. |
| Databáze: | OpenAIRE |
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