Article

Phenotype Driven Data Augmentation Methods for Transcriptomic Data

2023-10-31

Abstract excerpt

With machine learning taking over biomedical applications, working with transcriptomic data on supervised learning tasks is challenging due to high dimensionality, low patient numbers and class imbalances. Machine learning models tend to overfit these data and do not generalise well on out-of-distribution samples. Data augmentation strategies help alleviate this by introducing synthetic data points and acting as r...

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Literature Corpus work: d94d4e36-a4ed-5692-9aa3-b81f8121c69d
DOI: 10.1101/2023.10.26.564263

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