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Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

2023-10-10

Abstract excerpt

<title>Abstract</title> <p><italic>Background</italic> Numerous transcriptomic-based models have been developed to predict or understand the fundamental mechanisms driving biological phenotypes. However, few models have successfully transitioned into clinical practice due to challenges associated with generalizability and interpretability. To address these issues, researchers have turned to dimensionality reducti...

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Literature Corpus work
8389d254-5547-582e-8c19-222c1d95aa37
DOI
10.21203/rs.3.rs-3398654/v1
Open publication

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Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic dataDOI 10.21203/rs.3.rs-3398654/v1
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