ArticleMachine learning2023
Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm.
Article in Machine learning, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MDTR: a knowledge-guided interpretable representation for quantifying liver toxicity at transcriptomic level.Frontiers in pharmacology · 2024Article
- A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.IBRO neuroscience reports · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
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Authors and funding
5 authors.
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Abstract
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as
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Registered trials
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