Pattern‐centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability
André Patrício,
Rafael S. Costa,
Rui Henriques
Abstract:The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom‐built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and protei… Show more
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