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 proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.

中文翻译：

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基于区分性基因关联的以模式为中心的组学数据转换有助于 TCGA 中的预测任务，同时确保可解释性


组学数据源的日益普及正在推动对癌症等复杂疾病背后的调控机制的研究。然而，产生的大量分子特征以及它们之间固有的相互作用导致了一定程度的复杂性，阻碍了描述性和预测性任务，需要定制的算法来从这些数据源中提取相关信息。我们提出了一种转换，将以分子（例如转录本和蛋白质）为中心的数据转移到一个新的数据空间，该空间专注于由统计相关的共表达模式给出的假定调节模块。为此，所提出的转换通过双聚类从数据中提取模式，并使用它们来创建新变量，并保证可解释性和判别力。该转换可实现高达 99% 的降维，并提高跨多个组学层的各种分类器的预测性能。结果表明，组学数据从以基因为中心到以模式为中心的数据转换支持预测任务和人类解释，尤其有助于精准医学应用。