ArticleMethods in molecular biology (Clifton, N.J.)2025
Machine Learning for Detecting and Analyzing Chromoanagenesis Events.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
A comprehensive analysis of chromoanagenesis pan-cancer features is crucial for a broad and deep understanding of the phenomena. In this chapter, we describe a cancer-type agnostic machine-learning algorithm for detecting chromoanagenesis. We leveraged data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a predictive algorithm for chromoanagenesis detection based on CNA data, with an accuracy of 86%. This algorithm was applied to analyze data from over 10,000 TCGA cancer patients. The analysis identified cancer-type specific chromoanagenesis characteristics and revealed distinct sets of genes impacted by chromoanagenesis versus non-chromoanagenesis tumorigenesis.
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