2020/11/22 by Stefan Groha, Groha, Stefan, Caroline Weis +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Topological and Geometric Data Analysis #cs.LG #q-bio.GN
paper · pdf · doi:10.48550/arxiv.2011.11070
openalex publication_date 2020/11/22 · arxiv created 2021/04/22 · arxiv updated 2021/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.