Prediction in Cancer Genomics Using Topological Signatures and Machine Learning

DOI10.1007/978-3-030-43408-3_10zbMath1448.62165OpenAlexW3038125657MaRDI QIDQ5118370

Arina Ushakova, Radmila Sazdanovic, Georgina Gonzalez, Javier Arsuaga

Publication date: 8 September 2020

Published in: Topological Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-43408-3_10

zbMATH Keywords

predictionmachine learningcancer genomicstopological signatures

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Persistent homology and applications, topological data analysis (55N31) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20) Topological data analysis (62R40)

Related Items (1)

What Are Higher-Order Networks?

Uses Software

• R
• Gudhi
• Perseus
• jPlex
• GitHub
• Eirene
• Ripser
• KCsmart

Cites Work

• Unnamed Item
• Morse theory for filtrations and efficient computation of persistent homology
• Applications of computational homology to the analysis of treatment response in breast cancer patients
• Hidden Markov models approach to the analysis of array CGH data
• The Group Lasso for Logistic Regression
• Simplicial Models and Topological Inference in Biological Systems
• Statistical topological data analysis using persistence landscapes

This page was built for publication: Prediction in Cancer Genomics Using Topological Signatures and Machine Learning