A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data

DOI10.1007/978-3-030-42266-0_14zbMath1500.92079OpenAlexW3013230449MaRDI QIDQ5041149

Sayan Mandal, Laxmi Parida, Niina Haiminen, Aldo Guzmán-Sáenz, Saugata Basu

Publication date: 13 October 2022

Published in: Algorithms for Computational Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-42266-0_14

zbMATH Keywords

topological data analysisgene expressionParkinson's diseasephenotype prediction

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20) Topological data analysis (62R40)

Uses Software

• Scikit
• TensorFlow
• Keras

Cites Work

• Measuring and testing dependence by correlation of distances
• A topological approach for protein classification
• Efficient and robust persistent homology for measures
• Persistent homology transform for modeling shapes and surfaces
• Protein Classification with Improved Topological Data Analysis.
• Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation
• Persistence Images: A Stable Vector Representation of Persistent Homology
• Stability of persistence diagrams
• PERSISTENCE BARCODES FOR SHAPES
• Statistical topological data analysis using persistence landscapes
• Unnamed Item

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