Kernel Methods for Omics Data Mining - Theory and Applications

This book provides a new perspective on omics data modelling and analysis in bioinformatics area. Taking into consideration on the high-dimensionality and nonlinearity properties in omics data, the book detangles nonlinearity of data through novel perspectives of matrix optimization. Through integration of machine learning frameworks, various novel techniques are proposed to deal with the complexity of omics data analysis. Intuitive examples and illustrations are provided to help readers for understanding the key idea and general procedures in omics data analysis. This book is intended for academic scholars and practitioners who are interested in learning, computational biology, optimization and related fields. The graduate students in the above field can also benefit from this book.

Inhaltsverzeichnis

Omics Data: Acquisition and Mining.- Omics Data: Acquisition and Mining.-  Kernels and Spectrum Perturbations .-  Hadamard Kernel SVM with Applications.-  Regularized Multiple Kernel Learning Framework.-  Correlation Kernels for SVM Classification.- Weighted GTS Kernel and Applications in Drug Side-effect Profiles Prediction.- Single Cell RNA-sequencing Data Analysis.- Kernel Non-negative Matrix Factorization Framework for Single Cell Clustering.- Deep Neural Network with Kernel Nonnegative Matrix
Factorization for Single Cell Clustering.-  Multi-omics Single-cell Data Integration via High-order Kernel
Spectral Clustering.

Produktdetails

• Autoren: Wai-Ki Ching, Hao Jiang
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Fester Einband
• Erscheint: 05.12.2025
• EAN: 9789819531288
• ISBN: 978-981-9531-28-8
• Seiten: 215
• Illustration: XI, 215 p. 88 illus.
• Serie: Intelligent Control and Learning Systems
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik > Sonstiges; Optimierung, Künstliche Intelligenz, Data Mining, Optimization, Computeranwendungen in Industrie und Technologie, Support Vector Machines, clustering, Health Informatics, Computational Intelligence, Mathematical and Computational Biology, Gene Expression, Kernel Methods, Multi-omics, Non-negative Matrix Factorization, Learning system, Single cell data