Kernel Methods for Omics Data Mining: Theory and Applications: 10 (Intelligent Control and Learning Systems, 10) - Tapa dura

Acerca del autor

Hao Jiang received the B.Sc. degree in Mathematics from the Harbin Institute of Technology, Harbin, China, in 2009. She received the Ph.D. degree from the University of Hong Kong, in 2013. She was the recipient of the University Postgraduate Fellowships in 2010. In 2010 and 2012, she was a Visiting Scholar with Soka University, Tokyo, Japan, and Kyoto University, Kyoto, Japan, respectively. 

She is currently a full Professor with the School of Mathematics, Renmin University of China, Beijing, China. Her research interests include learning-based modeling in bioinformatics, optimization, and control of complex systems. She has published more than 60 refereed journal and conference papers. In addition, she was the recipient of Best paper award of ISB in 2012, and the Best paper award finalist award of DDCLS in 2022.

Wai-Ki Ching is a full Professor at the Department of Mathematics, University of Hong Kong. He obtained his B. Sc. and M. Phil. in Mathematics from University of Hong Kong and his Ph.D. Systems Engineering and Engineering Management from Chinese University of Hong Kong. He received 2013 Higher Education Outstanding Scientific Research Output Awards (Second Prize) from the Ministry of Education, China (2014), Distinguished Alumni Award, Faculty of Engineering, Chinese University of Hong Kong (2017), 2019 Higher Education Outstanding Scientific Research Output Awards (Second Prize), Hunan Province, China (2019), Outstanding Research Student Supervisor Award, University of Hong Kong (2020) and he was World's Top 2% Most-cited Scientists (2021) by Stanford University. His research interests are Matrix Computations and Stochastic Modeling for Quantitative Finance and Bioinformatics. He is an author/editor of over 350 publications including over 250 journal papers, 5 edited journal special issues, 6 books and over 110 book chapters and conference proceedings.

De la contraportada

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.