Kernel Methods for Omics Data Mining: Theory and Applications
暫譯: 基於核方法的組學數據挖掘：理論與應用

Jiang, Hao, Ching, Wai-Ki

Kernel Methods for Omics Data Mining: Theory and Applications
  • 出版商: Springer
  • 出版日期: 2026-01-13
  • 售價: $5,810
  • 貴賓價: 9.5 折 $5,520
  • 語言: 英文
  • 頁數: 230
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819531284
  • ISBN-13: 9789819531288
  • 相關分類: Data-mining

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.


商品描述(中文翻譯)

本書提供了對生物資訊學領域中組學數據建模與分析的新視角。考慮到組學數據的高維度性和非線性特性，本書通過矩陣優化的新穎視角來解開數據的非線性。通過整合機器學習框架，提出了多種新技術來應對組學數據分析的複雜性。書中提供了直觀的範例和插圖，以幫助讀者理解組學數據分析中的關鍵概念和一般程序。本書旨在為對學習、計算生物學、優化及相關領域感興趣的學術學者和實務工作者提供參考。上述領域的研究生也能從本書中受益。

作者簡介

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.

作者簡介(中文翻譯)

Hao Jiang於2009年獲得中國哈爾濱工業大學數學學士學位，並於2013年獲得香港大學博士學位。她於2010年獲得大學研究生獎學金。2010年和2012年，她分別在日本東京的創價大學和京都大學擔任訪問學者。

她目前是中國人民大學數學學院的全職教授。她的研究興趣包括生物信息學中的基於學習的建模、複雜系統的優化和控制。她已發表超過60篇經過審核的期刊和會議論文。此外，她於2012年獲得ISB最佳論文獎，並於2022年成為DDCLS最佳論文獎決賽入圍者。

Wai-Ki Ching是香港大學數學系的全職教授。他在香港大學獲得數學學士和哲學碩士學位，並在香港中文大學獲得系統工程與工程管理博士學位。他於2013年獲得中國教育部的高等教育優秀科學研究成果獎（第二名）（2014年）、香港中文大學工程學院的傑出校友獎（2017年）、2019年湖南省高等教育優秀科學研究成果獎（第二名）（2019年）、香港大學的傑出研究生導師獎（2020年），並於2021年被史丹佛大學評選為全球前2%最被引用的科學家。他的研究興趣包括量化金融和生物信息學的矩陣計算和隨機建模。他是超過350篇出版物的作者/編輯，包括超過250篇期刊論文、5本編輯的期刊特刊、6本書籍以及超過110篇書章和會議論文集。

類似商品