menu
{ "item_title" : "Kernel Methods for Omics Data Mining", "item_author" : [" Hao Jiang", "Wai-Ki Ching "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/81/953/128/9819531284_b.jpg", "price_data" : { "retail_price" : "139.99", "online_price" : "139.99", "our_price" : "139.99", "club_price" : "139.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Kernel Methods for Omics Data Mining|Hao Jiang

Kernel Methods for Omics Data Mining : Theory and Applications

local_shippingShip to Me
On Order. Usually ships in 2-4 weeks
FREE Shipping for Club Members help

Overview

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.


This item is Non-Returnable

Details

  • ISBN-13: 9789819531288
  • ISBN-10: 9819531284
  • Publisher: Springer
  • Publish Date: January 2026
  • Dimensions: 9.38 x 6.41 x 0.71 inches
  • Shipping Weight: 1.25 pounds
  • Page Count: 232

Related Categories

You May Also Like...

    1

BAM Customer Reviews