logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications 1st Edition K G Srinivasa Editor

  • SKU: BELL-11035374
Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications 1st Edition K G Srinivasa Editor
$ 31.00 $ 45.00 (-31%)

4.7

96 reviews

Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications 1st Edition K G Srinivasa Editor instant download after payment.

Publisher: Springer Nature
File Extension: PDF
File size: 9.58 MB
Pages: 332
Author: K. G. Srinivasa (editor), G. M. Siddesh (editor), S. R. Manisekhar (editor)
ISBN: 9789811524448, 9811524440
Language: English
Year: 2020
Edition: 1

Product desciption

Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications 1st Edition K G Srinivasa Editor by K. G. Srinivasa (editor), G. M. Siddesh (editor), S. R. Manisekhar (editor) 9789811524448, 9811524440 instant download after payment.

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

 

Related Products