Answers.Body: <p>There are plenty of books when we talk about bioinformatics but only a few of them are available for application of machine learning in bioinformatics. From these selected books I would suggest that you go ahead with <a href="https://www.springer.com/gp/book/9789811524448" rel="nofollow noreferrer">Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications</a>. Its a recent book published in 2020 and covers most of the important topics in bioinformatics along with their ML applications. Apart from that you can also refer <a href="https://www.wiley.com/en-in/Machine+Learning+in+Bioinformatics-p-9780470116623" rel="nofollow noreferrer">Machine Learning in Bioinformatics</a> it’s quite old but still it covers most of the concepts with in-depth explanation. Along with that you can refer to this amazing book <a href="https://compgenomr.github.io/book/" rel="nofollow noreferrer">Computational Genomics with R</a> available free online. This will cover both theory and code for different ML algorithms (both supervised and unsupervised) with their application in bioinformatics. It also covers important topics like RNA Seq Analysis, ChIP Seq Analysis in depth with code explanation of different real world examples which gives you head start in this beautiful subject.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-10T22:17:40.410Z
Answers.Text: The signature thing is frowned upon here too. I guess that is the reason why your post has been so heavily downvoted.
Answers.UserDisplayName: Ram RS
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Answers.Text: Thanks a lot.  Computational Genomics with R  is really interesting
Answers.UserDisplayName: The August
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Answers.Body: <p>One could read <a href="https://rads.stackoverflow.com/amzn/click/com/026202506X" rel="nofollow noreferrer" rel="nofollow noreferrer"><em>Bioinformatics: The machine learning approach</em></a> as an overview of the established applications of machine learning to sequence alignment, RNA structure prediction, etc...</p>
  <p>...Yet, this book is 20 years old, and there have been many important developments in bioinformatics and machine learning in this period, which have not yet been covered in any books. In other words, reading books on bioinformatics and/or machine learning is good for getting firm basic background in either of them, but any such books are inevitably out of date at the time of their publication.</p>
  <p>I would like however to stress the importance of the basic background - one regularly sees announcements about <em>breakthroughs</em> achieved by reinventing techniques that were already described in the book that I mentioned.</p>
  
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Body: <p>I'm looking for books on applications of machine learning and statistical data mining in bioinformatics with example codes provided in R and/or python.</p>
  <p>I came across the following two books: the first being a quite good one, provides the code in MATLAB, which I'm not very familiar with. The second one too is a good book with examples in R.</p>
  <ol>
  <li><p>Feature Selection and Ensemble Methods for Bioinformatics by Oleg Okun</p>
  </li>
  <li><p>Data Science and Predictive Analytics: Biomedical and Health Applications using R by Ivo Dinov</p>
  </li>
  </ol>
  <p>Can people suggest other books based on their first hand experiences?</p>
  <p>Thanks</p>
  
Tags: <machine-learning>
Title: Books on application of Machine Learning in Bioinformatics
ThreadText: # Books on application of Machine Learning in Bioinformatics
  
  (Asked by: username_0 on 2021-02-04)
  
  I'm looking for books on applications of machine learning and statistical data mining in bioinformatics with example codes provided in R and/or python.
  
  I came across the following two books: the first being a quite good one, provides the code in MATLAB, which I'm not very familiar with. The second one too is a good book with examples in R.
  
  1. Feature Selection and Ensemble Methods for Bioinformatics by Oleg Okun
  2. Data Science and Predictive Analytics: Biomedical and Health Applications using R by Ivo Dinov
  
  Can people suggest other books based on their first hand experiences?
  
  Thanks
  
  **Tags:** machine-learning
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-02-10. Score: -2 | Accepted answer
  
  There are plenty of books when we talk about bioinformatics but only a few of them are available for application of machine learning in bioinformatics. From these selected books I would suggest that you go ahead with [Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications](https://www.springer.com/gp/book/9789811524448). Its a recent book published in 2020 and covers most of the important topics in bioinformatics along with their ML applications. Apart from that you can also refer [Machine Learning in Bioinformatics](https://www.wiley.com/en-in/Machine+Learning+in+Bioinformatics-p-9780470116623) it’s quite old but still it covers most of the concepts with in-depth explanation. Along with that you can refer to this amazing book [Computational Genomics with R](https://compgenomr.github.io/book/) available free online. This will cover both theory and code for different ML algorithms (both supervised and unsupervised) with their application in bioinformatics. It also covers important topics like RNA Seq Analysis, ChIP Seq Analysis in depth with code explanation of different real world examples which gives you head start in this beautiful subject.
  
  > **Comments:**
  > **username_2**: The signature thing is frowned upon here too. I guess that is the reason why your post has been so heavily downvoted.  
  > **username_0**: Thanks a lot.  Computational Genomics with R  is really interesting  
  
  ---
  
  ### Answer by username_3 on 2021-02-04. Score: 0
  
  One could read [*Bioinformatics: The machine learning approach*](https://rads.stackoverflow.com/amzn/click/com/026202506X) as an overview of the established applications of machine learning to sequence alignment, RNA structure prediction, etc...
  
  ...Yet, this book is 20 years old, and there have been many important developments in bioinformatics and machine learning in this period, which have not yet been covered in any books. In other words, reading books on bioinformatics and/or machine learning is good for getting firm basic background in either of them, but any such books are inevitably out of date at the time of their publication.
  
  I would like however to stress the importance of the basic background - one regularly sees announcements about *breakthroughs* achieved by reinventing techniques that were already described in the book that I mentioned.
  
  
  ---
  
  