About the book
An introduction to machine learning methods and their applications to problems in bioinformatics
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.
From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.
Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Editions of Machine Learning in Bioinformatics
- Hardcover
- ISBN 9780470116623
- EBook
- ISBN 9780470397428
- EBook
- ISBN 9780470397411
Read an Excerpt
An introduction to machine learning methods and their applications to problems in bioinformatics
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.
From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.
Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Frequently Asked Questions
What is Machine Learning in Bioinformatics about?
An introduction to machine learning methods and their applications to problems in bioinformatics
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.
From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.
Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
What core themes, tropes, or subjects are explored in Machine Learning in Bioinformatics?
Computing and Information Technology > Computer science
Where can I read a sample of Machine Learning in Bioinformatics?
You can read an official preview of the few pages here https://www.book2look.com/book/9780470116623
Is Machine Learning in Bioinformatics part of a series, and can it be read as a standalone?
Yes it is a part of series Wiley Series in Bioinformatics
Who is/are the Author/s of the book Machine Learning in Bioinformatics?
John Wiley & Sons
Who is the Publisher of the book Machine Learning in Bioinformatics?
Wiley
What are the ISBN numbers for the physical and digital editions?
Machine Learning in Bioinformatics is available as hardcover(ISBN 9780470116623) and ebook(ISBN 9780470397428) and ebook(ISBN 9780470397411)
Where can I buy Machine Learning in Bioinformatics online or support local independent bookshops?
You can buy Machine Learning in Bioinformatics from below
Where can I buy Machine Learning in Bioinformatics?