- Paperback: 448 pages
- Publisher: Cambridge University Press; 1 edition (September 3, 2007)
- Language: English
- ISBN-10: 0521706947
- ISBN-13: 978-0521706940
- Product Dimensions: 6.9 x 0.8 x 9.7 inches
- Shipping Weight: 1.9 pounds (View shipping rates and policies)
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #3,512,577 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Methods for Computational Gene Prediction 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
"... groundbreaking book..."
This advanced text describes in detail the algorithms and models used to identify genes in genomic DNA sequences. It provides the underlying theory of both established techniques and also methods at the forefront of current research and is ideal for use in a first course in bioinformatics or computational biology.
Top customer reviews
Majoros is articulate and explains complex material with a good balance between equations and prose. Each chapter ends with challenging exercises which probe the reader's grasp of the material. The book is very well-referenced, with 8 pages containing approximately 160 citations. I did my Ph.D. in 1994 on the subject of eukaryotic gene prediction. If you are looking for a single book on the subject for reference or detailed study, this is clearly it.
covering different methods for gene prediction from the basics to
state-of-the-art models that use more exotic approaches. The
information is meticulously organized so that new ideas are introduced
in a logical and almost transparent manner. Thus, it is easy to read,
and quickly provides the reader with an expert understanding of a
This book is appropriate for anyone interested in understanding how
the A, C, G, T letters comprising the genome is decoded into genes,
the first step in decoding the book of life. For people studying
bioinformatics, it should definitely be on your reading list. No
background in computer science or biology is required, although a
familiarity of math at the high school level is needed.
A bonus: the book also provides clear and concise descriptions of
relevant numerical and machine learning methods, e.g. basic
probability, information theory, bayesian networks, etc.
I don't want to give the impression that all this book is good for is hidden markov models, either, though a hefty portion of the book is dedicated to them. It also contains reviews of probability theory, statistics and various machine learning techniques aside from HMMs that are equally clear. I'm not saying that this replaces Durbin, since the focus isn't the same, but it's a great bioinformatics book nonetheless and has won a permanent spot on my shelf.