Neuf :
57,26€57,26€
Retours GRATUITS
Livraison à 0,01 € samedi 18 novembre. Détails
Ou livraison accélérée jeudi 16 novembre. Commandez dans les 2 h 50 min. Détails
Il ne reste plus que 3 exemplaire(s) en stock.
Expédié par
Amazon
Vendu par
Amazon
Retours
Retournable jusqu'au 31 janvier 2024
Paiement
Transaction sécurisée
Achetez d'occasion 28,36 €
Téléchargez l'application Kindle gratuite et commencez à lire des livres Kindle instantanément sur votre smartphone, tablette ou ordinateur - aucun appareil Kindle n'est requis.
Lisez instantanément sur votre navigateur avec Kindle pour le Web.
Utilisation de l'appareil photo de votre téléphone portable - scannez le code ci-dessous et téléchargez l'application Kindle.
Image indisponible
couleur :
-
-
-
- Pour voir cette vidéo, téléchargez Flash Player
Suivre ces auteurs
OK
Computational Genome Analysis: An Introduction Relié – 13 août 2007
| Prix Amazon | Neuf à partir de | Occasion à partir de |
|
Format Kindle
"Veuillez réessayer" | — | — |
- Format Kindle
40,08 € Lisez avec notre Appli gratuite - Relié
57,26 €3 autres D'occasion 6 autres Neuf - Broché
37,75 €1 autres D'occasion 3 autres Neuf
Options d'achat et paniers Plus
This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.
- Nombre de pages de l'édition imprimée535 pages
- LangueAnglais
- ÉditeurSpringer-Verlag New York Inc.
- Date de publication13 août 2007
- Dimensions15.24 x 2.54 x 23.5 cm
- ISBN-100387987851
- ISBN-13978-0387987859
Description du produit
Biographie de l'auteur
Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.
Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.
Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.
Détails sur le produit
- Éditeur : Springer-Verlag New York Inc.; 1st ed. 2005. Corr. 3rd printing 2007 édition (13 août 2007)
- Langue : Anglais
- Relié : 535 pages
- ISBN-10 : 0387987851
- ISBN-13 : 978-0387987859
- Poids de l'article : 2,15 Kilograms
- Dimensions : 15.24 x 2.54 x 23.5 cm
- Commentaires client :
À propos des auteurs

Découvrir d'autres livres de l'auteur, voir des auteurs similaires, lire des blogs d'auteurs et plus encore

Découvrir d'autres livres de l'auteur, voir des auteurs similaires, lire des blogs d'auteurs et plus encore
Commentaires client
Les avis clients, y compris le nombre d’étoiles du produit, aident les clients à en savoir plus sur le produit et à décider s'il leur convient.
Pour calculer le nombre global d’étoiles et la ventilation en pourcentage par étoile, nous n'utilisons pas une simple moyenne. Au lieu de cela, notre système prend en compte des éléments tels que la date récente d'un commentaire et si l'auteur de l'avis a acheté l'article sur Amazon. Les avis sont également analysés pour vérifier leur fiabilité.
En savoir plus sur le fonctionnement des avis clients sur AmazonMeilleurs commentaires provenant d’autres pays
This is my first time taking any coursework in the bioinformatics field so perhaps it is simply because this material is new to me, but I found this book fairly difficult to read. I had to supplement it with other books, wikipedia entries, etc. to be able to understand many of the terms (which this book fails to define).
If you're willing to put forth the effort of filling in the gaps, then this is a great book. If you already have a strong background in computer science and biology then this is likely an excellent book for reference material, or to expand you knowledge in an already familiar area.
Also note that there is a large amount of discussion of probability in this area of study. You may wish to brush up on your skills in probability prior to reading this.
The book is really a good intro to the subject. This was my first book on the subject and I think I did the right choice and ended up with a very good feeling on what means the application of computers and statistics on genome stuff. But I think the book's title rather be "Statistical Genome Analysis," due to the fact that the authors give more strength on the statistics techniques used when analyzing genome data, what is cool. "Computational" is tied to some R codes, shown throughout the book, actually, very good hints on using R to do some basic stuff with genome data. Of course, due to the date of publication of the book (2005) many web links are outdated or doesn't exist any more. But nothing that a Google search couldn't solve it. And, of course, due to the accelerated advance of the technology in the field of genomics, like sequencing, some concepts are outdated, too. I have heard from some bioinformatics PhD that microarray tech, for example, is with its days numbered, entering RNA-seq.
Of course, this doesn't take the merits of the book. If you, reader of this note, is interested in buying the book, go on and do it! You, like myself, will not be disappointed.