Proteome Informatics
Conrad Bessant
Sold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
New - Hardcover
Condition: New
Quantity: 1 available
Add to basketSold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
Condition: New
Quantity: 1 available
Add to basketNeuware - The field of proteomics has developed rapidly over the past decade, with many new instruments, protocols and biological applications appearing. The informatics aspects of proteomics have evolved at an equally rapid pace, with many new tools, new algorithms, and improvements to best practice being published. Providing a detailed introduction to the main informatics topics that underpin the various LC-MS/MS protocols used for protein identification and quantitation, this book brings together the key proteome informatics methodologies for researchers and practising analytical scientists.
Seller Inventory # 9781782624288
The field of proteomics has developed rapidly over the past decade nurturing the need for a detailed introduction to the various informatics topics that underpin the main liquid chromatography tandem mass spectrometry (LC-MS/MS) protocols used for protein identification and quantitation. Proteins are a key component of any biological system, and monitoring proteins using LC-MS/MS proteomics is becoming commonplace in a wide range of biological research areas. However, many researchers treat proteomics software tools as a black box, drawing conclusions from the output of such tools without considering the nuances and limitations of the algorithms on which such software is based. This book seeks to address this situation by bringing together world experts to provide clear explanations of the key algorithms, workflows and analysis frameworks, so that users of proteomics data can be confident that they are using appropriate tools in suitable ways.
Conrad Bessant is Professor of Bioinformatics at Queen Mary University of London. He has particular interests in proteomics, software development and machine learning and is striving to ensure that everyone using proteomics data can access the latest analysis methods and knows how to use them in the most effective way.
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