Chapter title |
Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies
|
---|---|
Chapter number | 28 |
Book title |
Clinical Metabolomics
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7592-1_28 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7591-4, 978-1-4939-7592-1
|
Authors |
Julien Boccard, Serge Rudaz |
Abstract |
Assessing potential alterations of metabolic pathways using large-scale approaches today plays a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies should be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter considering their application to clinical research. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 38% |
Student > Ph. D. Student | 4 | 31% |
Student > Doctoral Student | 2 | 15% |
Student > Master | 1 | 8% |
Unknown | 1 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 23% |
Medicine and Dentistry | 2 | 15% |
Chemistry | 2 | 15% |
Computer Science | 1 | 8% |
Neuroscience | 1 | 8% |
Other | 1 | 8% |
Unknown | 3 | 23% |