Difference between revisions of "Tim Ebbels"
Viniciusveri (talk | contribs) |
FitriAmalia (talk | contribs) |
||
| Line 5: | Line 5: | ||
''' Biography''' | ''' Biography''' | ||
| − | + | Prof Tim Ebbels obtained his PhD in astrophysics from the University of Cambridge and in 1998 moved into bioinformatics via postdoctoral work at Imperial College London. His group focuses on the application of bioinformatic, machine learning and chemometric techniques to post-genomic data, with a particular emphasis on computational metabolomics. He has worked on projects ranging from environmental monitoring, through molecular epidemiology, to toxicogenomics and high-performance computing infrastructures. Much work focuses on modelling of the analytical technologies used to obtain metabolomic data, but his group is also addressing problems of data integration, visualisation, network analysis, time series and metabolite annotation. He is particularly known for the ‘BATMAN’ software for analysing complex metabolic NMR spectra. Tim is an active member of the metabolomics community, having served as a Director of the international Metabolomics Society from 2012-2018 (Secretary from 2014-16). He has co-organised several international conferences (international scientific committee Metabolomics 2014-17) and is a co-founder of the London Metabolomics Network. He is a member of the OECD Metabolomics Reporting Framework, co-chaired the ECETOC Metabolomics Standards Initiative in Toxicology (MERIT) and is an editorial board member for BMC Bioinformatics and the Journal of Chemometrics. He has a strong commitment to postgraduate education, serving as Director of the MRes in Biomedical Research at Imperial College (>700 students trained), leading its Data Science stream and leading the Data Analysis short course at the Imperial’s International Phenome Training Centre. He is a Fellow of the Royal Society of Chemistry. | |
==Expert Opinion== | ==Expert Opinion== | ||
Revision as of 09:53, 10 January 2022
Contents
Short Biography
Biography
Prof Tim Ebbels obtained his PhD in astrophysics from the University of Cambridge and in 1998 moved into bioinformatics via postdoctoral work at Imperial College London. His group focuses on the application of bioinformatic, machine learning and chemometric techniques to post-genomic data, with a particular emphasis on computational metabolomics. He has worked on projects ranging from environmental monitoring, through molecular epidemiology, to toxicogenomics and high-performance computing infrastructures. Much work focuses on modelling of the analytical technologies used to obtain metabolomic data, but his group is also addressing problems of data integration, visualisation, network analysis, time series and metabolite annotation. He is particularly known for the ‘BATMAN’ software for analysing complex metabolic NMR spectra. Tim is an active member of the metabolomics community, having served as a Director of the international Metabolomics Society from 2012-2018 (Secretary from 2014-16). He has co-organised several international conferences (international scientific committee Metabolomics 2014-17) and is a co-founder of the London Metabolomics Network. He is a member of the OECD Metabolomics Reporting Framework, co-chaired the ECETOC Metabolomics Standards Initiative in Toxicology (MERIT) and is an editorial board member for BMC Bioinformatics and the Journal of Chemometrics. He has a strong commitment to postgraduate education, serving as Director of the MRes in Biomedical Research at Imperial College (>700 students trained), leading its Data Science stream and leading the Data Analysis short course at the Imperial’s International Phenome Training Centre. He is a Fellow of the Royal Society of Chemistry.
Expert Opinion
Question 1
1. Q
Answer
Question 2
2. Q
Answer
Question 3
3. Q
Answer
Question 4
4. Q
Answer
Question 5
5. Q
Answer
References
1. Witting, M., et al., DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model. Analytical and Bioanalytical Chemistry, 2015. 407(4): p. 1059-1073.
2. Salzer, L. and M. Witting, Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode. Metabolites, 2021. 11(5): p. 284.
3. Witting, M. and S. Böcker, Current status of retention time prediction in metabolite identification. Journal of Separation Science, 2020. 43(9-10): p. 1746-1754.
4. Dirksen, P., et al., CeMbio - The Caenorhabditis elegans Microbiome Resource. G3: Genes|Genomes|Genetics, 2020. 10(9): p. 3025-3039.
5. Rappez, L., et al., SpaceM reveals metabolic states of single cells. Nature Methods, 2021. 18(7): p. 799-805.
