New perspectives of bacteria identification by mass spectrometry and machine learning

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• Dr. Frank-Michael Schleif, Medical Department, Leipzig University
• Monday 22 February 2010, 16:00-17:00
• UG40, School of Computer Science.

If you have a question about this talk, please contact Per Kristian Lehre.

The automatic identification of bacteria is a very important topic in different fields of medicine but also in cases where impurities are an issue like in the food industry. Recent achievements in mass spectrometry have simplified this task but the quick and safe identification of bacteria is still challenging.

The talk gives a short introduction to mass spectrometry based identification of bacteria and presents an advanced machine learning approach to identify measured samples with respect to a database of known bacteria signatures. I will highlight some critical points in the current approaches and shown how some of them can be overcome. The presented approach is based on the famous tree based self organizing map, a prototype based learning method and is extended to fit to the considered analysis task. Initial results are presented and potential future research directions.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG40, School of Computer Science
• computer sience

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