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Bioinformatics

Welcome to the website of the Professorship of Bioinformatics at TUM Campus Straubing for Biotechnology and Sustainability led by Prof. Dr. Dominik Grimm.

The research lab conducts research in the field of bioinformatics and machine learning, focusing on the development of statistical and machine learning methods to better understand complex biological systems, their functioning and biochemical properties. For this purpose, the lab works on methods to detect genotype-phenotype relationships and to extract phenotypic features from imaging data. These methods have applications in genetics, precision medicine, and precision agriculture. An emerging area of research is the development of artificial intelligence to solve problems in life sciences and chemistry without including specific prior knowledge.

Lab News

Machine Learning Research School in Bangkok: Poster Award für Josef Eiglsperger

Machine Learning Research School in Bangkok: Poster Award für Josef Eiglsperger

Award for Josef Eiglsperger: The doctoral student at the Professorship of Bioinformatics (Prof. Dominik Grimm) at Weihenstephan-Triesdorf University of Applied Sciences (HSWT) at TUM Campus Straubing (TUMCS) received the “Outstanding Poster Award” for his poster and presentation at the Machine Learning Research School (MLRS) in Bangkok. The Research Summer School ran from 02.08. to 09.08.2023 and took place at the Digital Economy Promotion Agency.

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First EIC Transition Funding for TUM

First EIC Transition Funding for TUM

We are delighted to have received funding from the European Innovation Council (EIC) as part of the EIC Transition programme to work with colleagues at the Technical University of Munich (TUM) to develop a new technology that will make it quicker, easier and more accurate to apply fertiliser in the field. Using a combination of biosensor test strips and satellite-based remote sensing data, the Technical University of Munich (TUM) is developing a method to determine the nutritional status of cereals and the perfect amount of fertiliser to apply. The automatic provision of digital analysis data to the tractor terminal should save time and prevent over-fertilisation in the future. The Straubing Campus for Sustainability plays a leading role within the TUM in the development of environmentally friendly technologies. The GrimmLab will be responsible for the development of the machine learning related parts for fertilizer recommendations.

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New Paper: Policy-Based Self-Competition for Planning Problems

New Paper: Policy-Based Self-Competition for Planning Problems

New paper at International Conference on Learning Representation (ICLR): “Policy-Based Self-Competition for Planning Problems”. AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well. One technique to address this problem is transforming the single-player task through self-competition. The main idea is to compute a scalar baseline from the agent’s historical performances and to reshape an episode’s reward into a binary output, indicating whether the baseline has been exceeded or not. However, this baseline only carries limited information for the agent about strategies how to improve. We leverage the idea of self-competition and directly incorporate a historical policy into the planning process instead of its scalar performance. Based on the recently introduced Gumbel AlphaZero (GAZ), we propose our algorithm GAZ ‘Play-to-Plan’ (GAZ PTP), in which the agent learns to find strong trajectories by planning against possible strategies of its past self. We show the effectiveness of our approach in two well-known combinatorial optimization problems, the Traveling Salesman Problem and the Job-Shop Scheduling Problem. With only half of the simulation budget for search, GAZ PTP consistently outperforms all selected single-player variants of GAZ.

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Kontakt

Professorship Bioinformatics

Petersgasse 18
94315 Straubing

Head

Prof. Dr. Dominik Grimm

Phone: +49 (0) 9421 187-230
Fax: +49 (0) 9421 187-285
E-Mail: dominik.grimm@hswt.de

Team Assistant

Anna Fischer

Phone: +49 (0) 9421 187-231
Fax: +49 (0) 9421 187-285
E-Mail: anna.fischer@hswt.de