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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

New Paper: Forecasting seasonally fluctuating sales of perishable products in the horticultural industry

New Paper: Forecasting seasonally fluctuating sales of perishable products in the horticultural industry

Our latest paper on horticultural demand forecasting has been published in Expert Systems with Applications: “Forecasting seasonally fluctuating sales of perishable products in the horticultural industry”. Accurately forecasting demand is a potential competitive advantage, especially in the context of perishable products such as in the horticultural industry, where the disposal of unsold items results in environmental and financial damage. Despite challenging operational decisions to avoid out-of-stock and overstock situations, horticultural businesses have received limited attention in forecasting research. In addition, horticultural sales are typically highly seasonal. Sudden changes in both directions, rising and falling, characterize horticultural sales cycles.In our study, we explore the research questions of the applicability of general versus dataset-specific predictors, the impact of external information, and online model update schemes. Using a diverse set of real-world horticultural data, we applied three classical and twelve machine learning-based forecasting approaches.💡 Key Findings:Multivariate machine learning models dominate: Our results show the superiority of multivariate machine learning methods over classical forecasting approaches, with the ensemble learner XGBoost emerging as a standout performer.External factors play a critical role: The inclusion of statistical, calendrical, and weather-related features in the feature set is critical for robust performance.Firm-specific predictors outperform general cross-firm models: We find that a generalized model, which would be advantageous in terms of computational resources, maintenance, and transferability to other datasets, falls short in capturing the heterogeneity of horticultural data, highlighting the need for firm-specific predictors.Impact of frequent model updates is negligible: Surprisingly, frequent model updates have a negligible impact on forecast quality, allowing long-term forecasting without significant performance degradation.

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Professorship Bioinformatics

Petersgasse 18
94315 Straubing


Prof. Dr. Dominik Grimm

Phone: +49 (0) 9421 187-230


Team Assistants

Anna Fischer

Phone: +49 (0) 9421 187-231

Jasmin Schneider

Phone: +49 (0) 9421 187-201