News

We welcome our new Team Assistant Anna in our team.

Video about the project EWIS: Evaluation and development of modern methods of artificial intelligence for automatic weed detection in sorghum using drones

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to provide an overview of a typical RNA-Seq experiment that includes extraction of sample RNA, enrichment, and cDNA library preparation. Next, we review tools for quality control and data pre-processing followed by a standard workflow to perform RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies, that is a reference-based alignment and a de novo assembly approach. We learn how to do basic downstream analyses of RNA-Seq data, including quantification of expressed genes, differential gene expression (DE) between different groups as well as functional gene analysis. Eventually, we provide a best-practice example for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNA-Seq-Analyses.

We are happy to share an interview Richa gave for the portal “Research in Germany”. In this interview Richa gave insights about her research in Germany, and what she likes about living here.

New publication about “Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops” just got published in Plant Methods.

Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.

New publication about “Network-guided search for genetic heterogeneity between gene pairs” just got published in Bioinformatics: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa581/5861532

We propose a novel method for finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple A. thaliana phenotypes, and for a study of rare variants in migraine patients.

Zierpflanzen, Stauden oder Schnittblumen sind hierzulande extrem beliebt, vor allem in der Woche vor Muttertag brummt das Geschäft. Dieses Jahr allerdings ist die Nachfrage nach Blumenschmuck oder Sträußen trotz Muttertag eingebrochen, weil aufgrund der Corona-Pandemie viele Veranstaltungen wie Hochzeiten abgesagt wurden. Das Problem, dass die Nachfrage nach gärtnerischen Produkten stark von externen Faktoren wie Witterung, Feiertagen, regionalen Veranstaltungen oder anderen – oftmals noch unbekannten – Einflussfaktoren abhängig ist, besteht für viele Wertschöpfungsketten im Gartenbau.