Machine Learning for Weed Detection (Video)
Video about the project EWIS: Evaluation and development of modern methods of artificial intelligence for automatic weed detection in sorghum using drones
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 seeking highly motivated candidates for a scientific research position as Postdoctoral Researcher, to further advance the recently launched Synthetic Biology Foundry at TUM Campus Straubing (SynBiofoundry@TUM).
Maura will work on novel bioinformatics and machine learning techniques to gain a better understanding of genotype-phenotype relationships.
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.