Dominik Grimm, Autor bei BIT - TUM Campus Straubing

New Paper: easyPheno, an easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization

New paper in Bioinformatics Advances: “easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization”. Predicting complex traits from genotypic information is a major challenge in various biological domains. With easyPheno, we present a comprehensive Python framework enabling the rigorous training, comparison and analysis of phenotype predictions for a variety of different models, ranging from common genomic selection approaches over classical machine learning and modern deep learning-based techniques. Our framework is easy-to-use, also for non-programming-experts, and includes an automatic hyperparameter search using state-of-the-art Bayesian optimization. Moreover, easyPheno provides various benefits for bioinformaticians developing new prediction models. easyPheno enables to quickly integrate novel models and functionalities in a reliable framework and to benchmark against various integrated prediction models in a comparable setup. In addition, the framework allows the assessment of newly developed prediction models under pre-defined settings using simulated data. We provide a detailed documentation with various hands-on tutorials and videos explaining the usage of easyPheno to novice users.

New StMELF Funding: AI for Weed Regulation with Robots

We are happy to receive funding from the Bavarian State Ministry of Food, Agriculture and Forestry to continue or research about developing novel machine learning techniques for weed identification in drone imagery to enable automatic weed removal due to autonomous robots on agricultural fields.

Dominik talks at the Computomics Podcast about ML for agriculture

In this episode Dominik gives us insights into CropML, a BMBF funded project. The project evaluates new machine learning techniques for more accurate plant breeding by integrating heterogeneous external factors. Different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques have been compared. Learn why advanced models are the future and where the challenges are.

New Paper: HeliantHOME, a public and centralized database of phenotypic sunflower data

New paper in Scientific Data: “HeliantHOME, a public and centralized database of phenotypic sunflower data”. We have created HeliantHOME (http://www.helianthome.org), a curated, public, and interactive database of phenotypes including developmental, structural and environmental ones, obtained from a large collection of both wild and cultivated sunflower individuals.

New Paper: A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

New paper: “A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species”. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.