News

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

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.

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New Paper: Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields

New Paper: Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields

New paper about “Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields”. In this work, we developed and analysed different deep learning-based architectures to accurately segment crop and weed species in UAV images of agricultural fields under challenging capturing conditions. For this propose, we created an expert-curated fully-annotated weed segmentation UAV dataset in sorghum fields. We show that our trained models have high precision in detecting general plant shapes with minor weaknesses at borders of the plants. More importantly, our method is capable in segmenting intra-row and partly occluded weeds on an individual plant basis. All code and data are publicly available on GitHub and Mendeley Data.

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New Paper: Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes

New Paper: Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes

New paper about “Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes”. In this work, we systematically analyzed gene cassettes from more than 1800 bacterial for the abundance of transcriptional and translational associated genes clustered in distinct gene cassettes. We identified three highly frequent cassettes containing transcriptional and translational genes. Interestingly, each of the three cassettes harbors a gene (nusG, rpsD and nusA) encoding a protein which links transcription and translation in bacteria. Furthermore, our analyses suggest an enrichment of these gene cassettes in pathogenic bacterial phyla.

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New Journal Paper about Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

New Journal Paper about Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

Sara published a review paper in a collaboration with Zoran Nikoloski from the Max Planck Institute of Molecular Plant Physiology and the Intitute of Biochemistry and Biology at the University of Potsdam about the “Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward”

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Josef joins the Team as Research Assistant

Josef joins the Team as Research Assistant

Josef joins the team as research assistant. He will work on novel machine learning methods for time series forecasting within the project “Digital management support systems for small and medium-sized enterprises in value chains of ornamental plants, perennials and cut flowers (PlantGrid)”, funded by the Federal Office of Food and Agriculture.

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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