Guiding questions to avoid data leakage in biological machine learning applications.

Autor: Bernett J; TUM School of Life Sciences, Technical University of Munich, Freising, Germany., Blumenthal DB; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. david.b.blumenthal@fau.de., Grimm DG; TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany. dominik.grimm@tum.de.; Bioinformatics, Weihenstephan-Triesdorf University of Applied Sciences, Straubing, Germany. dominik.grimm@tum.de.; TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany. dominik.grimm@tum.de., Haselbeck F; TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany.; Bioinformatics, Weihenstephan-Triesdorf University of Applied Sciences, Straubing, Germany.; Smart Farming, Weihenstephan-Triesdorf University of Applied Sciences, Freising, Germany., Joeres R; Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.; Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany.; Center for Bioinformatics, Saarland University, Saarbrücken, Germany., Kalinina OV; Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany. olga.kalinina@helmholtz-hips.de.; Center for Bioinformatics, Saarland University, Saarbrücken, Germany. olga.kalinina@helmholtz-hips.de.; Medical Faculty, Saarland University, Homburg, Germany. olga.kalinina@helmholtz-hips.de., List M; TUM School of Life Sciences, Technical University of Munich, Freising, Germany. markus.list@tum.de.; Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany. markus.list@tum.de.

Publikováno v: Nature methods [Nat Methods] 2024 Aug; Vol. 21 (8), pp. 1444-1453. Date of Electronic Publication: 2024 Aug 09.

Superior protein thermophilicity prediction with protein language model embeddings.

Autor: Haselbeck F; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany., John M; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany., Zhang Y; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany., Pirnay J; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany., Fuenzalida-Werner JP; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany., Costa RD; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany., Grimm DG; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany.; Technical University of Munich, TUM School of Computation, Information and Technology (CIT), 85748 Garching, Germany.

Publikováno v: NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 Oct 11; Vol. 5 (4), pp. lqad087. Date of Electronic Publication: 2023 Oct 11 (Print Publication: 2023).

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

Autor: John M; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany., Haselbeck F; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany., Dass R; Computomics GmbH, Tübingen, Germany., Malisi C; Computomics GmbH, Tübingen, Germany., Ricca P; Computomics GmbH, Tübingen, Germany., Dreischer C; Computomics GmbH, Tübingen, Germany., Schultheiss SJ; Computomics GmbH, Tübingen, Germany., Grimm DG; Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.; Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany.; Technical University of Munich, Department of Informatics, Garching, Germany.

Publikováno v: Frontiers in plant science [Front Plant Sci] 2022 Nov 04; Vol. 13, pp. 932512. Date of Electronic Publication: 2022 Nov 04 (Print Publication: 2022).

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Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland.

Autor: Zhang, Jinmeng¹ (AUTHOR) zhangjm@agri.ac.cn, Yu, Feng^1,2 (AUTHOR) yufeng21@mails.ucas.ac.cn, Zhang, Qian¹ (AUTHOR) yujy@agri.ac.cn, Wang, Ming¹ (AUTHOR) tanyr@agri.ac.cn, Yu, Jinying¹ (AUTHOR), Tan, Yarong¹ (AUTHOR)

Publikováno v: Agronomy. Mar2024, Vol. 14 Issue 3, p494. 32p.