Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare twelve 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 allows 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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Jan D Hüwel,
Prof. Dr. Florian Haselbeck,
Prof. Dr. Dominik Grimm,
Christian Beecks
One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing Event Triggered Kernel Adjustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA's ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.Code availability: https://github.com/JanHuewel/ETKA
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Prof. Dr. Dominik Grimm
Towards a better understanding of the genetic architecture of complex traits (2022) Keynote @TüBMI 2022, Tübinger Bioinformatics and Medical Informatics Days 2022 .
Quirin Göttl,
Prof. Dr. Dominik Grimm,
Prof. Dr.-Ing. Jakob Burger
The present work uses reinforcement learning (RL) for automated flowsheet synthesis. The task of synthesizing a flowsheet is reformulated into a two-player game, in which an agent learns by self-play without prior knowledge. The hierarchical RL scheme developed in our previous work (Göttl et al., 2021b) is coupled with an improved training process. The training process is analyzed in detail using the synthesis of ethyl tert-butyl ether (ETBE) as an example. This analysis uncovers how the agent’s evolution is driven by the two-player setup.
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M.Sc. Sara Omranian,
Zoran Nikoloski,
Prof. Dr. Dominik Grimm
Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field.
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Indrajit Nanda,
Claus Steinlein,
Thomas Haaf,
Eva M. Buhl,
Prof. Dr. Dominik Grimm,
Scott L Friedman,
Steffen K. Meurer,
Sarah K Schröder,
Ralf Weiskirchen
Immortalized hepatic stellate cells (HSCs) established from mouse, rat, and humans are valuable in vitro models for the biomedical investigation of liver biology. These cell lines are homogenous, thereby providing consistent and reproducible results. They grow more robustly than primary HSCs and provide an unlimited supply of proteins or nucleic acids for biochemical studies. Moreover, they can overcome ethical concerns associated with the use of animal and human tissue and allow for fostering of the 3R principle of replacement, reduction, and refinement proposed in 1959 by William M. S. Russell and Rex L. Burch. Nevertheless, working with continuous cell lines also has some disadvantages. In particular, there are ample examples in which genetic drift and cell misidentification has led to invalid data. Therefore, many journals and granting agencies now recommend proper cell line authentication. We herein describe the genetic characterization of the rat HSC line HSC-T6, which was introduced as a new in vitro model for the study of retinoid metabolism. The consensus chromosome markers, outlined primarily through multicolor spectral karyotyping (SKY), demonstrate that apart from the large derivative chromosome 1 (RNO1), at least two additional chromosomes (RNO4 and RNO7) are found to be in three copies in all metaphases. Additionally, we have defined a short tandem repeat (STR) profile for HSC-T6, including 31 species-specific markers. The typical features of these cells have been further determined by electron microscopy, Western blotting, and Rhodamine-Phalloidin staining. Finally, we have analyzed the transcriptome of HSC-T6 cells by mRNA sequencing (mRNA-Seq) using next generation sequencing (NGS).
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Prof. Dr. Florian Haselbeck,
Jennifer Killinger,
Prof. Dr. Klaus Menrad,
Prof. Dr. Thomas Hannus,
Prof. Dr. Dominik Grimm
Forecasting future demand is of high importance for many companies as it affects operational decisions. This is especially relevant for products with a short shelf life due to the potential disposal of unsold items. Horticultural products are highly influenced by this, however with limited attention in forecasting research so far. Beyond that, many forecasting competitions show a competitive performance of classical forecasting methods. For the first time, we empirically compared the performance of nine state-of-the-art machine learning and three classical forecasting algorithms for horticultural sales predictions. We show that machine learning methods were superior in all our experiments, with the gradient boosted ensemble learner XGBoost being the top performer in 14 out of 15 comparisons. This advantage over classical forecasting approaches increased for datasets with multiple seasons. Further, we show that including additional external factors, such as weather and holiday information, as well as meta-features led to a boost in predictive performance. In addition, we investigated whether the algorithms can capture the sudden increase in demand of horticultural products during the SARS-CoV-2 pandemic in 2020. For this special case, XGBoost was also superior. All code and data is publicly available on GitHub: https://github.com/grimmlab/HorticulturalSalesPredictions.
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Prof. Dr. Dominik Grimm,
Quirin Göttl,
Prof. Dr.-Ing. Jakob Burger
Reinforcement Learning für die automatisierte Fließbildsynthese (2021) AI4Life, KI Symposium .
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