TY - JOUR A1 - Perscheid, Cindy T1 - Comprior BT - Facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets JF - BMC Bioinformatics N2 - Background Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. Results We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance. Conclusion Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness KW - Feature selection KW - Prior knowledge KW - Gene expression KW - Reproducible benchmarking Y1 - 2021 U6 - https://doi.org/10.1186/s12859-021-04308-z SN - 1471-2105 VL - 22 SP - 1 EP - 15 PB - Springer Nature CY - London ER - TY - GEN A1 - Perscheid, Cindy T1 - Comprior: facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets T2 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - Background Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. Results We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance. Conclusion Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 010 KW - Feature selection KW - Prior knowledge KW - Gene expression KW - Reproducible benchmarking Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-548943 SP - 1 EP - 15 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - GEN A1 - Perscheid, Cindy A1 - Uflacker, Matthias T1 - Integrating Biological Context into the Analysis of Gene Expression Data T2 - Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference N2 - High-throughput RNA sequencing produces large gene expression datasets whose analysis leads to a better understanding of diseases like cancer. The nature of RNA-Seq data poses challenges to its analysis in terms of its high dimensionality, noise, and complexity of the underlying biological processes. Researchers apply traditional machine learning approaches, e. g. hierarchical clustering, to analyze this data. Until it comes to validation of the results, the analysis is based on the provided data only and completely misses the biological context. However, gene expression data follows particular patterns - the underlying biological processes. In our research, we aim to integrate the available biological knowledge earlier in the analysis process. We want to adapt state-of-the-art data mining algorithms to consider the biological context in their computations and deliver meaningful results for researchers. KW - Gene expression KW - Machine learning KW - Feature selection KW - Association rule mining KW - Biclustering KW - Knowledge bases Y1 - 2019 SN - 978-3-319-99608-0 SN - 978-3-319-99607-3 U6 - https://doi.org/10.1007/978-3-319-99608-0_41 SN - 2194-5357 SN - 2194-5365 VL - 801 SP - 339 EP - 343 PB - Springer CY - Cham ER - TY - JOUR A1 - Lischeid, Gunnar A1 - Webber, Heidi A1 - Sommer, Michael A1 - Nendel, Claas A1 - Ewert, Frank T1 - Machine learning in crop yield modelling BT - A powerful tool, but no surrogate for science JF - Agricultural and forest meteorology N2 - Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling. KW - Crop modelling KW - Machine learning KW - Random forests KW - Support vector KW - machine KW - Feature selection KW - Equivocality Y1 - 2021 U6 - https://doi.org/10.1016/j.agrformet.2021.108698 SN - 0168-1923 SN - 1873-2240 VL - 312 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zhelayskaya, Irina S. A1 - Vasile, Ruggero A1 - Shprits, Yuri Y. A1 - Stolle, Claudia A1 - Matzka, Jürgen T1 - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index JF - Space Weather: The International Journal of Research and Applications N2 - The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance. KW - Kp index KW - Predictive models KW - Feature selection KW - Machine learning KW - Validation Y1 - 2019 U6 - https://doi.org/10.1029/2019SW002271 SN - 1542-7390 VL - 17 IS - 10 SP - 1461 EP - 1486 PB - American Geophysical Union CY - Washington ER -