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Search · four archives
11 papers · ranked by Valyu relevance
Danny Lu, Aalim Weljie, Alexander R. de Leon, Yarrow McConnell + 2 more
'Oliver F. Bathe' 'Karen Kopciuk'] Background Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary…
Taneli Pusa, Juho Rousu, Kai Wang
Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking…
Andreas Mayr, Benjamin Hofner, Elisabeth Waldmann, Tobias Hepp + 2 more
'Sebastian Meyer' 'Olaf Gefeller'] Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of…
Benjamin Hofner, Luigi Boccuto, Markus Göker
Background Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n≪p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from…
Yidi Deng, Jiadong Mao, Jarny Choi, Kim-Anh Lê Cao
Inferring reproducible relationships between biological variables remains a challenge in the statistical analysis of omics data. For example, methods that identify statistical associations may lack interpretability or reproducibility. The situation can be greatly improved, however, by introducing the measure of…
Yonghan Kwon, Kyunghwa Han, Young Joo Suh, Inkyung Jung
Stability selection is a variable selection algorithm based on resampling a dataset. Based on stability selection, we propose weighted stability selection to select variables by weighing them using the area under the receiver operating characteristic curve (AUC) from additional modelling. Through an extensive…
Anyou Wang, Rong Hai
Numerous software have been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hampers their application to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork)…
Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data…
Karan Uppal, Eva K. Lee
Recent studies have shown that the ensemble feature selection approaches are essential for generating robust classifiers. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and do not account for classification accuracy while…
Ivan Lorca-Alonso, Miguel Arenas, Ugo Bastolla
In previous studies, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed…
Roberta Coletti, J. Orestes Cerdeira, Marcos Raydan, Marta B. Lopes
High-dimensional omics data often contain more variables than observations, which negatively impacts the performance of classical data analysis methods. Dimensionality reduction is typically addressed through variable selection strategies that incorporate a penalty term into the model. While effective for selecting…