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10.1093/nargab/lqac014 http://scihub22266oqcxt.onion/10.1093/nargab/lqac014 35265835!8900155!35265835     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35265835&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       NAR+Genom+Bioinform 2022 ; 4 (1): lqac014 Nephropedia Template TP {{tp|p=35265835|t=2022. Feature selection for kernel methods in systems biology |pdf=|usr=}}{{35265835}} gab.com Text Feature selection for kernel methods in systems biology JO: NAR Genom Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=35265835 #FOAvip The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a l(1) penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/. Twit Text FOAVip Feature selection for kernel methods in systems biology ????NAR Genom Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=35265835 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35265835 #FOAvip English Wikipedia <ref>{{Cite pmid|35265835}}</ref> Feature selection for kernel methods in systems biology #MMPMID35265835 Brouard C; Mariette J; Flamary R; Vialaneix N NAR Genom Bioinform 2022[Mar]; 4 (1): lqac014 PMID35265835show ga The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a l(1) penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/. ?Feature selection for kernel methods in systems biology (epmc).pdf DeepDyve Pubget Overpricing