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10.1093/bioinformatics/bty277 http://scihub22266oqcxt.onion/10.1093/bioinformatics/bty277 C6022556!6022556!29949975     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2018 ; 34 (13): i509-18 Nephropedia Template TP {{tp|p=29949975|t=2018. Learning with multiple pairwise kernels for drug bioactivity prediction |pdf=|usr=}}{{29949975}} gab.com Text Learning with multiple pairwise kernels for drug bioactivity prediction JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29949975 #FOAvip Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167?995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco. Supplementary information: Supplementary data are available at Bioinformatics online. Twit Text FOAVip Learning with multiple pairwise kernels for drug bioactivity prediction ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29949975 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29949975 #FOAvip English Wikipedia <ref>{{Cite pmid|29949975}}</ref> Learning with multiple pairwise kernels for drug bioactivity prediction #MMPMID29949975 Cichonska A; Pahikkala T; Szedmak S; Julkunen H; Airola A; Heinonen M; Aittokallio T; Rousu J Bioinformatics 2018[Jul]; 34 (13): i509-18 PMID29949975show ga Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167?995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco. Supplementary information: Supplementary data are available at Bioinformatics online. äLearning with multiple pairwise kernels for drug bioactivity predictio(epmc).pdf DeepDyve Pubget Overpricing