{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Matlock K"],"funding":["National Institutes of Health","NIGMS NIH HHS"],"pagination":["3143-3145"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6736036"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["35(17)"],"pubmed_abstract":["<h4>Summary</h4>Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples.<h4>Availability and implementation</h4>The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further e"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features."],"pmcid":["PMC6736036"],"funding_grant_id":["R01 GM122084","R01GM122084"],"pubmed_authors":["Rahman R","Ghosh S","Matlock K","Pal R"],"additional_accession":[]},"is_claimable":false,"name":"Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features.","description":"<h4>Summary</h4>Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples.<h4>Availability and implementation</h4>The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further e","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Sep","modification":"2025-04-22T14:19:29.882Z","creation":"2020-09-04T07:03:17Z"},"accession":"S-EPMC6736036","cross_references":{"pubmed":["30649230"],"doi":["10.1093/bioinformatics/btz010"]}}