https://socictopen.socict.org/files/original/5ee30172084ad367be7c28d031845e86.pdf 6f15a8a5cec6a4f7d466ceccab0f2454 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhanced regulatory sequence prediction using gapped k-mer features. Creator An entity primarily responsible for making the resource Mahmoud Ghandi, Dong Won Lee, Morteza Mohammad-Noori, Michael A Beer Description An account of the resource Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. Date A point or period of time associated with an event in the lifecycle of the resource 2014 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pcbi.1003711 Source A related resource from which the described resource is derived PLoS Computational Biology Publisher An entity responsible for making the resource available Public Library of Science (PLoS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General) Language A language of the resource EN