The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
Open accessAbstract
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.
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Opportunities and obstacles for deep learning in biology and medicine
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Big Bird: Transformers for Longer Sequences
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Metadata
- Authors
- Ghandi, M., Lee, D., Mohammad-Noori, M., & Beer, M. A.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-07-17
- Volume
- 10
- Issue
- 7
- Pages
- e1003711
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W1988581590
- Cited by
- 489
- References
- 38
- FWCI
- 10.10
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ghandi, M., Lee, D., Mohammad-Noori, M., & Beer, M. A. (2014). Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1003711
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