GkmSVM: An R package for gapped-kmer SVM

Bioinformatics

Volumn 32, Issue 14, 2016, Pages 2205-2207

  Ghandi, Mahmoud ^a   Mohammad Noori, Morteza ^b   Ghareghani, Narges ^b   Lee, Dongwon ^c   Garraway, Levi ^a,d   Beer, Michael A ^c,e  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b UNIVERSITY OF TEHRAN   (Iran)

^c JOHNS HOPKINS UNIVERSITY   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; DNA SEQUENCE; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE; SUPPORT VECTOR MACHINE;

ALGORITHMS; SEQUENCE ANALYSIS, DNA; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 84992398648     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw203     Document Type: Article

References (14)

1. Fletez-Brant, C. et al. (2013) kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Res., 41, W544-W556.
2. Ghandi, M. (2012) A sequence based model for nucleosome positioning in yeast. (Doctoral dissertation). Proquest/UMI 3537355.
3. Ghandi, M. et al. (2014a) Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol., 10, e1003711.
4. Ghandi, M. et al. (2014b) Robust k-mer frequency estimation using gapped kmers. J. Math. Biol., 69, 469-500.
5. Gorkin, D.U. et al. (2012) Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes. Genome Res., 22, 2290-2301.
6. Graham, R.L. et al. (1994) Concrete Mathematics: A Foundation for Computer Science 2nd ed. Addison and Wesley, Boston.
7. Lee, D. et al. (2011) Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res., 21, 2167-2180.
8. Lee, D. et al. (2015) A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet., 47, 955-961.
9. Lee, D. (2016) LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics, inpress.
10. Leslie, C. et al. (2004) Mismatch string kernels for discriminative protein classification. Bioinformatics, 20, 467-476.
11. Leslie, C. and Kuang, R. (2004) Fast String kernels using inexact matching for protein sequences. J. Mach. Learn. Res., 5, 1435-1455.
12. Mo, A. et al. (2016) Epigenomic landscapes of retinal rods and cones. eLife, 5, e11613.
13. Pimkin, M. et al. (2014) Divergent functions of hematopoietic transcription factors in lineage priming and differentiation during erythro-megakaryopoiesis. Genome Res., 24, 1932-1944.
14. Svetlichnyy, D. et al. (2015) Identification of high-impact cis-regulatory mutations using transcription factor specific random forest models. PLoS Comput. Biol., 11, e1004590.