Experiments show that the MLVS-based classifiers are able to outperform or perform on par with several existing methods that are specifically designed for the ...

Abstract: Analysing and classifying sequences based on similarities and differences is a mathematical problem of escalating relevance and importance.

We demonstrate the usefulness of the model by applying it to the problem of identifying signal peptides. MLVS feature vectors are generated from a collection of ...

Experiments show that the MLVS-based classifiers are able to outperform or perform on par with several existing methods that are specifically designed for the ...

Exploiting multi-layered vector spaces for signal peptide detection. Int. J. Data Min. Bioinform. 13(2): 141-157 (2015). [+][–]. Coauthor network. maximize.

Butler, L. Pannell, and M, Tan, "Exploiting Multi-Layered Vector Spaces for Signal Peptide Detection", International Journal of Data Mining and Bioinformatics, ...

2015 Vol.13 No.2 ; 141-157, Exploiting multi-layered vector spaces for signal peptide detection. Tom Johnsten; Laura Fain; Leanna Fain; Ryan G. Benton; Ethan ...

Exploiting multi-layered vector spaces for signal peptide detection. T Johnsten, L Fain, L Fain, RG Benton, E Butler, L Pannell, M Tan. International Journal ...

Tom Johnsten Associate Professor ... Exploiting multi-layered vector spaces for signal peptide detection. Johnsten, Tom⋅Fain, Laura⋅Fain, Leanna⋅Benton, Ryan ...

Aug 7, 2022 · We have developed a computational framework for constructing synthetic signal peptides from a base set of protein sequences.