Computational framework for generating synthetic signal peptides

Author:

Johnsten Tom1,Prakash Aishwarya1,Daly Grant T.1,Benton Ryan G.1,Clark Tristan1

Affiliation:

1. University of South Alabama

Funder

National Institutes of Environmental Health Sciences

Publisher

ACM

Reference15 articles.

1. Can Akkoç , Tom Johnsten , and Ryan G Benton . 2011 . Multi-layered Vector Spaces for Classifying and Analyzing Biological Sequences. In International Conference on Bioinformatics and Computational Biology, ISCA, 160--166 . Can Akkoç, Tom Johnsten, and Ryan G Benton. 2011. Multi-layered Vector Spaces for Classifying and Analyzing Biological Sequences. In International Conference on Bioinformatics and Computational Biology, ISCA, 160--166.

2. SignalP 5.0 improves signal peptide predictions using deep neural networks

3. Stephen D. Bay and Michael J. Pazzani. 1999 . Detecting Change in Categorical Data: Mining Contrast Sets. In International Conference on Machine Learning, 302--306 . Stephen D. Bay and Michael J. Pazzani. 1999. Detecting Change in Categorical Data: Mining Contrast Sets. In International Conference on Machine Learning, 302--306.

4. Guozhu Dong and James Bailey . 2013. Contrast Data Mining Concepts Algorithms and Applications ( 1 st ed.). CRC Press . Guozhu Dong and James Bailey. 2013. Contrast Data Mining Concepts Algorithms and Applications (1st ed.). CRC Press.

5. High-performance signal peptide prediction based on sequence alignment techniques

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