Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types
- 1 August 2005
- journal article
- research article
- Published by Elsevier in Biochemical and Biophysical Research Communications
- Vol. 334 (1) , 288-292
- https://doi.org/10.1016/j.bbrc.2005.06.087
Abstract
No abstract availableKeywords
This publication has 42 references indexed in Scilit:
- Using GO-PseAA predictor to identify membrane proteins and their typesBiochemical and Biophysical Research Communications, 2005
- Using cellular automata to generate image representation for biological sequencesAmino Acids, 2005
- SLLE for predicting membrane protein typesJournal of Theoretical Biology, 2005
- Using complexity measure factor to predict protein subcellular locationAmino Acids, 2004
- Application of SVM to predict membrane protein typesJournal of Theoretical Biology, 2004
- Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular LocationJournal of Biological Chemistry, 2002
- Prediction of protein cellular attributes using pseudo‐amino acid compositionProteins-Structure Function and Bioinformatics, 2001
- An evidence-theoretic k-NN rule with parameter optimizationIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1998
- Relation between amino acid composition and cellular location of proteinsJournal of Molecular Biology, 1997
- A Joint Prediction of the Folding Types of 1490 Human Proteins from their Genetic CodonsJournal of Theoretical Biology, 1993