A comparison study on protein fold recognition

Author

Bologna, Guido ; Appel, Ron D.

Author_Institution

Swiss Inst. of Bioinformatics, Geneva, Switzerland

Volume

5

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

2492

Abstract

Although two proteins may be structurally similar, they may not have significant sequence similarity. The recognition of protein fold structures without relying on sequence similarity is a complex task. This work presents a comparison study on the recognition of 3-dimensional protein folds by Machine Learning models. Combinations of neural networks were trained by bagging and arcing with two datasets available online (http://www.nersc.gov/). Our results improved the average predictive accuracy obtained by Support Vector Machines in previously published work.

Keywords

biology computing; learning (artificial intelligence); molecular biophysics; molecular configurations; multilayer perceptrons; pattern classification; proteins; 3D protein folds; arcing; average predictive accuracy; bagging; discretized interpretable multilayer perceptrons; fold structure recognition; machine learning models; neural networks; protein fold recognition; training algorithm; Accuracy; Bagging; Bioinformatics; Machine learning; Neural networks; Neurons; Predictive models; Proteins; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1201943

Filename

1201943