A novel method based on new adaptive LVQ neural network for predicting protein–protein interactions from protein sequences

Protein–Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper,...

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Vydáno v:	Journal of theoretical biology Ročník 336; s. 231 - 239
Hlavní autoři:	Yousef, Abdulaziz, Moghadam Charkari, Nasrollah
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 07.11.2013
Témata:	Algorithms Amino Acid Sequence amino acid sequences amino acids Classifier combination data collection Databases, Protein Feature extraction Helicobacter pylori Helicobacter pylori - metabolism Humans learning Learning vector quantization neural network neural networks Neural Networks (Computer) new methods physicochemical properties prediction Principal Component Analysis Principal components analysis Protein Interaction Mapping - methods protein-protein interactions proteins Protein–protein interaction prediction Saccharomyces cerevisiae - metabolism Feature extraction Principal components analysis Protein–protein interaction prediction Learning vector quantization neural network Classifier combination
ISSN:	0022-5193, 1095-8541, 1095-8541
On-line přístup:	Získat plný text
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