Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks | Zendy

János Murvai | Zendy; Kristian Vlahoviček | Zendy; Csaba Szepesvári | Zendy; Sándor Pongor | Zendy

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Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks

Author(s) -

János Murvai,

Kristian Vlahoviček,

Csaba Szepesvári,

Sándor Pongor

Publication year - 2001

Publication title -

genome research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.556

H-Index - 297

eISSN - 1549-5469

pISSN - 1088-9051

DOI - 10.1101/gr.168701

Subject(s) - artificial neural network , sigmoid function , domain (mathematical analysis) , artificial intelligence , sequence (biology) , biology , pattern recognition (psychology) , function (biology) , computer science , protein domain , mathematics , genetics , gene , mathematical analysis

An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.

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