Neural Networks for Molecular Sequence Classification

  • Cathy H. Wu


Nucleic acid and protein sequences contain a wealth of information of interest to molecular biologists, since the genome forms the blueprint of the cell. Currently, a database search for sequence similarities represents the most direct computational approach to decipher the codes connecting molecular sequences with protein structure and function (Doolittle, 1990). If the unknown protein is related to one of known structure/function, inferences based on the known structure/function and the degree of the relationship can provide the most reliable clues to the nature of the unknown protein. This technique has proved successful and has led to new understanding in a wide variety of biological studies (Boswell and Lesk, 1988).


Neural Network Input Vector Training Pattern Sequence Entry Classification Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Birkhäuser Boston 1994

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  • Cathy H. Wu

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