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Generate, test, and explain: synthesizing regularity exposing attributes in large protein databases

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1 Author(s)
de la Maza, M. ; Artificial Intelligence Lab., MIT, Cambridge, MA, USA

Describes a database mining system that synthesizes regularity-exposing attributes in large protein databases. After processing the primary and secondary structure data, this system discovers an amino acid representation that captures what are thought to be the three most important amino acid characteristics (size, charge, and hydrophobicity) for tertiary structure prediction. A neural network trained using this 16-bit representation achieves a performance accuracy on the secondary structure prediction problem that is comparable to the one achieved by a neural network trained using the standard 24-bit amino acid representation.<>

Published in:

System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on  (Volume:5 )

Date of Conference:

4-7 Jan. 1994