Summary
A sensitive technique to recognize distantly related protein sequences has been implemented on a DEC massively parallel computer (MASPAR). The method relies on finding conserved subsequence regions (motifs) in multiple sequence alignments, training neural nets to recognize them, and then searching all segments in large databases of protein sequences to which the nets respond well. The net/motif search has several advantages over other present-day techniques: the sensitivity of neural nets, independence of deletions and insertions as well as gap penalties, and concentration on the most conserved subsegments for searching and scoring. Use of the parallel processors provides ready access to the comparison results as well as quick response for the molecular biological community.
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Vogt, G., Frishman, D., Argos, P. (1994). A Parallel-Processor Implementation of an Algorithm to Delineate Distantly Related Protein Sequences using Conserved Motifs and Neural Networks. In: Bock, HH., Lenski, W., Richter, M.M. (eds) Information Systems and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46808-7_37
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