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Please use this
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http://hdl.handle.net/1721.1/30388
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| Title: |
Machine Learning Approaches to Modeling
the Physiochemical Properties of Small Peptides |
| Authors: |
Jensen, Kyle
Styczynski,
Mark
Stephanopoulos, Gregory |
| Keywords: |
Machine
learning
peptides
modeling
physio-chemical properties |
| Issue Date: |
Jan-2006 |
| Series/Report no.: |
Molecular Engineering of Biological and
Chemical Systems (MEBCS) |
| Abstract: |
Peptide and protein sequences are most
commonly represented as a strings: a series of letters selected from
the twenty character alphabet of abbreviations for the naturally
occurring amino acids. Here, we experiment with representations of
small peptide sequences that incorporate more physiochemical
information. Specifically, we develop three different physiochemical
representations for a set of roughly 700 HIV–I protease substrates.
These different representations are used as input to an array of six
different machine learning models which are used to predict whether
or not a given peptide is likely to be an acceptable substrate for
the protease. Our results show that, in general, higher–dimensional
physiochemical representations tend to have better performance than
representations incorporating fewer dimensions selected on the basis
of high information content. We contend that such representations
are more biologically relevant than simple string–based
representations and are likely to more accurately capture peptide
characteristics that are functionally important. |
| URI: |
http://hdl.handle.net/1721.1/30388 |
| Appears in Collections: |
Molecular
Engineering of Biological and Chemical Systems
(MEBCS)
|
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