Oligopeptides' frequencies in the classification of proteins' primary structures
P. Sirabella; A. Giuliani; A. Colosimo · 1998 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
This paper reports about an approach to the classification of proteins' primary structures taking advantage of the Self Organizing Maps algorithm and of a numerical coding of the aminoacids based upon their physico-chemical properties. Hydrophobicity, volume, surface area, hydrophilicity, bulkiness, refractivity and polarity were subjected to a Principal Component Analysis and the first two principal components, explaining 84.8 % of the total observed variability, were used to cluster the aminoacids into 4 or 5 classes through a k-means algorithm. This leads to an economical representation of
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Metadata source: arXiv