Automatic discovery of cross-family sequence features associated with protein function.

ABSTRACT:

Background

Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterized protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed.

Results

We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are re

SUBMITTER: Brameier M 

PROVIDER: S-EPMC1395344 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature