Abstract - Confirmation of Data Mining Based Predictions of Protein Function
Ross D. King, Paul H. Wise and Amanda Clare
Bioinformatics, to appear (2004).
Motivation:
A central problem in bioinformatics is the assignment of function to
sequenced open reading frames (ORFs). The most common approach is
based on inferred homology using a statistically based sequence
similarity (SIM) method e.g. PSI-BLAST. Alternative non-SIM based
bioinformatic methods are becoming popular. One such method is Data
Mining Prediction (DMP). This is based on combining evidence from
amino-acid attributes, predicted structure, and phylogenic patterns;
and uses a combination of Inductive Logic Programming data mining, and
decision trees to produce prediction rules for functional class. DMP
predictions are more general than is possible using homology. In
2000/1 DMP was used to make public predictions of the function of 1309
E. coli ORFs. Since then biological knowledge has advanced
allowing us to test our predictions.
Results:
We examined the updated (20.02.02) Riley group genome annotation, and
examined the scientific literature for direct experimental derivations
of ORF function. Both tests confirmed the DMP predictions. Accuracy
varied between rules, and with the detail of prediction, but they were
generally significantly better than random. For voting rules,
accuracies of 75-100% were obtained. Twenty one of these DMP
predictions have been confirmed by direct experimentation. The DMP
rules also have interesting biological explanations. DMP is, to the
best of our knowledge, the first non-SIM based prediction method to
have been tested directly on new data.
Availability:
We have designed the ``Genepredictions'' database
for protein functional predictions. This is intended to act as an
open repository for predictions for any organism and can be accessed
at http://www.genepredictions.org.
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