Functional bioinformatics for Arabidopsis thaliana

Amanda Clare, Andreas Karwath, Helen Ougham, Ross D. King

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Motivation: The genome of Arabidopsis thaliana, which has the best understood plant genome, still has approximately one-third of its genes with no functional annotation at all from either MIPS or TAIR. We have applied our Data Mining Prediction (DMP) method to the problem of predicting the functional classes of these protein sequences. This method is based on using a hybrid machine-learning/data-mining method to identify patterns in the bioinformatic data about sequences that are predictive of function. We use data about sequence, predicted secondary structure, predicted structural domain, InterPro patterns, sequence similarity profile and expressions data. Results: We predicted the functional class of a high percentage of the Arabidopsis genes with currently unknown function. These predictions are interpretable and have good test accuracies. We describe in detail seven of the rules produced. Availability: Rulesets are available at and predictions are available at
Original languageEnglish
Pages (from-to)1130-1136
Number of pages7
Issue number9
Publication statusPublished - 2006


  • bioinformatics, data mining, inductive logic programming, machine learning, relational learning, scientific knowledge


Dive into the research topics of 'Functional bioinformatics for Arabidopsis thaliana'. Together they form a unique fingerprint.

Cite this