Pertinent background knowledge for learning protein grammars
Bryant, CH, Fredouille, DC, Wilson, A, Jayawickreme, CK, Jupe, S and Topp, S 2006, 'Pertinent background knowledge for learning protein grammars' , in: Machine learning: ECML 2006 , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (4212) , Springer, Berlin / Heidelberg, Germany, pp. 54-65.
Download (266kB) | Preview
We are interested in using Inductive Logic Programming(ILP) to infer grammars representing sets of protein sequences. ILP takes as input both examples and background knowledge predicates. This work is a first step in optimising the choice of background knowledge predicates for predicting the function of proteins. We propose methods to obtain different sets of background knowledge. We then study the impact of these sets on inference results through a hard protein function inference task: the prediction of the coupling preference of GPCR proteins. All but one of the proposed sets of background knowledge are statistically shown to have positive impacts on the predictive power of inferred rules, either directly or through interactions with other sets. In addition, this work provides further confirmation, after the work of Muggleton et al. (2001) that ILP can help to predict protein functions.
|Item Type:||Book Section|
|Additional Information:||Paper originally presented at the 17th European Conference on Machine Learning in Berlin, Germany, September 18-22 2006|
|Editors:||Fürnkranz, J, Scheffer, T and Spiliopoulou, M|
|Themes:||Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science|
Subjects / Themes > Q Science > QH Natural history > QH301 Biology
Subjects outside of the University Themes
|Schools:||Colleges and Schools > College of Science & Technology|
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
|Depositing User:||Dr Chris H. Bryant|
|Date Deposited:||16 Feb 2009 11:55|
|Last Modified:||27 Sep 2011 12:20|
Document DownloadsMore statistics for this item...
Actions (login required)
|Edit record (repository staff only)|