Name
Abstract | ||
|---|---|---|
We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.2390/biecoll-jib-2006-31 | JOURNAL OF INTEGRATIVE BIOINFORMATICS |
Keywords | Field | DocType |
integrable system,machine learning,upstream open reading frame,gene expression,random sampling | Inductive logic programming,Computer science,Theoretical computer science,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
3 | 2 | 1613-4516 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Selpi | 1 | 1 | 0.71 |
| Christopher H. Bryant | 2 | 117 | 9.28 |
| Graham J L Kemp | 3 | 238 | 49.92 |
| Marija Cvijović | 4 | 34 | 2.35 |