Name
Abstract | ||
|---|---|---|
The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analyse data from mutant phenotype growth experiments with the yeast S. cerevisiae to predict novel gene functions. The analysis of the data presented a number of challenges: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We developed resampling strategies and modified the algorithm C4.5 to deal with these problems. Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 putative genes of currently unknown function at an estimated accuracy of ≥ 80%. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44794-6_4 | PKDD |
Keywords | Field | DocType |
complete classification,available data,knowledge discovery,novel gene function,accurate rule,algorithm c4,large number,large amount,multi-label phenotype data,biological science,unknown function,new data analysis method,data analysis methods,missing values | Classifier chains,Data mining,Phenotype,Data analysis,Computer science,Multi-label classification,Knowledge extraction,Artificial intelligence,Missing data,Resampling,Decision tree learning,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-42534-9 | 268 | 14.15 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amanda Clare | 1 | 592 | 47.37 |
| Ross D. King | 2 | 1774 | 194.85 |