Name
Title | ||
|---|---|---|
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology. |
Abstract | ||
|---|---|---|
Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by a high risk of overfitting the data due the abundance of attributes (simultaneously measured gene expression values) and shortage of available examples (observations). To avoid this pitfall and achieve predictor robustness, state-of-the-art approaches construct complex classifiers that combine relatively weak contributions of up to thousands of genes (attributes) to classify a disease. The complexity of such classifiers limits their transparency and consequently the biological insights they can provide. The goal of this study is to apply to this domain the methodology of constructing simple yet robust logic-based classifiers amenable to direct expert interpretation. On two well-known, publicly available gene expression classification problems, the paper shows the feasibility of this approach, employing a recently developed subgroup discovery methodology. Some of the discovered classifiers allow for novel biological interpretations. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.jbi.2004.07.007 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
complex classifier,disease markers,gene expression measurements,gene expression datasets,disease marker,comprehensible classification,subgroup discovery,novel biological interpretation,available example,subgroup discovery methodology,direct expert interpretation,gene expression value,comprehensible model,gene expression data,biological insight,available gene expression classification,machine learning,gene expression | Data mining,Computer science,Robustness (computer science),Overfitting,Economic shortage,Disease markers | Journal |
Volume | Issue | ISSN |
37 | 4 | 1532-0464 |
Citations | PageRank | References |
27 | 1.22 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dragan Gamberger | 1 | 757 | 60.53 |
| Nada Lavrač | 2 | 989 | 72.19 |
| Filip Železný | 3 | 129 | 13.09 |
| Jakub Tolar | 4 | 89 | 5.88 |