Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.
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.
Gamberger
Dragan
D
Laboratory for Information Systems, Rudjer Bosković Institute, Zagreb, Croatia. dragan.gamberger@irb.hr <dragan.gamberger@irb.hr>
Lavrac
Nada
N
Zelezný
Filip
F
Tolar
Jakub
J
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D005820:Genetic Testing / Q000379:methods; D014644:Genetic Variation / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
D014408:Biomarkers, Tumor; D005819:Genetic Markers