Figure 2From: SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cellsClassification performance of different types of classifiers. The performance of the best SVM in each category is compared to three other standard machine learning methods: LDA (Linear Discriminant Analysis), Decision Tree, and ANN (Artificial Neural Networks) and a simple fold-change-based predictor. Performance of machine learning methods is evaluated and accuracy is measured using LOOCV. Labelling of panels is as follows, "microarray": using genome-wide mRNA microarray profiling data; "chip": using genome-wide ChIP-seq of transcription factors data; "micro-chip": using both microarray and ChIP-seq. The fold-change-based predictor results are only under the "microarray" panel since it uses only microarray data.Back to article page