Myeloid Leukemia

234 Kohlmann et al.
Fig. 5. Concept of support vector machine (SVM)-based classification. The SVM
operates by mapping the given training set into a possibly high-dimensional feature
space and attempting to locate in that space a plane that separates positive from negative
samples. The hyperplane, i.e., a plane in a space with more than three dimensions,
corresponds to a nonlinear decision boundary in the input space.
3.4.5. Classification of Samples Based on Gene Expression Patterns:
SVM-Based Classification
For classification of microarray data, the support vector machine (SVM)
algorithm can be used. SVMs are learning machines that can perform binary
classification tasks (16–18). A classification task involves training and testing
gene expression profiles, which consist of some data instances. Each instance
in the training set contains “target values” (class labels, i.e., leukemia classes)
and several “attributes” (features, i.e., genes). The goal of this approach is to
produce a model that predicts target values of data instances in the testing set
that are given only the attributes. Applied to gene expression data, an SVM
would begin with a set of genes that have a common function—e.g., genes that
demonstrate differential expression between distinct leukemia subtypes. After
nonlinearly mapping the n-dimensional input space into a high-dimensional
feature space, a linear classifier is constructed in this high-dimensional feature
space (Fig. 5).
Using this training set, a SVM would learn to discriminate between the types
and subtypes of leukemias based on expression data. Having found such a
plane, the SVM can then predict the classification of an unlabeled new sample
by mapping it into the feature space and asking on which side of the separating
plane the example lies (Fig. 6). Then a label is assigned according to its relationship
with the decision boundary (10,19). Multi-class SVM classifiers can
be built with linear kernels using the library LIBSVM version 2.36
(www.csie.ntu.edu.tw/~cjlin/libsvm/) (20).