Support vector machines for novel class detection in Bioinformatics.
Novelty detection techniques might be a promising way of dealing with high-dimensional classification problems in Bioinformatics. We present preliminary results of the use of a one-class support vector machine approach to detect novel classes in two Bioinformatics databases. The results are compatible with theory and inspire further investigation.
Spinosa
Eduardo J
EJ
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brazil. ejspin@icmc.usp.br
Carvalho
André C P L F de
AC
D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000295:instrumentation; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D005822:Genetic Vectors; D006801:Humans; D007938:Leukemia / Q000235:genetics*; D008223:Lymphoma / Q000235:genetics*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results