Bio-molecular diagnosis through Random Subspace Ensembles of Learning Machines.

Traditional clinical diagnostic approaches may sometimes fail in detecting tumors (Alizadeh et al. 2001). Several results showed that bio-molecular analysis of malignancies may help to better characterize malignancies (e.g. gene expression profiling). Information for supporting both diagnosis and prognosis of malignancies at bio-molecular level may be obtained from high-throughput biotechnologies (e.g. DNA microarray). Recent work on unsupervised analysis of complex bio-molecular data (Bertoni and Valentini, 2006) showed that random projections obeying the Johnson-Lindenstrauss lemma can be used for: – Discovering structures in bio-molecular data – Validating clustering results – Improving clustering results RS ensembles can improve the accuracy of biomolecular diagnosis characterized by very high dimensional data. They could be also easily applied to heterogeneous bio-molecular and clinical data. A new promising approach consists in combining state of the art feature (gene) selection methods and RS ensembles. RS ensembles are computationally intensive but can be easily parallelized using clusters of workstations (e.g. in a MPI framework).