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  DISI -> Research -> Areas -> Artificial Intelligence -> Research topic

Research topic: Machine learning in bioinformatics


Machine learning, Bioinformatics, Functional genomics, Ensemble methods, Classification, Support vector machines, Multi-layer perceptrons, Error-correcting output coding, DNA Microarray data analysis


Bioinformatics is currently a very active research area, mainly because of the recent advancements in genomics and related technologies. "Smart" methods for automating or assisting analysis and understanding are required by the size and complexity of these data. Machine learning techniques (such as unsupervised analysis via clustering and self-organizing maps, subspace methods, classification with support vector machines, with neural networks, with decision trees, and so on) provide valuable tools for these tasks.

In particular, we have focused on the analysis of DNA microarray data also through a partnership with the Advanced Biotechnology Center of the University of Genova, which provides a facility for the production of DNA microarrays. We have studied the application of several types of classifiers to disease discrimination, class discovery, differential diagnosis. One research contribution is in the field of ensemble machines, a technique to combine several classifiers into a composite one. This allows a pool of (possibly heterogeneous) two-class classifiers to tackle multi-class classification problems.

We are also studying a technique to automate and optimize the design of oligonucleotide microarrays to be applied to the typing of the human HLA system, a central immunologic problem in transplantology currently tackled with other, more expensive approaches.


For more informations on this topic:
The Machine Learning and Soft Computing web page