Learning in Biomedicine and Bioinformatics Using Affinity Propagation

Data sets arising in biomedicine and bioinformatics are often huge and consist of quite different types of data (eg, sequence data and microarray measurements). Consequently, standard machine learning techniques usually cannot be directly applied. In this talk, I will describe an algorithm called affinity propagation and discuss why it offers flexibility in analyzing the kinds of data sets arising in bioinformatics and biomedicine. I'll describe applications in the areas of whole-genome transcript detection using microarrays, image segmentation, text analysis and motif discovery. Affinity propagation can implemented in a couple dozen lines of MATLAB or C and is suitable for distributed computing environments, making it attractive for high-throughput computations.