In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community. These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction. The goal of this study is to guide the focus of scalable computing experts in the endeavor of applying new storage and scalable computation designs to bioinformatics algorithms that merit their attention most, following the engineering maxim of "optimize the common case".

Digital media tend to combine text and images to express richer information, especially on image hosting and online shopping websites. This trend presents a challenge in understanding the contents from different forms of information. Features representing visual information are usually sparse in high dimensional space, which makes the learning process intractable. In order to understand text and its related visual information, we present a new graphical model-based approach to discover more meaningful information in rich media. We extend the standard Latent Dirichlet Allocation (LDA) framework to learn in high dimensional feature spaces.