Redescription Mining and Applications in Bioinformatics

Our ability to interrogate the cell and computationally assimilate its answers is improving at a dramatic pace. For instance, the study of even a focused aspect of cellular activity, such as gene action, now benefits from multiple high-throughput data acquisition technologies such as microarrays, genome-wide deletion screens, and RNAi assays. A critical need is the development of algorithms that can bridge, relate, and unify diverse categories of data descriptors. Redescription mining is such an approach. Given a set of biological objects (e.g., genes, proteins) and a collection of descriptors defined over this set, the goal of redescription mining is to use the given descriptors as a vocabulary and find subsets of data that afford multiple definitions. The premise of redescription mining is that subsets that afford multiple definitions are likely to exhibit concerted behavior and are, hence, interesting. We present algorithms for redescription mining based on formal concept analysis and applications of redescription mining to multiple biological datasets. We demonstrate how redescriptions identify conceptual clusters of data using mutually reinforcing features, without explicit training information.

Semantic Annotation of Mutable Data

Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in each case, an annotation needs to clearly identify its scope and its own terminology. However, electronic annotation of data differs from annotation of documents: the content of the annotations, including expectations and supporting evidence, is more often shared among members of networks. Any consequent actions taken by the holders of the annotated data could be shared as well. But even those current annotation systems that admit data as their subject often make it difficult or impossible to annotate at fine-enough granularity to use the results in this way for data quality control. We address these kinds of issues by offering simple extensions to an existing annotation ontology and describe how the results support an interest-based distribution of annotations. We are using the result to design and deploy a platform that supports annotation services overlaid on networks of distributed data, with particular application to data quality control. Our initial instance supports a set of natural science collection metadata services. An important application is the support for data quality control and provision of missing data. A previous proof of concept demonstrated such use based on data annotations modeled with XML-Schema