Clustering in Relational Biological Data

The scientific endeavor of biology is becoming increasingly reliant on data in electronic form, and it is therefore necessary for biologists to manage and understand large quantities of data. Publicly available data including biological se- quences, biological structures, and literature in the life sciences have grown to such an extent that computing is essential simply to store and access it. Here we describe a clustering ap- proach by exploiting the relational structure of biological data to help with the next step: to enhance understanding of the data by combin- ing techniques from information retrieval with those from bioinformatics. By computing over a network of sequence-structure-literature rela- tionships it is possible to infer clusters of related articles, sequences and structures. This paper de- scribes the general framework and its application to several biological domains.