Abstract: 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 sequences, 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 approach by exploiting the relational structure of biological data to help with the next step: to enhance understanding of the data by combining techniques from information retrieval with those from bioinformatics. By computing over a network of sequence-structure-literature relationships it is possible to infer clusters of related articles, sequences and structures. This paper describes the general framework and its application to several biological domains.
Publication Year: 2004
Publication Date: 2004-01-01
Language: en
Type: article
Access and Citation
Cited By Count: 6
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