Computer Science Technical Reports
CS at VT

Compositional Mining of Multi-Relational Biological Datasets

Jin, Ying and Murali, T.M. and Ramakrishnan, Naren (2007) Compositional Mining of Multi-Relational Biological Datasets. Technical Report TR-07-29, Computer Science, Virginia Tech.

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High-throughput biological screens are yielding ever-growing streams of information about multiple aspects of cellular activity. As more and more categories of datasets come online, there is a corresponding multitude of ways in which inferences can be chained across them, motivating the need for compositional data mining algorithms. In this paper, we argue that such compositional data mining can be effectively realized by functionally cascading redescription mining and biclustering algorithms as primitives. Both these primitives mirror shifts of vocabulary that can be composed in arbitrary ways to create rich chains of inferences. Given a relational database and its schema, we show how the schema can be automatically compiled into a compositional data mining program, and how different domains in the schema can be related through logical sequences of biclustering and redescription invocations. This feature allows us to rapidly prototype new data mining applications, yielding greater understanding of scientific datasets. We describe two applications of compositional data mining: (i) matching terms across categories of the Gene Ontology and (ii) understanding the molecular mechanisms underlying stress response in human cells.

Item Type:Departmental Technical Report
Keywords:compositional data mining, biclustering, redescription mining, bioinformatics, inductive logic programming
Subjects:Computer Science > Information Retrieval
Computer Science > Bioinformatics
ID Code:988
Deposited By:jin, ying
Deposited On:22 August 2007