Network biology: Large-scale biomedical data and text mining

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This document discusses three areas of network biology: association networks, signaling networks, and drug networks. For association networks, it describes the STRING database which integrates protein-protein interaction data from multiple sources. For signaling networks, it discusses using phosphoproteomics data and sequence analysis to infer kinase-substrate relationships and build networks. For drug networks, it talks about using chemical and phenotypic similarity networks to discover new drug-drug and drug-target relationships for drug repurposing.

Network biology Large-scale biomedical data and text mining Lars Juhl Jensen

Szklarczyk, Franceschini et al., Nucleic Acids Research , 2011

Korbel et al., Nature Biotechnology , 2004

Letunic & Bork, Trends in Biochemical Sciences , 2008

von Mering et al., Nucleic Acids Research , 2005

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009 O’Donoghue et al., Journal of Web Semantics , 2010

Miller, Jensen et al., Science Signaling , 2008

Linding, Jensen, Ostheimer et al., Cell , 2007

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Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the re- lational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.

Text Mining - Techniques & Limitations (A Pharmaceutical Industry Viewpoint)

Frank Oellien

Presentation given at the 6th and last meeting of the European Commission "Licenses for Europe" Text and Data Mining Working Group (WG4). The first part of the talk gives a very brief introduction of some basic concepts of text mining techniques used in Pharmaceutical industry using the Accelrys PP text mining collection. The second part of the talk focuses on existing limitations pharmaceutical companies are facing in the field of Text mining. http://ec.europa.eu/licences-for-europe-dialogue/en/content/text-and-data-mining-working-group-wg4

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