J. Comput. Biol. - Narratives in the network: interactive methods for mining cell signaling networks.

Tópicos

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Resumo

In this article, we describe our work on graph mining as applied to the cellular signaling pathways in the Signal Transduction Knowledge Environment (STKE). We present new algorithms and a graphical tool that can help biologists discover relationships between pathways by looking at structural overlaps within the database. We address the problem of determining pathway relationships by using two data mining approaches: clustering and storytelling. In the first approach, our tool brings similar pathways to the same cluster, and in the second, our tool determines intermediate overlapping pathways that can lead biologists to new hypotheses and experiments regarding relationships between the pathways. We formulate the problem of discovering pathway relationships as a subgraph discovery problem and propose a new technique called Subgraph-Extension Generation (SEG), which outperforms the traditional Frequent Subgraph Discovery (FSG) approach by magnitudes. Our tool provides an interface to compare these two approaches with a variety of similarity measures and clustering techniques as well as in terms of computational performance measures such as runtime and memory consumption.

Resumo Limpo

articl describ work graph mine appli cellular signal pathway signal transduct knowledg environ stke present new algorithm graphic tool can help biologist discov relationship pathway look structur overlap within databas address problem determin pathway relationship use two data mine approach cluster storytel first approach tool bring similar pathway cluster second tool determin intermedi overlap pathway can lead biologist new hypothes experi regard relationship pathway formul problem discov pathway relationship subgraph discoveri problem propos new techniqu call subgraphextens generat seg outperform tradit frequent subgraph discoveri fsg approach magnitud tool provid interfac compar two approach varieti similar measur cluster techniqu well term comput perform measur runtim memori consumpt

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