On Data and Visualization Models for Signaling Pathways

Ratprasartporn, Nattakarn; Çakmak, Ali; Özsoyoǧlu, Gültekin

doi:10.1109/ssdbm.2006.36

Cited by 1 publication

(1 citation statement)

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“…PathCase (Ratprasartporn et al, 2006), for example, provides a way to visualize signaling pathways as nested graphs and employs four abstraction levels to counter the visual complexity of signaling pathways. Xian and Yanbin (2007) design a model for the WNT signaling pathway, a gene regulation route of living cells of various organisms.…”

Section: Literature Reviewmentioning

confidence: 99%

Narratives in the Network: Interactive Methods for Mining Cell Signaling Networks

Hossain

Akbar

Polys

2012

Journal of Computational Biology

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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.

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Section: Literature Reviewmentioning

confidence: 99%