A novel approach to detect hot-spots in large-scale multivariate data

Wu, Jianhua; Kendrick, Keith M.; Feng, Jianfeng

doi:10.1186/1471-2105-8-331

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…For example, in our experimental data recorded from the inferotemporal (IT) cortex of sheep, every measured neuron receives common exogenous inputs from the visual cortex and feedbacks from the prefrontal cortex [ 7 , 15 ]. Even with advanced multielectrode array (MEA) techniques, it is only able to record a tiny subset of interacting neurons in a single area [ 15 , 17 ] and there are bound to be endogenous variables. Hence controlling environmental inputs is a critical issue when applying Granger causality to experimental data.…”

Section: Introductionmentioning

confidence: 99%

Impact of environmental inputs on reverse-engineering approach to network structures

Sinfield

Buchanan‐Wollaston

et al. 2009

BMC Syst Biol

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BackgroundUncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs.ResultsWith considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism.ConclusionWe demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

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Section: Introductionmentioning

confidence: 99%

Impact of environmental inputs on reverse-engineering approach to network structures

Sinfield

Buchanan‐Wollaston

et al. 2009

BMC Syst Biol

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Integrating computational and mixture-based screening of combinatorial libraries

et al. 2010

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Mixture-based synthetic combinatorial library (MB-SCL) screening is a well-established experimental approach for rapidly retrieving structure-activity relationships (SAR) and identifying hits. Virtual screening is also a powerful approach that is increasingly being used in drug discovery programs and has a growing number of successful applications. However, limited efforts have been made to integrate both techniques. To this end, we combined experimental data from a MB-SCL of bicyclic guanidines screened against the κ-opioid receptor and molecular similarity methods. The activity data and similarity analyses were integrated in a biometric analysis-similarity map. Such a map allows the molecules to be categorized as actives, activity cliffs, low similarity to the reference compounds, or missed hits. A compound with IC(50) = 309 nM was found in the "missed hits" region, showing that active compounds can be retrieved from a MS-SCL via computational approaches. The strategy presented in this work is general and is envisioned as a general-purpose approach that can be applied to other MB-SCLs.

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A computationally efficient method for delineating irregularly shaped spatial clusters

et al. 2010

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This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the ''Housing Submarket Identifier'' (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts.

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A novel approach to detect hot-spots in large-scale multivariate data

Cited by 5 publications

References 17 publications

Impact of environmental inputs on reverse-engineering approach to network structures

Impact of environmental inputs on reverse-engineering approach to network structures

Integrating computational and mixture-based screening of combinatorial libraries

A computationally efficient method for delineating irregularly shaped spatial clusters

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