Biomedical relation extraction using stochastic difference equations

Fakhry, Carl Tony; Zarringhalam, Kourosh; Chen, Ping

doi:10.1109/hpec.2015.7322474

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Several approaches for reconstruction of regulatory networks form gene expression data have been proposed by the scientific community (11,12). Public sources of gene regulatory interactions are either computationally derived, based on biological experiments, or manually curated from biomedical literature (13,14,15). These sources include the Transcriptional Regulatory Element Database (TRED) (16), the Transcription Regulatory Regions Database (TRRD) (17), and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text Mining (TRRUST) (18).…”

Section: Introductionmentioning

confidence: 99%

Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Farahmand

O’Connor

Macoska

et al. 2019

Preprint

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Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manuallycurated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-Seq experiments with gene-expression data from tissue-specific RNA-Seq experiments. Our GGM approach identifies high confidence TF-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually-curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

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Section: Introductionmentioning

confidence: 99%

Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Farahmand

O’Connor

Macoska

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Farahmand

O’Connor

Macoska

et al. 2019

Nucleic Acids Research

View full text Add to dashboard Cite

Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator–gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)–gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF–gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

show abstract

Biomedical relation extraction using stochastic difference equations

Cited by 2 publications

References 10 publications

Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

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