scholarly journals ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Todd Riley ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Interactions ◽  
Mining System ◽  
Link Type ◽  
Gene Regulatory ◽  
Rest Api ◽  
Tf Gene ◽  
Text Mining System ◽  
Mode Of Regulation

ABSTRACTBackgroundTranscription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine.MethodsIn this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases.ResultsOur results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage.AvailibilitySource code and datasets are available for download on GitHub: https://github.com/samanfrm/modex HTTP REST API: https://watson.math.umb.edu/modex/[type query]

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

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

Abstract 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.

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

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A. Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

ABSTRACTInference 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 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.

CREATING KNOWLEDGE REPOSITORIES FROM BIOMEDICAL REPORTS: THE MEDSYNDIKATE TEXT MINING SYSTEM

2001 ◽  
Author(s):  
UDO HAHN ◽  
MARTIN ROMACKER ◽  
STEFAN SCHULZ
Keyword(s):  
Text Mining ◽  
Mining System ◽  
Knowledge Repositories ◽  
Text Mining System

scholarly journals Construction of biological networks for Korean medical herb using the text-mining system

2015 ◽  
Vol 4 (1) ◽  
pp. 140
Author(s):  
Seok Jong Yu ◽  
Chul Kim ◽  
Yongseong Cho ◽  
Junehawk Lee ◽  
Hyojin Kang
Keyword(s):  
Text Mining ◽  
Biological Networks ◽  
Mining System ◽  
Text Mining System

scholarly journals Text-mining System Simpleminer: Use of IE Table and Sort Graph

2008 ◽  
Vol 28-1 (1) ◽  
pp. 365-365
Author(s):  
Masaki MURATA
Keyword(s):  
Text Mining ◽  
Mining System ◽  
Text Mining System

scholarly journals Extracting Drug Names and Associated Attributes from Discharge Summaries: DrugEx, an End-to-End Text Mining System (Preprint)

10.2196/24678 ◽  
2020 ◽  
Author(s):  
Ghada Alfattni ◽  
Maksim Belousov ◽  
Niels Peek ◽  
Goran Nenadic
Keyword(s):  
Text Mining ◽  
Mining System ◽  
Drug Names ◽  
End To End ◽  
Text Mining System ◽  
Discharge Summaries

scholarly journals CLUO: Web-Scale Text Mining System for Open Source Intelligence Purposes

2013 ◽  
Vol 14 (1) ◽  
pp. 45 ◽  
Author(s):  
Przemyslaw Maciolek ◽  
Grzegorz Dobrowolski
Keyword(s):  
Text Mining ◽  
Open Source ◽  
Mining System ◽  
Open Source Intelligence ◽  
Text Mining System
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