ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction
| Autor: | Kourosh Zarringhalam, Saman Farahmand, Todd Riley |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
PubMed
Computer science Gene regulatory network Repressor Health Informatics Computational biology computer.software_genre 03 medical and health sciences 0302 clinical medicine Transcription (biology) Gene expression Data Mining Humans Gene Regulatory Networks 030212 general & internal medicine Biomarker discovery Gene Transcription factor 030304 developmental biology Regulation of gene expression 0303 health sciences Activator (genetics) Biomedical text mining Computer Science Applications Information extraction Gene Expression Regulation Transcription Factor Gene computer Software Transcription Factors |
| DOI: | 10.1101/672725 |
| Popis: | BackgroundTranscription 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] |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|