Version 1
: Received: 18 April 2024 / Approved: 19 April 2024 / Online: 19 April 2024 (12:03:32 CEST)
How to cite:
Wang, J. TF-Target Finder: An R Web Application Bridging Multiple Predictive Models for Decoding Transcription Factor-Target Interactions. Preprints2024, 2024041312. https://doi.org/10.20944/preprints202404.1312.v1
Wang, J. TF-Target Finder: An R Web Application Bridging Multiple Predictive Models for Decoding Transcription Factor-Target Interactions. Preprints 2024, 2024041312. https://doi.org/10.20944/preprints202404.1312.v1
Wang, J. TF-Target Finder: An R Web Application Bridging Multiple Predictive Models for Decoding Transcription Factor-Target Interactions. Preprints2024, 2024041312. https://doi.org/10.20944/preprints202404.1312.v1
APA Style
Wang, J. (2024). TF-Target Finder: An R Web Application Bridging Multiple Predictive Models for Decoding Transcription Factor-Target Interactions. Preprints. https://doi.org/10.20944/preprints202404.1312.v1
Chicago/Turabian Style
Wang, J. 2024 "TF-Target Finder: An R Web Application Bridging Multiple Predictive Models for Decoding Transcription Factor-Target Interactions" Preprints. https://doi.org/10.20944/preprints202404.1312.v1
Abstract
Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application, utilizing the R programming language, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform for exploring gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
