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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

Fully interpretable deep learning model of transcriptional control.

Yi Liu¹, Kenneth Barr², John Reinitz³

¹Department of Statistics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA.

Bioinformatics (Oxford, England)

|July 14, 2020

Summary

We developed an interpretable Deep Neural Network (DNN) that models transcriptional control by integrating biological chemistry with machine learning. This approach precisely predicts gene expression, advancing systems biology and functional genomics.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Area of Science:

Systems Biology
Genomics
Molecular Genetics

Background:

Deep Neural Networks (DNNs) are increasingly used in systems biology for functional genomics and molecular genetics.
Current DNN applications often use a 'black box' approach, neglecting the biological system's internal structure.
DNNs have not been extensively applied to detailed transcriptional control modeling due to differing equation formalisms.

Purpose of the Study:

To present a novel DNN model for detailed transcriptional control.
To demonstrate an interpretable DNN structure faithful to the chemistry of transcription factor binding.
To show how systems biology models can be derived from DNN structures.

Main Methods:

Derived a DNN from a systems biology model of transcriptional regulation.
Ensured the DNN's internal structure reflects the chemistry of transcription factor-DNA binding.
Applied the DNN to model transcriptional control in the early Drosophila melanogaster embryo.

Main Results:

Developed a precise and predictive DNN for modeling detailed transcription control.
The DNN's internal structure is fully interpretable and biologically faithful.
The model provides a framework for analyzing large-scale genomic data.

Conclusions:

This work bridges the gap between machine learning and detailed biological modeling.
The interpretable DNN advances the application of AI in systems biology.
The developed model serves as a foundation for future genomic-scale analyses.