ArticleBioinformatics (Oxford, England)2020
Fully interpretable deep learning model of transcriptional control.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.Bioinformatics (Oxford, England) · 2025Article
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA.Biochimica et biophysica acta. Gene regulatory mechanisms · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.Nature communications · 2025Article
- CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data.Briefings in bioinformatics · 2023Article
- An intrinsically interpretable neural network architecture for sequence-to-function learning.Bioinformatics (Oxford, England) · 2023Article
- An intrinsically interpretable neural network architecture for sequence to function learning.bioRxiv : the preprint server for biology · 2023Article
- Optimization of Transcription Factor Genetic Circuits.Biology · 2022Article
- Artificial neural networks enable genome-scale simulations of intracellular signaling.Nature communications · 2022Article
- Application of Domain- and Genotype-Specific Models to Infer Post-Transcriptional Regulation of Segmentation Gene Expression inLife (Basel, Switzerland) · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Multiscale plant modeling: from genome to phenome and beyond.Emerging topics in life sciences · 2021Review
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.Genome biology · 2020Article
- Binary Expression Enhances Reliability of Messaging in Gene Networks.Entropy (Basel, Switzerland) · 2020Article
- Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.Nucleic acids research · 2020Article
- Deep learning for inferring transcription factor binding sites.Current opinion in systems biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
motivationThe universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a 'black box' approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks.
resultsIn this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. . AVAILABILITY AND IMPLEMENTATION: The implementation and data for the models used in this paper are in a zip file in the supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.