ReviewGenes & development2024
Decoding biology with massively parallel reporter assays and machine learning.
Review in Genes & development, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- N-terminal fusion length: The key to reliable and context-preserving regulatory sequence characterization.Synthetic and systems biotechnology · 2027Article
- Decoding RNA-protein interactions using high-throughput methods.RNA biology · 2026Review
- Non-coding regulatory variants in adolescent idiopathic scoliosis risk and pathogenesis.Communications biology · 2026Review
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- An in vivo parallelized reporter assay to uncover tissue-specific splicing regulatory sequences in a multicellular animal.Nucleic acids research · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- Decoding thebioRxiv : the preprint server for biology · 2026Article
- Review
- ePerturbDB: enhancer's experimental perturbation database.Database : the journal of biological databases and curation · 2026Article
- Enhancing mRNA translation efficiency with discriminative and generative artificial intelligence by optimizing 5' UTR sequences.iScience · 2025Article
- Massively parallel assay of human splice variants reveals cis-regulatory drivers of disease-associated and cell type-specific splicing regulation.bioRxiv : the preprint server for biology · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.Cell systems · 2025Article
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications.Frontiers in molecular biosciences · 2025Review
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- The Advances in Deep Learning Modeling of Polyadenylation Codes.Wiley interdisciplinary reviews. RNAReview
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
Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of
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.