ReviewMolecular systems biology2020
Enhancing scientific discoveries in molecular biology with deep generative models.
Review in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models.bioRxiv : the preprint server for biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- A Deep Learning Approach to Assessing Cell Identity in Stem Cell-Based Embryo Models.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Article
- scRegulate: single-cell regulatory-embedded variational inference of transcription factor activity from gene expression.Bioinformatics (Oxford, England) · 2025Article
- In silico biological discovery with large perturbation models.Nature computational science · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- GENNUS: generative approaches for nucleotide sequences enhance mirtron classification.NAR genomics and bioinformatics · 2025Article
- Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- multiDGD: A versatile deep generative model for multi-omics data.Nature communications · 2024Article
- VI-VS: calibrated identification of feature dependencies in single-cell multiomics.Genome biology · 2024Article
- Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.Nature biotechnology · 2024Article
- scDiffusion: conditional generation of high-quality single-cell data using diffusion model.Bioinformatics (Oxford, England) · 2024Article
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
Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out-of-sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.
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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.