ReviewNature methods2021
Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.
Review in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
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Who cites it
36 citing papers in PubMed.
- Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Physics-guided design of intrinsically disordered proteins.bioRxiv : the preprint server for biology · 2026Article
- Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.Infectious Disease Modelling · 2026Article
- IFNIKB: a type I interferon database for antitumuor immunity studies.Database : the journal of biological databases and curation · 2026Article
- Near-infrared hyperspectral imaging combined with elemental and isotopic analyses reflects geographical origins of cultured salmonids.Current research in food science · 2026Article
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.Bioinformatics (Oxford, England) · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
- Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.Biophysical journal · 2025Article
- Article
- Universal differential equations as a unifying modeling language for neuroscience.Frontiers in computational neuroscience · 2025Review
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- Overview of Molecular Modeling in Drug Discovery with a Special Emphasis on the Applications of Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Article
- Insights gained from computational modeling of YAP/TAZ signaling for cellular mechanotransduction.NPJ systems biology and applications · 2024Review
- A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators.Scientific reports · 2024Article
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation.NPJ systems biology and applications · 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
2 authors.
Funding
Abstract
Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, machine-learnable components trained on experimental data. Such programs are having a growing impact on molecular and cellular biology. In this Perspective, we describe an emerging 'differentiable biology' in which phenomena ranging from the small and specific (for example, one experimental assay) to the broad and complex (for example, protein folding) can be modeled effectively and efficiently, often by exploiting knowledge about basic natural phenomena to overcome the limitations of sparse, incomplete and noisy data. By distilling differentiable biology into a small set of conceptual primitives and illustrative vignettes, we show how it can help to address long-standing challenges in integrating multimodal data from diverse experiments across biological scales. This promises to benefit fields as diverse as biophysics and functional genomics.
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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.