ReviewNature methods2023
Julia for biologists.
Review in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 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
25 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Cell-specific Cahn-Hilliard models predict condensed fates of the chromosomal passenger complex.PLoS computational biology · 2026Article
- Article
- GeneDrive.jl: A decision tool to optimize biological vector control strategies under climate change.PLoS computational biology · 2025Article
- PEtab.jl: advancing the efficiency and utility of dynamic modelling.Bioinformatics (Oxford, England) · 2025Article
- Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune parameters in metastatic breast cancer.bioRxiv : the preprint server for biology · 2025Article
- Cahn-Hilliard dynamical models for condensed biomolecular systems.bioRxiv : the preprint server for biology · 2025Article
- Translating microbial kinetics into quantitative responses and testable hypotheses using Kinbiont.Nature communications · 2025Article
- Reversible molecular simulation for training classical and machine-learning force fields.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.Small methods · 2025Article
- Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence.Journal of medical radiation sciences · 2025Article
- Translating short-form Python exercises to other programming languages using diverse prompting strategies.GigaScience · 2025Article
- MetaRange.jl: A Dynamic and Metabolic Species Range Model for Plant Species.Ecology and evolution · 2025Article
- ComplexityMeasures.jl: Scalable software to unify and accelerate entropy and complexity timeseries analysis.PloS one · 2025Article
- Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life.Nature communications · 2024Article
- Generating synthetic signaling networks for in silico modeling studies.Journal of theoretical biology · 2024Article
- A next-generation dynamic programming language Julia: Its features and applications in biological science.Journal of advanced research · 2024Review
- RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.BMC bioinformatics · 2024Article
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages-Julia-is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia's design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.
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.