Paper List
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Formation of Artificial Neural Assemblies by Biologically Plausible Inhibition Mechanisms
This work addresses the core limitation of the Assembly Calculus model—its fixed-size, biologically implausible k-WTA selection process—by introducing...
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How to make the most of your masked language model for protein engineering
This paper addresses the critical bottleneck of efficiently sampling high-quality, diverse protein sequences from Masked Language Models (MLMs) for pr...
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Module control in youth symptom networks across COVID-19
This paper addresses the core challenge of distinguishing whether a prolonged societal stressor (COVID-19) fundamentally reorganizes the architecture ...
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JEDI: Jointly Embedded Inference of Neural Dynamics
This paper addresses the core challenge of inferring context-dependent neural dynamics from noisy, high-dimensional recordings using a single unified ...
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ATP Level and Phosphorylation Free Energy Regulate Trigger-Wave Speed and Critical Nucleus Size in Cellular Biochemical Systems
This work addresses the core challenge of quantitatively predicting how the cellular energy state (ATP level and phosphorylation free energy) governs ...
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Packaging Jupyter notebooks as installable desktop apps using LabConstrictor
This paper addresses the core pain point of ensuring Jupyter notebook reproducibility and accessibility across different computing environments, parti...
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SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
This paper addresses the core challenge of generating privacy-preserving synthetic genotype data that maintains both statistical fidelity and downstre...
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Continuous Diffusion Transformers for Designing Synthetic Regulatory Elements
This paper addresses the challenge of efficiently generating novel, cell-type-specific regulatory DNA sequences with high predicted activity while min...
Packaging Jupyter notebooks as installable desktop apps using LabConstrictor
Turku Bioscience Centre, University of Turku and Åbo Akademi University | Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa | UCL Laboratory for Molecular Cell Biology, University College London
30秒速读
IN SHORT: This paper addresses the core pain point of ensuring Jupyter notebook reproducibility and accessibility across different computing environments, particularly when sensitive data cannot leave institutional firewalls.
核心创新
- Methodology Introduces a zero-command-line workflow using GitHub Actions to automatically validate environments and package notebooks into one-click installable desktop applications for Windows, macOS, and Linux.
- Methodology Implements automated dependency specification through environment scanning and requirements generation, reducing manual configuration errors and ensuring version compatibility.
- Methodology Provides app-like user experience with code hiding by default, version tracking, and offline capability, bridging the gap between rapid development and practical deployment.
主要结论
- LabConstrictor successfully packages Jupyter notebooks into installable desktop applications with automated validation through GitHub Actions CI/CD pipelines.
- The framework supports offline execution after installation, enabling use in secure environments with institutional firewalls and low-connectivity settings.
- By reducing deployment barriers, LabConstrictor transforms quickly shared notebook methods into tools regularly used in practice, promoting routine reuse across laboratories.
摘要: Life sciences research depends heavily on open-source academic software, yet many tools remain underused due to practical barriers. These include installation requirements that hinder adoption and limited developer resources for software distribution and long-term maintenance. Jupyter notebooks are popular because they combine code, documentation, and results into a single executable document, enabling quick method development. However, notebooks are often fragile due to reproducibility issues in coding environments, and sharing them, especially for local execution, does not ensure others can run them successfully. LabConstrictor closes this deployment gap by bringing CI/CD-style automation to academic developers without needing DevOps expertise. Its GitHub-based pipeline checks environments and packages notebooks into one-click installable desktop applications. After installation, users access a unified start page with documentation, links to the packaged notebooks, and version checks. Code cells can be hidden by default, and run-cell controls combined with widgets provide an app-like experience. By simplifying the distribution, installation, and sharing of open-source software, LabConstrictor allows faster access to new computational methods and promotes routine reuse across labs.