@sayalaruano @Henrywebel 🧰 Code is available #opensource: .Documentation can be found here: 

@sayalaruano @Henrywebel VueGen is a cross-platform tool for automated scientific report generation. It supports multiple formats (PDF, HTML, PPTX, Streamlit apps, and more), integrates outputs from diverse bioinformatics tools, and enables customization via a unified configuration system.

📄 Explore the latest from Bioinformatics Advances: "VueGen: Automating the generation of scientific reports" . Full article available: . Authors include: @sayalaruano, @Henrywebel

@DelaplaceFranck 🖥️ BooN is #opensource and available on GitHub:

@DelaplaceFranck BooN is a #Python-based software platform for #Boolean network analysis. It supports network design, symbolic computation of stable states, and controllability analysis. Benchmarking shows strong performance on networks with up to 50 nodes and varying formula complexity.

🧠 Recently published in Bioinformatics Advances: "BooN: Boolean network analysis software" by @DelaplaceFranck. Explore the full study:

🛠️ Open-source code and materials available:

This study benchmarks multiple #LLMs—including GPT-4, Mixtral-8x7B, and BioMistral—for #phenotype-driven gene prioritization in rare disease cases. A divide-and-conquer approach improved accuracy and reduced bias in ranking candidate genes across datasets from BG, UDN, and DDD.

🧬 Now published in Bioinformatics Advances: “Survey and improvement strategies for gene prioritization with large language models” . Explore the full study:

@RuixiangT @DongxueMao @LiuPF @ZhandongLiu @huxia 🛠️ Open-source code and materials available:

@RuixiangT @DongxueMao @LiuPF @ZhandongLiu @huxia This study benchmarks multiple #LLMs—including GPT-4, Mixtral-8x7B, and BioMistral—for #phenotype-driven gene prioritization in rare disease cases. A divide-and-conquer approach improved accuracy and reduced bias in ranking candidate genes across datasets from BG, UDN, and DDD.

🧬 Now published in Bioinformatics Advances: “Survey and improvement strategies for gene prioritization with large language models” . Explore the full study: Authors include: @RuixiangT, @DongxueMao, @LiuPF, @ZhandongLiu, @huxia

@BarakRaveh 🧰 Source code and benchmark data are openly available:

@BarakRaveh This study introduces the TEMPO integrator, a temporally multiscale method that reduces the number of force evaluations in Brownian dynamics simulations. Benchmarked on IDP models and nucleocytoplasmic transport, TEMPO-enabled simulations achieved 27–32x speedups while preserving.

🧮 Explore the latest from Bioinformatics Advances: “The TEMPO integrator: Accelerating molecular simulations by Temporally Multiscale Force Prediction” . Full article available: . Authors include: @BarakRaveh

🖥️ Source code and sample data can be found here: 

It implements both maxT and adaptive permutation methods, achieving speedups of up to 447x over PLINK on large datasets. The tool is deployable via AWS EC2 and requires no prior FPGA expertise.

This paper presents an FPGA-based tool that accelerates genome-wide association study (#GWAS) permutation testing for continuous #phenotypes.

🧠 Just out in Bioinformatics Advances: “FPGA acceleration of GWAS permutation testing” . Explore the full study:

@pietermonsieurs 💻 The DAPCy software is available #opensource: .Find code, documentation, and tutorials here: