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Bioinformatics Advances

@BioinfoAdv

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A fully open access, peer-reviewed journal published jointly by Oxford University Press and the International Society for Computational Biology.

Joined April 2021
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@BioinfoAdv
Bioinformatics Advances
6 minutes
@sayalaruano @Henrywebel 🧰 Code is available #opensource: .Documentation can be found here: 
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@BioinfoAdv
Bioinformatics Advances
6 minutes
@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.
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@BioinfoAdv
Bioinformatics Advances
6 minutes
📄 Explore the latest from Bioinformatics Advances: "VueGen: Automating the generation of scientific reports" . Full article available: . Authors include: @sayalaruano, @Henrywebel
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@BioinfoAdv
Bioinformatics Advances
1 day
@DelaplaceFranck 🖥️ BooN is #opensource and available on GitHub:
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@BioinfoAdv
Bioinformatics Advances
1 day
@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.
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@BioinfoAdv
Bioinformatics Advances
1 day
🧠 Recently published in Bioinformatics Advances: "BooN: Boolean network analysis software" by @DelaplaceFranck. Explore the full study:
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@BioinfoAdv
Bioinformatics Advances
2 days
🛠️ Open-source code and materials available:
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@BioinfoAdv
Bioinformatics Advances
2 days
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.
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@BioinfoAdv
Bioinformatics Advances
2 days
🧬 Now published in Bioinformatics Advances: “Survey and improvement strategies for gene prioritization with large language models” . Explore the full study:
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@BioinfoAdv
Bioinformatics Advances
2 days
@RuixiangT @DongxueMao @LiuPF @ZhandongLiu @huxia 🛠️ Open-source code and materials available:
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@BioinfoAdv
Bioinformatics Advances
2 days
@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.
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@BioinfoAdv
Bioinformatics Advances
2 days
🧬 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
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@BioinfoAdv
Bioinformatics Advances
3 days
@BarakRaveh 🧰 Source code and benchmark data are openly available:
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@BioinfoAdv
Bioinformatics Advances
3 days
@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.
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@BioinfoAdv
Bioinformatics Advances
3 days
🧮 Explore the latest from Bioinformatics Advances: “The TEMPO integrator: Accelerating molecular simulations by Temporally Multiscale Force Prediction” . Full article available: . Authors include: @BarakRaveh
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@BioinfoAdv
Bioinformatics Advances
4 days
🖥️ Source code and sample data can be found here: 
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@BioinfoAdv
Bioinformatics Advances
4 days
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.
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@BioinfoAdv
Bioinformatics Advances
4 days
This paper presents an FPGA-based tool that accelerates genome-wide association study (#GWAS) permutation testing for continuous #phenotypes.
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@BioinfoAdv
Bioinformatics Advances
4 days
🧠 Just out in Bioinformatics Advances: “FPGA acceleration of GWAS permutation testing” . Explore the full study:
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@BioinfoAdv
Bioinformatics Advances
7 days
@pietermonsieurs 💻 The DAPCy software is available #opensource: .Find code, documentation, and tutorials here:
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