
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
@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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🧮 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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This paper presents an FPGA-based tool that accelerates genome-wide association study (#GWAS) permutation testing for continuous #phenotypes.
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@pietermonsieurs 💻 The DAPCy software is available #opensource: .Find code, documentation, and tutorials here:
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@pietermonsieurs DAPCy reimplements the DAPC method in #Python using scikit-learn to enhance scalability for large #genomic datasets. It applies truncated SVD for dimensionality reduction, supports sparse matrices, and introduces cross-validation strategies to improve accuracy and efficiency over.
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🐍 Now published in Bioinformatics Advances: “DAPCy: a Python package for the Discriminant Analysis of Principal Components method for population genetic analyses” . Full article available: . Authors include: @pietermonsieurs
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@RishadShafik This study applies logic-based Tsetlin Machines to predict bacterial pathogens in PD-related peritonitis from effluent immune profiles. A hierarchical classification strategy achieved up to 100% validation accuracy at key stages, outperforming neural networks while enabling full.
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🔍 Just out in Bioinformatics Advances: “Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines” . Explore the full study: . Authors include: @RishadShafik
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