PyKappa: Rule-based modeling in Python

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• Biology/BioinformaticsSep 25, 2026

  An integrated platform for genome-wide functional analysis in Aspergillus niger through Ac/Ds insertion profiling and metabolic modeling

  Here, we establish a unified experimental and computational platform in Aspergillus niger ATCC1015 by combining curated metabolic reconstruction, Ac/Ds transposon mutagenesis and machine-learning-based essentiality prediction.

  K. Huang, Y. Lin, B. Wang and 1 more — bioRxiv

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• Biology/BioinformaticsSep 25, 2026

  ARSA: an autonomous research scientist for target nomination in Alzheimer's disease and related dementias

  To address this challenge, we present ARSA, an autonomous research scientist that transforms natural-language research interests and molecular data into prioritized, evidence-grounded target shortlists.

  X. Li, J. Liu, J. Liu and 8 more — bioRxiv

  PreprintReal-world use

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• Biology/BioinformaticsSep 25, 2026

  SurfGraphPro: Integrating Protein Language Models with Geometric Deep Learning On Coarse Protein Surfaces for Binding Site Prediction

  To our knowledge, this represents the first approach to integrate protein language model embeddings with coarse geometric surface representations for binding site prediction, demonstrating that learned evolutionary features coupled with geometric transformers can replace traditional feature engineering without sacrificing performance.

  A. Habib, L.-W. Hung, K. Gibson and 3 more — bioRxiv

  PreprintBold claims, read critically

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• Biology/BioinformaticsSep 25, 2026

  An open imaging and AI resource enabling unbiased quantification of extrachromosomal DNA at scale

  Here we establish an open resource for computational ecDNA imaging, integrating 2,986 native-resolution metaphase FISH image sets with manual annotations, standardized benchmarks and open-source quantification frameworks.

  P. Behnamie, R. Summers, J. Chen and 7 more — bioRxiv

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• Biology/BioinformaticsSep 25, 2026

  CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation

  Together, these results establish CMAC as a quantitative, deep learning framework for assigning absolute transcriptional maturation age and quantifying deviations from normal cardiomyocyte development.

  K. Quansah, N. Zureick, E. Chen and 7 more — bioRxiv

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