arXiv:2606. 07686v1 Announce Type: cross Abstract: Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods.
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arXiv:2509. 00123v2 Announce Type: replace-cross Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions.
arXiv:2607. 06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering.
arXiv:2607. 20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited.
arXiv:2607. 02103v1 Announce Type: cross Abstract: Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings where nonlinear interactions dominate and class imbalance further complicates reliable prediction of minority phenotypes.
The paper introduces BioGlyph, a method that translates network topology into a language of structural roles such as hubs, community cores, and cross-community connectors. By encoding these roles in a universal, interpretable vocabulary, BioGlyph allows large language models to answer structural reasoning questions about networks more accurately—improving system accuracy by up to 26 percentage points compared to edge-based or numerical representations. Experiments across twenty networks in five domains show the approach is especially effective for dense, community-structured networks and reveals biologically meaningful patterns in a budding-yeast protein-interaction network.
arXiv:2606. 20329v1 Announce Type: new Abstract: Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats.