Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W41).

Clay tracks publications from the Quantitative Biology feed list. Below are recent items from this category, each with its source and a short description of what the publication covers when one is available in the source feed.

Recent publications:
1. GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2609.35882
   Genome foundation models are most useful where sequences are generated, yet the largest models need datacenter accelerators and a place to send private DNA.
2. Fold'EM: Direct atomic structure inference from Cryo-EM particles
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2610.01358
   Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination.
3. Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2607.07425
   Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data.
4. Reduced Hodgkin-Huxley models based on the correlation between sodium and potassium gating variables
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2402.19185
   We analyse the interaction between the sodium (Na$^+$) and potassium (K$^+$) gating variables in the 4D Hodgkin-Huxley axonal electrophysiological model.
5. GenoTrace: Inheritable Watermarks for Genome Foundation Model Distillation
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2609.35881
   Can a genome model retain a detectable record of the synthetic sequences used to train it?
6. Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2609.12223
   Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation.
7. A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2606.13475
   In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse data.
8. Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2610.03160
   Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity of bnAb, incomplete understanding of their cellular origins across pathogens, and the inability of existing computational tools to generalize across emerging viral threats.
9. Generating eukaryotic reference genome assemblies: Earth BioGenome Project quality standards and recommendations
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.03075
   Reference genome assemblies are foundational resources across the biological sciences as they begin to expose the fundamental building blocks of each species and the molecular toolkits vital for adaptation and survival.
10. Parameter uncertainty in dynamical models: a practical identifiability index
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2606.08475
   Ordinary differential equation models are widely used to describe how complex systems change over time, but reliable conclusions depend on how well their parameters can be estimated from limited, noisy data.
11. Generalizable single-cell perturbation response prediction using energy-guided flow matching
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2610.02232
   Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems.

Sources in this brief: q-bio.BM (Biomolecules); q-bio.CB (Cell Behavior); q-bio.GN (Genomics); q-bio.QM (Quantitative Methods).
Selected 11 of 11 available items for this weekly brief.