Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W43).

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. OmicsBench: a long-horizon multi-turn benchmark of AI agents on research-study-scale bioinformatics
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.21074
   Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most benchmarks of agents' bioinformatics capabilities pose a single short task with a single-shot prompt.
2. Awakening as a Trait-Level Transformation of Consciousness
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.20856
   Awakening is reported across contemplative traditions yet remains poorly defined in science, where it is often conflated with transient states induced by meditation or psychedelics, obscuring its significance for theories of consciousness and cognitive architecture.
3. Poisoning the corpora of protein design systems during pre-training and retrieval
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.21200
   Protein foundation models increasingly support the generation of novel biological sequences, yet their reliance on large public datasets creates opportunities for adversarial manipulation of the material they learn from and retrieve.
4. How deterministic trajectories and their fluctuations underlie allele-frequency statistics in the strong-selection regime
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2609.14549
   We determine statistics of allele-frequency trajectories across replicate populations.
5. Forecasting Without Full Identification: Reliability and Computational Cost in Clot-Growth Simulation
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2608.08165
   We forecast clot growth from sparse early measurements with incomplete biochemical information.
6. Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2505.02712
   Cellular reprogramming, the artificial transformation of one cell type into another, has been attracting increasing research attention due to its therapeutic potential for complex diseases.
7. Designing Protein Structures from Natural Language
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2610.13917
   A central challenge in de novo protein design is generating proteins with desired structural and functional properties.
8. 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.
9. Uncertainty-aware neural emulation reveals climate-resilient maize traits at scale
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2605.22848
   Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive for large scale exploration of genotype and environment interactions.
10. JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2602.17162
   Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature".
11. Neural Computation Runs on Dissipative Structures Under Cyclic Calibration: A Systems Perspective on Sleep
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.21486
   Biological function operates far from thermodynamic equilibrium, sustained by metastable structures that continuously cycle through synthesis, function execution, and degradation--consuming energy and exporting entropy in the process.
12. Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2601.07871
   Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging.
13. Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.19053
   Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life.
14. Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/1307.2965
   The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities.
15. On the Regularity of Reaction Systems
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2409.05340
   Reaction networks have been widely used as generic models in diverse areas of applied sciences, such as biology, chemistry, ecology, epidemiology, and computer science.
16. QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2512.23080
   The discovery of novel odorant molecules is key for the fragrance and flavor industries, yet efficiently navigating the vast chemical space to identify structures with desirable olfactory properties remains a significant challenge.
17. 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.
18. EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.15302
   We introduce EPI-KAN, a novel method for estimating COVID-19 time-varying parameters.
19. Deep learning-based virtual perturbation reveals increasing heterogeneity of gene regulatory responses with aging
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.19029
   A central hypothesis in aging biology proposes that the gradual accumulation of stochastic molecular damage drives transcriptional deregulation (TD), ultimately leading to functional decline.
20. Transferable conditional brain charts link cortico-subcortical structure to Alzheimer's disease progression
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.21660
   Normative models chart how brain measures vary across the lifespan, expressing each individual as a deviation from a healthy reference.
21. RetroTAC: Learning Route-Derived Synthetic Accessibility for PROTACs
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2610.13639
   Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation, expanding therapeutic opportunities beyond conventional inhibition.
22. Leaky protection reveals three patterns in the relative advantages of vaccination strategies across transmission intensity
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2610.13567
   Targeted vaccination often prioritizes individuals who contribute most to transmission.
23. An Octahedral Fibrous Constitutive Model for Heart Valve Mechanics and Function
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2606.04234
   Fibrous soft tissues derive their nonlinear mechanical response from networks of extracellular matrix fibers.
24. The influence of decay in dynamic models of gene regulatory networks
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2610.18261
   We investigate the influence of decay rates on the dynamics of three-dimensional gene regulatory networks modeled by nonlinear differential equations with logistic sigmoidal regulatory functions.
25. RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2510.24736
   Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains challenging due to limited data, complex sequence-function relationships, and the narrow space of biologically viable sequences.
26. 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.
27. Taxonomic Classification with Tag Arrays
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2610.10500
   Taxonomic classifiers based on exact matches differ in which matches they use and in how each match splits its credit among the taxa containing it: Kraken~2 gives a $k$-mer's credit to the lowest common ancestor of those taxa, SPUMONI~2 labels each position of the read, Tagger gives each maximal exact match (MEM) one label, and Cliffy splits a match's credit evenly over a roughly logarithmic sample of the taxa.
28. Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2610.20766
   Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces.
29. AI Scientists for Building Virtual-Cell Models
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2508.02276
   Autonomous agents, from virtual laboratories to biological data-analysis agents and co-scientists, have begun to turn scientific goals into hypotheses.
30. On estimating the effective sample size of phylogenetic trees in an autocorrelated chain
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2603.03521
   Estimating the effective sample size (ESS) is fundamental in Bayesian phylogenetic inference to account for autocorrelation in MCMC samples.
31. MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2610.16212
   Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared only at late training checkpoints, obscuring differences in convergence efficiency.
32. Enforcing Thermodynamic Consistency in Parameter Optimization and Bayesian Inference of Biochemical Reaction Networks
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2610.17155
   Subcellular signaling pathways regulating, e.g., cell division or synaptic plasticity, are often modeled as dynamical networks of reactions satisfying the law of mass action.
33. MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2610.13305
   We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across diverse target backbones.

Sources in this brief: q-bio.BM (Biomolecules); q-bio.CB (Cell Behavior); q-bio.GN (Genomics); q-bio.MN (Molecular Networks); q-bio.NC (Neurons and Cognition); q-bio.OT (Other Quantitative Biology); q-bio.PE (Populations and Evolution); q-bio.QM (Quantitative Methods); q-bio.TO (Tissues and Organs).
Selected 33 of 476 available items for this weekly brief.