Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W38).

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. ADMET-EvO: a self-evolving scientific agent for sustained research across heterogeneous tasks
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2609.10121
   Scientific agents can move beyond automated model building by using accumulated evidence to revise both their questions and experimental strategies.
2. What good is modeling? Introducing ecology and evolution students to theory
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2604.13344
   Theory and empirical science should be in constant dialogue, but often find it hard to understand one another.
3. A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2609.08070
   The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question.
4. When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2512.10147
   \textbf{Motivation:} Pre-trained protein language models (PLMs) such as ESM-2 have become the default representation for biological sequence tasks, but they are computationally heavy and require GPUs both for embedding and for fine-tuning.
5. SpCAST enables scalable and interpretable integration of single-cell RNA sequencing and single-cell-resolved spatial transcriptomics
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2605.26904
   Single-cell-resolution spatial transcriptomics (scST) preserves tissue architecture but often provides targeted or sparse transcriptomic measurements, whereas scRNA-seq offers broader coverage without spatial context.
6. Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2609.10193
   Protein-protein interaction (PPI) databases do not faithfully reflect biological realities.
7. Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2608.30337
   Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification.
8. Aletheia: An Offline-First Clinical Decision Support System for Differential Diagnosis in Low-Resource Healthcare Settings
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.24814
   Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in rural settings.
9. Continuum modeling of fluidic and elastic flow during growth-driven wound closure in partial-EMT cell monolayers
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2607.05820
   Large-scale circular gap closure occurs over a time scale on which cell growth and proliferation become important.
10. Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2609.05438
   The human brain folds in utero, primarily during late gestation.
11. DNA: Differentially private Neural Augmentation for contact tracing
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2404.13381
   The COVID19 pandemic had enormous economic and societal consequences.
12. Stationary covariance spectra of discrete-time non-normal random recurrent dynamics
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2606.31944
   Principal component analysis is widely used to characterize structure in the dynamics of recurrent neural networks.
13. Population-scale Ancestral Recombination Graphs with tskit 1.0
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2602.09649
   Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics.
14. Antipolar Cell-cell Adhesion-causing Collective Motility Disorder
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2609.01946
   In this study, we aim to theoretically investigate antipolar cell-cell adhesion, in which adhesion sites are located on the opposite side of the leading edge of migrating cells, as a candidate for irregularly polarized adhesion that induces disorder in collective cell migration.
15. Systematic pathway comparison on the powerset of rule-based biochemical systems
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2608.23180
   Computational pathway design often focuses on evaluating selected pathways or optimizing fluxes in a fixed network, but gives less direct access to the combinatorial question of which other enzyme subsets of the network can support productive alternative pathways.
16. Multi-ligand simultaneous docking of Carica papaya leaf phytochemicals, Carpaine and Rutin, reveals multi-mechanism inhibition of cancer proteins BCL-2 and WWP1
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2609.08547
   Cancer remains a major global health concern due to chemotherapy resistance and toxicity from high-dose treatments.
17. Conservative deterministic Markov models in mathematical biology: uniqueness of steady states, reversibility and computational methods
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2608.27252
   Ordinary differential equations are commonly used throughout the sciences to build mechanistic models of time-dependent processes.
18. ProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2609.09891
   Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically ''undruggable'' targets via the ubiquitin-proteasome system.
19. Competition drives excessive recruitment in collective search
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2609.11715
   Groups that search collectively often exploit what they find by recruiting: one member directs others to a site it has found.
20. Early psychosis shows deviations in scaling behaviour within a critical regime
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2606.06290
   Accumulating evidence suggests that large-scale brain activity exhibits scale-invariant dynamics consistent with operation in a near-critical regime.
21. Fusing Sequence Motifs and Pan-Genomic Features: Antimicrobial Resistance Prediction using an Explainable Lightweight 1D CNN-XGBoost Ensemble
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2509.23552
   Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis.
22. Stochastic mutation as a mechanism for the emergence of SARS-CoV-2 new variants -- A Scientific Conjecture on Artificial Intelligence Paradigm
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2502.10471
   The article summaries authors' researches on spreading dynamics of COVID 19 by use of the method of continuously asking and answering questions.
23. Uncovering Cellular Resolution in scRNAseq via Unbiased Cell and Gene Network Analysis
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2608.22982
   Conventional annotation of single-cell RNA-sequencing (scRNA-seq) data relies heavily on manual, marker-based thresholding, an approach that can obscure subtle transcriptomic gradients and collapse functionally distinct cell states into broad, heterogeneous populations.
24. Predicting directional flexibility in proteins
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2609.08474
   Predicting protein dynamics is a long-standing problem in computational structural biology.
25. Estimating Time-Dependent COVID-19 Parameters Using Kolmogorov-Arnold Network and Physics-Informed Neural Network
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.15302
   We introduce a novel method for estimating COVID-19 time-varying parameters.
26. Multi-fidelity batch Bayesian optimization for bioprocess development across scales
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2508.10970
   Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and food.
27. Jointly estimating transmissibility and prior immunity from epidemic time series
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.21657
   Infectious disease time series are often used to estimate a pathogen's basic reproduction number, $R_0$.
28. Discovering Subtypes of Neurodegenerative Progression with a Scalable Connectome-Constrained Dynamic Model
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2609.10890
   Parkinson's disease is clinically and biologically heterogeneous, yet its spatiotemporal progression remains poorly characterized.
29. A Network-Structured Bayesian Hierarchical Model for Sparse Mutation-Drug Response Associations: Application to Cancer Pharmacogenomics
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2609.05784
   We develop a network-structured Bayesian hierarchical model for sparse association mapping between genomic alterations and quantitative treatment-response phenotypes.
30. A quantitative model for the emergent population dynamics of the melanoma MITF rheostat
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2607.11820
   Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes.
31. DigiPhen: a new paradigm for building predictive models of biological systems
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2608.22079
   Reengineered biological systems have the potential to revolutionize chemical and material production, enhance critical mineral recovery, serve as threat sensors and improve human health.
32. PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2609.08101
   Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry.
33. COLD-CI: A large-scale very high-resolution label polygon dataset for cocoa and non-cocoa classification in Cote d'Ivoire
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2606.20767
   Spatially explicit information on cocoa cultivation is essential for land-use planning, deforestation monitoring, environmental assessment, and supply-chain analysis.

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 457 available items for this weekly brief.