Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W32).

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. IndelFreeAligner: A Streaming Aligner for Comprehensive Gapless Alignment Against Terabase-Scale References
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2607.27291
   The comparison of short sequences to massive reference databases is a cornerstone of modern genomics, but it presents a significant scalability challenge.
2. ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2607.27308
   We introduce ZUNA1.1, a 380M-parameter diffusion autoencoder for flexible EEG signal reconstruction.
3. The evolution of cooperation under imperfect phenotypic recognition
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.27570
   Phenotypic similarity is a classical mechanism for the evolution of cooperation.
4. GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2607.19083
   Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes.
5. TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2607.26854
   Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation.
6. Amortized Bayesian Causal Discovery of Extended Factor Graphs
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2607.22934
   Learning causal graphs from interventional data is a challenging problem with broad applications.
7. Necessary and sufficient condition for hysteresis in the mathematical model of the cell type regulation of \textit{Bacillus subtilis}
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2009.06524
   The key to a robust life system is to ensure that each cell population is maintained in an appropriate state.
8. BODIESReg: An Open-Source Pipeline for Registering 3D Body Scans Using Pose-Aligned Initialization
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.15463
   Biomechanical models are used to quantify and optimize human movement in clinical rehabilitation, sports science, and occupational health.
9. TRAECR: A Tool for Preprocessing Positron Emission Tomography Imaging for Statistical Modeling
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2511.04458
   Positron emission tomography (PET) imaging is widely used in a number of clinical applications, including cancer and Alzheimer's disease (AD) diagnosis, monitoring of disease development, and treatment effect evaluation.
10. DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2511.09026
   Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood.
11. Using Theory of Mind to Arbitrate between Social and Non-social Learning
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2607.28601
   Social learning is a powerful mechanism through which agents learn about the world from others.
12. Evolutionary adaptation through bet-hedging in finite populations under fluctuating environments
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.27983
   Natural populations evolve under fluctuating environments and limited resources, yet it is unclear how these factors jointly shape adaptation.
13. Markov state models revisited: Principles and algorithms for unbiased observables
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2607.19452
   Markov state models (MSMs) have become ubiquitous tools for analyzing molecular dynamics (MD) simulations because of their simple, powerful premise: although complete MD sampling may be impossible, the MSM can "stitch together" transition probabilities derived from local sampling to provide a global picture of kinetics and mechanisms.
14. Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2601.10202
   Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and generalizable detection methods from electrocardiogram (ECG) recordings.
15. Construction of an Inducible TBCE Overexpression System to Probe Tubulin Cofactor-Mediated Microtubule Homeostasis
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2607.23898
   We are studying a new regulator of microtubule homeostasis, wdA, in the fungus Aspergillus nidulans.
16. Open Questions about Time and Self-reference in Living Systems
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2508.11423
   Living systems exhibit a range of fundamental characteristics: they are active, self-referential, self-modifying systems.
17. From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2607.02564
   Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.
18. When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2607.25288
   Single-cell ribonucleic acid sequencing (scRNA-seq) is a foundational technology for precision-medicine workflows that contribute to United Nations Sustainable Development Goal 3 on Good Health and Well-being, and unsupervised clustering is the analytical step that turns raw expression matrices into interpretable cell populations.
19. Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2607.24797
   In the literate human brain, reading and writing doubly dissociate: a ventral decoding route (pure alexia) and a fronto-parietal encoding route (pure agraphia), sharing a partial orthographic core.
20. Hash Chemistry: Minimal Models for Evolutionary Growth of Complexity
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.28219
   Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities of arbitrary size, opening a combinatorially vast possibility space (a ``cardinality leap'').
21. Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2607.23518
   The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination.
22. Nanobot Algorithms for Treatment of Diffuse Cancer
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2509.06893
   Motile nanosized particles, or "nanobots", promise more effective and less toxic targeted drug delivery because of their unique scale and precision.
23. MERLIN-SUITE: Probabilistic modular GRN inference from multi-omics data integrating regulatory priors and transcription factor activity
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2607.01791
   Accurately reconstructing gene regulatory networks (GRNs) is essential for understanding transcriptional processes in development and disease.
24. Ecological systems in a modeling perspective
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2603.26860
   May (1974,1976) opened the debate on whether biological populations might exhibit nonlinear dynamics and chaos.
25. PPanGGOLiN V2: technical enhancement and extended functionalities for prokaryotic pangenome analysis
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2607.24111
   The exponential growth of genomic data, particularly for microbes, has made pangenomic approaches a gold standard for large-scale comparative genomics.
26. Three Failures of Pain Location: Why the Diagnostic Utility of Symptom Localization Is Not One Thing
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2607.26297
   Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others.
27. Causal Architecture Dynamics Prior to Arrival of Self-replicators in a Model of Catalytic Networks Relevant to Origin-of-Life
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2607.28250
   Agents that exert causal power in the world are thought to be the product of selection among diverse replicators; what is the causal structure of a medium before replicators appear, and evolution takes hold?
28. Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2607.21561
   Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution.
29. Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2607.25609
   Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts.
30. HyperNetWalk: A Unified Framework for Personalized and Cohort-Level Cancer Driver Gene Identification via Reverse Inference on Layered Signaling-Regulatory
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2601.01337
   Accurate identification of cancer driver genes from passenger mutations is essential for understanding tumorigenesis and clinical translation.

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 30 of 345 available items for this weekly brief.