Papers

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Quantitative Biology

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Saturday 8 August

Neurons and Cognition

Sensorimotor features of a reversal learning task bias decision behavior without disrupting individual difference structure

Elliot Huang, William Xu and Robert C. Wilson

In computational psychiatry, task-irrelevant factors such as a task's perceptual and motor features are typically assumed not to bias decision behavior, but at most to add noise. Yet growing evidence links sensorimotor processing to decision-making through multiple pathways, challenging this assumption. We tested this directly using a two-choice probabilistic reversal learning task completed by 90 participants under two sensorimotor conditions: a stationary condition requiring only arm movements to respond, and an active condition requiring participants to walk between physically separated computers. Task performance, measured as the propensity to choose the more probable rewarding option, did not differ between conditions. However, stay rate (the tendency to repeat the previous choice) was significantly elevated in the stationary condition, but only among participants who completed the active condition first; those who completed the stationary condition first showed no such difference. Logistic regression weights capturing the influence of recent win history on choice showed the same pattern, with stationary-condition elevation restricted to the active-first group. Cross-condition correlations for both measures were strong, indicating that this order-dependent effect shifted absolute measurements without disrupting individual difference structure. Prior sensorimotor history therefore does not compromise the task's ability to extract stable cognitive variables, but does bias the values it yields - a non-random source of variance that, if it generalizes beyond this experiment, could shift patients across diagnostic thresholds in proposed clinical applications. The finding adds to a growing list of potential methodological contaminants in computational psychiatry's cognitive-task paradigms that must be characterized and mitigated before clinical translation.

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Quantitative Methods · Cross-listed from eess.IV

$\texttt{DisMorph}$: learning to disentangle technical distortions from true biological change

Jingru Fu, Kathleen E. Larson, Douglas N. Greve et al. (+2 more)

Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registration is a key tool for estimating such change by computing a dense deformation that captures geometric differences between longitudinal scans. However, MRI scanners introduce geometric distortions that vary across acquisition systems and protocols, such as gradient non-linearity (GNL) distortion. Existing registration methods estimate a single field that conflates biological and technical effects, potentially biasing downstream morphometric measurements if distortions remain (partially) uncorrected. We propose $\texttt{DisMorph}$, a registration framework trained entirely on synthetic data that explicitly decomposes longitudinal deformation into technical and anatomical transforms. It predicts two dense deformations, each encoding one effect. During training, a novel generative model synthesizes both effects separately to provide disentanglement supervision, while domain randomization promotes generalization across imaging protocols. We evaluate our method in three complementary settings. On simulated data with known ground truth, our method detects anatomical change more accurately and consistently than conventional registration. On real image pairs that differ only by GNL distortion, our method assigns most geometric change to the distortion field, demonstrating specificity in the absence of anatomical change. On longitudinal Alzheimer's disease (AD) pairs, our method detects anatomical change in AD-related brain structures while identifying residual distortion left after standard correction. By disentangling MRI-induced distortion from biological change in the longitudinal deformation, our method paves the way for more accurate longitudinal morphometry in clinical settings where maintaining acquisition consistency is challenging.

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All authors Jingru Fu, Kathleen E. Larson, Douglas N. Greve, Bruce Fischl, Malte Hoffmann

Quantitative Methods · Cross-listed from cs.LG

Forecasting Without Full Identification: Reliability and Computational Cost in Clot-Growth Simulation

Lennon J. Shikhman, Lily DiPaulo, Ying Qian et al. (+1 more)

We forecast clot growth from sparse early measurements with incomplete biochemical information. Using 1,000 simulated trajectories, we compare inverse search, which weights candidate inputs by their agreement with early observations, with a direct multilayer perceptron (MLP) predicting future clot size. Inverse search is more accurate when tissue factor alone, or tissue factor and fibrinogen, are unknown. When all five inputs are unknown, direct prediction achieves 2.16% pooled relative $L_2$ forecast error, outperforming search with 8,192 candidates using a forward MLP (3.25%) or latent neural ordinary differential equation model (3.36%). A conditional error bound separates candidate disagreement, weight on poor early fits, surrogate error, and candidate coverage. Increasing randomized quasi-Monte Carlo candidate budgets from 512 to 32,768 reduces numerical variation. A covariance score ranks additional assays by expected forecast value while accounting for measurement noise. At 10% range-scaled assay noise, incorporating assay uncertainty reduces forecast error from about 5% to below 3% for both surrogates. An independent reaction cascade verifies candidate integration against tensor quadrature, with mean normalized forecast-integration error below $10^{-5}$ at 32,768 candidates. These results demonstrate accurate clot-growth forecasting without full biochemical identification and support selecting prediction strategies and additional measurements according to accuracy, reliability, and computational cost.

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All authors Lennon J. Shikhman, Lily DiPaulo, Ying Qian, He Li

Subcellular Processes · Cross-listed from cond-mat.stat-mech

Low-frequency output fluctuations in an open exclusion process with particle pausing

Quentin Thommen

Slow internal states reshape both the mean throughput and the temporal organization of a driven lattice gas. Exit-counting statistics reveal this effect in a finite open totally asymmetric simple exclusion process whose particles reversibly switch between active and paused states. Increasing pausing lowers the mean current smoothly, whereas the long-window Fano factor is strongly nonmonotonic. At the reference boundary rates, the maximum remains near a measured mean paused population $N_p=L\rho_{\rm paused}\simeq1.5$–$2$ across lattice lengths L=50-500, while the corresponding pausing rate scales as $k_p^{\rm max}\propto L^{-1}$. A minimal constant-birth, linear-death approximation translates an order-one collective crossover into this finite-size displacement and gives $N_p^\star\simeq1.50$ in the independent-pause, strong-blocking limit. The simulations delimit this approximation: the pause number is overdispersed, and at fixed $N_p$, slower unpausing increases both the correlation time and the noise amplitude. Residence-time and structural analyses further separate the relevant slow variables. The pause-free versus pause-containing residence-time scale tracks the fitted output-correlation time, whereas the noise amplitude follows fluctuations, rather than the mean size, of the largest particle cluster. Low-frequency output noise therefore identifies an intermittent finite-size regime shaped jointly by slow-defect kinetics and traffic-jam reorganization.

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Genomics

MAPK Pathway Activity and Heme Biosynthesis Gene Expression in IDH-Wildtype Glioblastoma: A Purity-Adjusted, Discovery-Validation Analysis

Manveer Singh Tib

5-aminolevulinic acid (5-ALA) promotes fluorescence-based resection of glioblastoma via protoporphyrin IX (PpIX); however, there is considerable heterogeneity in fluorescence intensity, limiting margin distinction. Cell-line studies show that stimulation of the MAPK pathway results in decreased PpIX levels through increased elimination by ABCB1, an efflux transporter, and ferrochelatase (FECH), the enzyme that converts PpIX to heme. However, the mechanism has yet to be studied in human tissue. Using two independent, purity-adjusted sets of primary IDH-wildtype glioblastoma specimens (TCGA-GBM, n=140, discovery; CGGA, n=87, validation), no reproducible correlation between MAPK signaling and its proposed downstream effectors (ABCB1 and FECH) could be detected. Instead, MAPK activity displayed a reproducibly negative relationship with PPOX, the enzyme that converts protoporphyrinogen IX to protoporphyrin IX: TCGA-GBM (n=133 with purity estimates; rho = -0.40, 95% bootstrap CI [-0.54, -0.25], adjusted p < 0.001), CGGA (rho = -0.28, 95% bootstrap CI [-0.47, -0.07], adjusted p = 0.0342). The finding was robust to permutation testing and to alternative approaches to purity adjustment. Here we report, for the first time, replicated human-tissue evidence for this association. If this correlation reflects causality, MAPK regulation of PpIX would act at the point of synthesis rather than at the clearance steps implied by previous cell-culture studies. We observed differential activation of the MAPK pathway with increased activation in the classical subtype and decreased activation in the proneural subtype. There was no correlation between MAPK pathway activation and overall survival (log-rank p = 0.73). The association between PPOX expression and MAPK pathway activation is biological rather than prognostic, identifying the MAPK pathway as a candidate handle for increasing 5-ALA fluorescence.

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Friday 7 August

Quantitative Methods

Remote Sensing-Based Hybrid Statistical and Machine Learning Ensemble Modeling of Yellowfin Tuna Catch in the Western Equatorial Indian Ocean

Avishka Wijepala, Sharukshan Niranjan, Lisitha Abeysekara et al. (+1 more)

Yellowfin tuna (Thunnus albacares) is an economically important pelagic species in the Indian Ocean, but its monthly catch variability is difficult to predict because reported catch is influenced by ocean productivity, thermal habitat, monsoon seasonality, fishing-ground characteristics, and fleet behavior. This study develops a remote sensing-based statistical and machine learning stacked ensemble for modeling monthly yellowfin tuna catch across five fishing grounds in the western equatorial Indian Ocean during 2003-2024. Monthly fishery records were integrated with satellite-derived chlorophyll-a concentration (CHL) and sea surface temperature (SST). Linear mixed models (LMMs) and a generalized additive mixed model (GAMM) were used to represent interpretable current and lagged environmental effects, while Random Forest models captured nonlinear interactions among lagged, rolling, seasonal, anomaly, and spatial predictors. Expanding-window validation over the pre-COVID evaluation period from 2008 to 2019 was used to reduce temporal leakage. Among the individual models, the GAMM achieved the best log-scale performance, with an R-squared value of 0.137 and an RMSE of 1.284, while RF-E6 achieved the lowest RMSE on the kilogram scale. The nonnegative Ridge stacked ensemble improved the log-scale R-squared value to 0.177, with an RMSE of 1.254 and an MAE of 1.000. These results show that satellite-derived CHL and SST contain useful but incomplete predictive information for monthly catch modeling.

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All authors Avishka Wijepala, Sharukshan Niranjan, Lisitha Abeysekara, Pasindu Weerasinghe

Molecular Networks

Permutation theory governs long-term dynamics of critical Boolean networks

Venkata Sai Narayana Bavisetty, Matthew Wheeler, Julian Vignes et al. (+4 more)

Boolean networks are widely used to model gene regulatory Attractors of Boolean networks model stable gene-expression patterns, yet deriving their properties from network structure remains an open problem. We solve this problem for critical $K=1$ networks by showing that their feedback loops induce a permutation whose order bounds the average attractor length both above and below by universal constant factors. This correspondence allows classical results from combinatorics and number theory to be applied directly to Boolean network dynamics. We find three distinct asymptotic scales for both average and maximum attractor lengths: typical networks scale as $\exp[(1/8+o(1))(\ln N)^2]$, the ensemble means grow as $\exp[N^{1/3+o(1)}]$, and extremal networks attain $\exp[(1+o(1))\sqrt{N\ln N}]$. Thus, ensemble averages are governed by rare network realizations and are unrepresentative of typical dynamics.

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All authors Venkata Sai Narayana Bavisetty, Matthew Wheeler, Julian Vignes, Matthew Stephen Jones Jr., Ruodan Liu, Sean Campbell, Claus Kadelka

Cell Behavior · Cross-listed from physics.bio-ph

Nuclear mechanics controls the temporal dynamics of cell unjamming

Leon Hillmann, Quirine J. S. Braat, Pablo Gottheil et al. (+9 more)

Cell unjamming in dense tissues is a complex but essential process in embryogenesis and cancer metastasis. Increasing evidence suggests that nuclear mechanics and density effects play a vital role in collective cell unjamming. However, state-of-the-art cell-shape-based theories fail to include nuclear and density effects, while computer models featuring rigid nuclei disagree with experimental observations of elongated nuclei promoting unjamming. Here, we introduce a computational model of confluent cells with explicitly deformable nuclei to study the dynamics of cell unjamming. Our simulations show an unjamming transition controlled by nuclear size and shape, reconciling conflicting theories of density-driven versus shape-driven mechanisms. We predict general relations connecting cellular and nuclear shape to collective cell motion, verified experimentally in distinct monolayers of MCF-10A and MDA-MB-436 breast cells, with striking accuracy. Our work establishes a rational connection between nuclear mechanics and tissue-scale rigidity transitions, highlighting the nucleus's key role in collective cell unjamming.

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All authors Leon Hillmann, Quirine J. S. Braat, Pablo Gottheil, Eliane Blauth, Anne Marie Scholz, Kolya M. Lettl, Jürgen Lippoldt, Pieta C. M. Wielstra, Sibylle Hess, Mitko Veta, Josef A. Käs, Liesbeth M. C. Janssen

Quantitative Methods

JUMP-lite: Compact, reproducible benchmarking of cell representations

Alán F. Muñoz, Johan Fredin Haslum, Runxi Shen et al. (+2 more)

Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Painting now provide millions of images for systematic study. However, JUMP alone occupies 115 TB, and fragmented evaluation practices make systematic comparisons of representation methods impractical for many researchers. Here we present Nahual, an open-source framework for reproducible model deployment, and JUMP-lite, a 92.0 GB subset of JUMP that is approximately 1,250-fold smaller, selected to cover genetic modalities and compound annotations and reduced via lossy JPEG XL compression. Using these resources, we benchmark five representation methods, including classical features (CellProfiler) and deep learning models (MorphEM, OpenPhenom, SubCell, DINOv2). Moderate compression broadly retains signal relative to uncompressed images. Standardized phenotypic activity and consistency metrics reveal meaningful performance differences across methods. Together, JUMP-lite and Nahual provide a foundation for accessible, reproducible benchmarking of image-based cell representations.

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All authors Alán F. Muñoz, Johan Fredin Haslum, Runxi Shen, Anne E. Carpenter, Shantanu Singh

Neurons and Cognition · Cross-listed from cs.LG

FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity

Deepank Girish, Yi Hao Chan, Yubin Zheng et al. (+2 more)

Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well. While Federated Learning (FL) offers a privacy-preserving paradigm for collaborative training, standard approaches continue to struggle with statistical heterogeneity. In particular, site differences pose a key challenge in multi-site data settings. Additionally, existing FL approaches for fMRI rely on static Functional Connectivity ( FC), omitting dynamic information in brain networks. To address this, we propose FedDOSE, a novel framework that explicitly decomposes site differences for analysis of dynamic FC (dFC). FedDOSE introduces a Modularity-Guided Tucker Decomposition block to encode high-dimensional dFC tensors and capture modular-level spatio-temporal patterns efficiently. Class-specific prototypes are generated across all sites and subsequently aligned at the global level by using a combination of Optimal Transport (OT) barycenter formulation and Procrustes analysis. Extensive experiments for diagnosing Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) on three multi-site resting-state fMRI datasets: ABIDE-I, ABIDE-II, and ADHD-200, demonstrate that FedDOSE outperforms state-of-the-art methods in ASD and ADHD detection. Our results highlight its effectiveness in learning robust representations from multi-site datasets for reliable analysis.

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All authors Deepank Girish, Yi Hao Chan, Yubin Zheng, Sukrit Gupta, Jagath C. Rajapakse

Quantitative Methods

muxvizpy: a Python library for the analysis of multilayer biological networks

Matteo Baldan, Francesco Zambelli, Andrea Valsecchi et al. (+4 more)

Biological systems are inherently multilayered: the same entities—genes, cells, or bacterial species—participate simultaneously in qualitatively distinct types of interactions, each carrying complementary information that no single relational view can capture. Analysing such systems with single-layer tools, or by collapsing layers into a monoplex projection, systematically discards inter-layer dependencies and can yield misleading conclusions about centrality, community structure, and robustness. The multilayer network formalism addresses, and muxViz established one of the first comprehensive toolkits for its structural analysis, but its R-only interface and dense data structures limit applicability to large biological networks. We introduce muxvizpy, a Python library that reimplements and extends the muxViz analytical catalogue with a sparse linear-algebra stack built on SciPy and PyTorch. Muxvizpy exposes seven categories through a unified, composable API and is numerically validated against muxViz on synthetic Erdős–Rényi and Barabási–Albert multiplex networks while substantially reducing peak memory and wall-clock time at scale. We illustrate its applicability on a virus–human protein-interaction multiplex in which computing some structural analysis was unfeasible. \\[2pt] muxvizpy is freely available under the MIT licence at https://github.com/CoMuNeLab/MuxVizPy. Mathematical definitions of all implemented metrics are provided in the Additional File.

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All authors Matteo Baldan, Francesco Zambelli, Andrea Valsecchi, Giacomo Baruzzo, Giulia Cesaro, Manlio De Domenico, Barbara Di Camillo

Quantitative Methods

Artificial Intelligence Can Match Domain Experts in Evidence Extraction and Critical Appraisal of Microbial Oncogenesis Research Publications

Kaela Kokkas, Hairong Wang, Richard Klein et al. (+11 more)

Confirmed oncogenic microbes contribute significantly to cancer burden. Identifying novel microbial oncogenicity could yield strategies that will reduce disease burdens. However, relevant evidence is dispersed and infeasible for humans to comprehensively synthesize. LLMs may enable scalable, expert-level systematic evidence synthesis to identify microbe-cancer pairs; however, such capabilities have not yet been demonstrated. Domain experts were recruited to create a dataset to benchmark LLM performance (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5, GPT-5 Nano) on 24 research papers using MMTV-LV and breast cancer as a case study. We devised a structured template for evidence extraction and appraisal, consisting of MCQ, Likert-scale, multi-select, and free-text question types (77 items across 24 papers). Agreement between (1) experts and (2) experts and each LLM was determined per question instance using novel metrics. LLMs were assessed by comparing inter-expert and expert-LLM agreement distributions to determine whether LLMs behaved as additional experts by increasing or maintaining inter-expert agreement. Free-text responses were further evaluated qualitatively. Across all question types, LLM responses aligned closely with experts, with GPT-5 and GPT-5 Nano achieving score distributions indistinguishable from experts. Gemini models behaved similarly but were significantly more lenient in applying microbial oncogenesis criteria. Hallucinations were rare. Methodological appraisal and identification of contradictions within full-texts were the most persistent LLM vulnerabilities. GPT-5 and GPT-5 Nano were indistinguishable from experts on structured domain research paper evaluation tasks. This supports use of LLMs for automated systematic evidence synthesis. However, methodological appraisal tasks and contradiction identification in full-texts remain weaknesses requiring strengthening.

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All authors Kaela Kokkas, Hairong Wang, Richard Klein, Nazir A. Ismail, Natalie Irwin, Mohammad Z. Moonsamy, Kubendran Naidoo, Jeremy Nel, Ekene E. Nweke, Raveen Parboosing, Emmanuel K. Sekyi, Rebecca T. van Dorsten, Bruce A. Bassett, Robert F. Breiman

Neurons and Cognition · Cross-listed from cs.CV

International Transfer of Stochastic Cortical Self-Reconstruction

Fabian Bongratz, Zhizheng Zhuo, Chao Zhang et al. (+3 more)

Stochastic cortical self-reconstruction (SCSR) enables personalized mapping of gray matter atrophy, a hallmark of neurodegenerative disorders such as Alzheimer's disease (AD), onto high-resolution cortical surfaces. Unlike conventional normative modeling approaches, which typically operate at a coarse regional level and remain inherently constrained by the covariates included during training, SCSR estimates an individualized healthy reference directly from the observed cortical thickness at the vertex level. This allows the detection of subtle, subject-specific deviations from healthy cortical shape. In this work, we investigate the generalization and transferability of SCSR, originally trained on UK Biobank (UKB) data, to an independent Chinese population dataset. Specifically, we evaluate the ability of SCSR-derived Z-scores to discriminate between healthy scans, individuals with mild cognitive impairment (MCI), and patients with AD, while also assessing model robustness across the lifespan. We compare four training strategies: direct application of the UKB-trained model, fine-tuning on Chinese data, training from scratch, and joint training on UKB and Chinese cohorts. As reconstruction backbones, we consider both a multilayer perceptron (MLP) and a Spherical UNet (SUNet). Our results demonstrate that SCSR provides robust detection of cortical atrophy in the Chinese population across all evaluated models. The highest discriminative performance was achieved by the fine-tuned SUNet model (average pairwise AUC = 0.848), followed closely by the UKB-trained SUNet. Moreover, reconstruction errors remained low across the lifespan, even when the training population exhibited a substantially narrower age distribution, indicating strong cross-population transferability.

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All authors Fabian Bongratz, Zhizheng Zhuo, Chao Zhang, Yaou Liu, Dennis M. Hedderich, Christian Wachinger

Molecular Networks

Simulating is not always understanding: When model complexity obscures biology

Lendert Gelens, Alejandro Fábregas-Tejeda, Grant Ramsey et al. (+2 more)

In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a complete cell cycle. While these models span a continuum of detail, increasing complexity changes what they capture and are able to explain, what they can predict, and how they can fall short. A model contributes to understanding only when it makes novel predictions, reveals an unexpected coupling between processes, or fails in a way that identifies missing parameters. We contend that what is important for understanding is not the number of components or spatial dimensions a model contains, but the ratio of free parameters to the experimental constraints available to pin them down, and whether we can see why it produces the behaviors it does. Large-scale agent-based models of cytoskeletal dynamics or tissue mechanics that are built on a small number of physically grounded rules can reveal rich self-organization behavior precisely because their parameter spaces are small enough to explore systematically. By contrast, when free parameters grow faster than the data available to constrain them, models become progressively harder to interpret – and even disprove –regardless of their biological scope. We argue that the field needs to reconsider the goal of complex models. We should move away from trying to include as many parameters as possible and instead aim for systematic comparisons with simpler representations, dynamical analysis, and explicit model hierarchies that trace how cellular behavior emerges from its parts.

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All authors Lendert Gelens, Alejandro Fábregas-Tejeda, Grant Ramsey, Sylvia Wenmackers, Bart Smeets

Molecular Networks

Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling

Quanquan Li, Yihe Chi, Liuyang Song et al. (+10 more)

A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships. Existing graph-based models commonly use the same network to structure gene representations and mediate intergene interactions, thereby implicitly treating stable associations as perturbation-response pathways. Gene Ontology and control-derived coexpression networks encode relatively stable relationships rather than intervention-specific response directions or magnitudes. We therefore propose GeneGeoFlow, which conditions a control-anchored residual flow on gene-wise geometry derived from biological networks to learn intervention-specific transcriptional responses. GeneGeoFlow derives multi-scale spectral coordinates from Gene Ontology and control-derived coexpression networks. A perturbation-conditioned, gene-wise gating module selects relevant structural scales and network sources, yielding intervention-specific gene geometry. The resulting geometry conditions a control-anchored residual flow without explicitly propagating target-derived signals along the graph. Condition-wise optimal transport couples unpaired control and perturbed populations for training, while a Delta-correlation objective aligns the predicted and observed condition-level expression-shift directions. GeneGeoFlow achieves Pearson Delta scores of 0.8979 on the Norman additive benchmark and 0.9088 on five held-out drug combinations in the fixed ComboSciPlex test split. These results support perturbation-conditioned gene geometry as an effective structural prior for intervention-specific response prediction, without conflating stable gene relationships with response propagation.

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All authors Quanquan Li, Yihe Chi, Liuyang Song, Hongbo Zhang, Jingyu Li, Xidong Xi, Conghua Wei, Yijie Sun, Yu Chen, Xin Liu, Qi Hu, Jing Ke, Guitao Cao

Quantitative Methods

Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models

Doniyorkhon Obidov, Xiaolong Guo, Yonghui Li et al. (+1 more)

Large Language Models (LLMs) have accelerated drug discovery, particularly in the automated design of antimicrobial peptides (AMPs). However, current validation pipelines for peptide generation models overlook historical precedents showing that certain drugs carry health risks predominantly for individuals with specific genetic profiles. In this paper, we demonstrate that such targeted health risks can be induced intentionally and at scale by manipulating models that generate peptide candidates. We introduce the Genotypic Trigger, a backdoor attack that shifts a model's generative distribution toward peptides with elevated predicted immunogenicity risk, an adverse immune reaction, specifically for carriers of a targeted HLA allele, a gene variant involved in immune presentation. Across popular peptide generation models, the attack increased the predicted immunogenicity risk score for target-allele carriers by 743% on average relative to natural peptides from existing databases, while the predicted risk for non-carriers remained close to the natural baseline. Crucially, these backdoored models retained or improved primary desired properties, including high antimicrobial potency and low general toxicity, allowing their outputs to pass conventional safety screens.

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All authors Doniyorkhon Obidov, Xiaolong Guo, Yonghui Li, Kaichen Yang

Thursday 6 August

Neurons and Cognition

Zenons Demon and the Denial of Domain-Generality for Transformer-Based Computational Models of Human Behavior

Mark Orr, Edward A. Cranford, Ken Ford et al. (+6 more)

Transformer-based models of human behavior (e.g., the Centaur model by Binz, et al., 2025) posit to be domain general computational models of human behavior. The claim of domain-generality is by virtue of the supposed capability to predict and simulate human behavior across a vast range of cognitive and perceptual domains. Further, it is argued that this degree of performance places such models on a path toward general, unified theories of cognition (Newell, 1990). We contest this characterization. We propose the Domain-Generality Thesis: A computational model is domain-general if and only if it performs well across a structurally distinct set of tasks. While transformer-based models of human behavior achieve impressive statistical breadth, we demonstrate that, by example, they fail this structural criterion, conflating parametric variations of a single task with genuine cognitive diversity. We construct an argument that denies the domain-generality of transformer-based models of human behavior and thus denies the purported status as a start on the path towards general, unified theories of cognition.

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All authors Mark Orr, Edward A. Cranford, Ken Ford, Kevin Gluck, William Hancock, Christian Lebiere, Peter Pirolli, Frank E. Ritter, Andrea Stocco

Quantitative Methods

Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra

Jesper Løve Hinrich, Pia Susan Mayer, Bekzod Khakimov et al. (+2 more)

Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem is critical in one-dimensional proton (1D 1H) NMR which has become the standard method providing fast acquisition and information-rich spectra in metabolomics and foodomics. This study demonstrates how chemical shifts can be utilised as a strength in 1D 1H NMR, when suitably modeled through the proposed Bayesian Shift-Invariant Non-negative Matrix Factorization (BSI-NMF) procedure. We find that BSI-NMF accurately recovers the underlying chemical signals in 1D 1H NMR spectra missed by existing analyses approaches across simulations, laboratory created datasets, and a large urine dataset obtained from 2439 people across Europe. Our study highlights how shifts in the chemical signatures - until now perceived as a nuisance - can in fact when suitably modelled be instrumental for unique recovery of metabolites. This creates an opportunity to experimentally induce chemical shifts changes to facilitate unique recovery of spectra.

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All authors Jesper Løve Hinrich, Pia Susan Mayer, Bekzod Khakimov, Søren Balling Engelsen, Morten Mørup

Quantitative Methods · Cross-listed from stat.AP

Hit Selection Using SSMD-Based Machine Learning Performance Metrics in High-Throughput Screening Assays

Xiaohua Douglas Zhang

High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance. This sparsity makes conventional machine-learning performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), difficult to estimate empirically because they require adequately sized labeled samples. Here, we introduce a model-based framework that derives these classification metrics from the strictly standardized mean difference (SSMD), a well-established HTS effect-size parameter. Under a Gaussian equal-variance assumption, we derive closed-form relationships linking SSMD to Youden-optimal sensitivity and specificity, and sensitivity at a preset specificity, yielding explicit estimators and exact confidence intervals from the noncentral t-distribution, even under single-replicate designs. Unlike classical statistical power, which approaches 1 as sample size grows regardless of how small the true non-zero difference between group means is, the SSMD-derived sensitivity converges to a finite population value that reflects the true degree of separation between two groups, making it a more meaningful and stable performance measure for hit selection. We demonstrate the utility of this framework in a hepatitis C virus primary siRNA screen comprising approximately 22,000 single-replicate measurements, showing that SSMD, AUROC, and sensitivity-based thresholds yield equivalent and interpretable hit sets. This work bridges classical HTS statistics and machine-learning evaluation theory, providing a statistically principled, reproducible way to estimate classification performance in ultra-low-replication screening workflows.

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Genomics · Cross-listed from stat.ME

An Entropy-based Coefficient of Determination with Adjustment of Optimization Bias

Longhai Li

Classical likelihood-ratio tests and $\Delta$AIC exacerbate the statistical significance crisis by scaling with sample size, often flagging negligible improvements as highly significant. While causal estimands like the average treatment effect (ATE) quantify practical magnitude, their reliance on the expectation operator ties them to the data's original coordinate scale. Furthermore, existing pseudo-$R^2$ metrics are inadequate: variance-based measures ignore higher-order distributional changes, and current formulations lack invariance to monotone transformations. We resolve these limitations by introducing Entropic Variance (EV) as a rigorous, scale-independent generalization of error variance in ordinary least squares. We define the population EV-based parameter, $\rho^2_V$, which projects unbounded cross-entropy onto a standardized $[0,1]$ scale, and establish that the EV-based $F_\text{V}$ statistic asymptotically follows an $F$-distribution. Building on these distributional properties, we propose two estimators: the empirical population $R^2_{\text{SV}}$ and the out-of-sample predictive $R^2_{\text{SVP}}$. Both are derived by exponentiating per-observation cross-entropy and incorporate a degrees-of-freedom correction for training optimism. Leveraging the $F_\text{V}$-distribution, we derive refined $p$-values and confidence intervals for $\rho^2_V$ without requiring intractable Fisher information matrices. Simulation studies and a Parkinson's disease microbiome application demonstrate the superiority of variable selection via these EV-$R^2$ metrics. Notably, evaluating the $R^2_{\text{SVP}}$ of a LASSO path via data-splitting reduced false discovery rates from 80% to 6% in simulations while fully preserving signal recall.

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Populations and Evolution

Replacers and their evolutionary stability in the Moran process on graphs

Michal Pecho, Jakub Svoboda, Lenka Kopfová et al. (+2 more)

Evolutionary dynamics in finite structured populations are commonly modeled by the Moran Birth-death process. A key quantity is the fixation probability of a single invader attempting to take over a population of residents. A recent work introduced a new neighborhood-aware phenotype called a replacer. A replacer never wastes their reproductive turn by always replacing an individual of the other type (if available). In this work, we study the evolutionary stability of resident replacers who are invaded by mutant replacers. We find that residents are strongly protected against such invasions, and we quantify the strength of this effect by showing three types of results. First, we show that on well-mixed populations of size $N$, the invader fixation probability is exponentially small in $N$, even when the invader has a fixed relative reproductive rate $r>1$, and the same holds for all high-degree graphs. Second, we study bounded-degree graphs. We prove that on cycles, the fixation probability of an advantageous invader decreases only by a constant factor. However, we also present graphs with maximum degree 4, where the invader fixation probability is exponentially small in $N$ whenever $r\le1.9$. Thus, high degrees are sufficient for evolutionary stability, whereas with low degrees the evolutionary stability depends on specific features of the underlying spatial structure. Third, we prove general bounds for arbitrary graphs. Namely, we show that for any graph $G$ the invader fixation probability drops below the natural baseline given by the standard Moran process with oblivious individuals on a well-mixed population, both for $r\approx 1$ and for $r\ge 2$. Together, our results establish that the evolutionary dynamics of replacers is better characterized by the phrase “survival of the first” rather than the classic “survival of the fittest”.

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All authors Michal Pecho, Jakub Svoboda, Lenka Kopfová, Josef Tkadlec, Krishnendu Chatterjee

Populations and Evolution · Cross-listed from cs.DM

Vertex cover number of valued constraints is a structural parameter for efficient local search

Artem Kaznatcheev

Many local search methods for problems in artificial intelligence can be viewed as an uphill climb on a corresponding discrete fitness landscapes. Finding even local peaks in these fitness landscapes is computationally intractable in theory, but often works in practice. So what features of fitness landscapes allow for efficient local search? Since any fitness landscapes can be represented by a (hyper)graph of valued constraints, I re-frame this as a question of parameterized complexity: what structural parameter of a valued constraint graph guarantees that a strict local search will find a local peak in the corresponding fitness landscape efficiently? Given a valued constrain graph of vertex cover number k, I prove that greedy local search will find the a local fitness peak in at most $2^{2k}\cdot(n - k + 1)$ steps and random uphill local search will find a local fitness peak in an expected number of at most $2^k\cdot n(n - k)$ steps. I also show that these results are asymptotically good for strict local search because there are valued constraint graphs of vertex cover number $k$ where every ascent from some initial assignment has a length of $\frac{9}{128} \cdot 2^k \cdot (n - k)$ or greater. This suggests vertex cover number as a good structural parameter for the complexity of local search.

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Quantitative Methods

PyOMES: an open-source framework for biochemical process modelling

Ethan Errington, Tom Vinestock, Jaewook Lee et al. (+1 more)

PyOMES is a Python-based, Open-source Modelling Environment for (bio)chemical process Simulation that aims to simplify the modelling of dynamic (including steady state) processes. This is done in a generalied, modular way to facilitate modelling a broad range of biological, chemical, and biochemical systems under a single modelling framework. PyOMES has been built to be accessible to a broad range of users - ranging from those with little modelling experience, such as experimentalists and students, through to more experienced power-users. Here, an introduction is provided to the PyOMES software including a summary of the design, architecture and vision. Use cases are then provided to demonstrate applicability of PyOMES to a number of (bio)chemical process modelling scenarios. Comparisons of predictions against existing benchmark software (i.e. PHREEQC) demonstrate the robustness of the package. Finally, a summary is provided of future directions for the PyOMES package - highlighting its establishment as a unified modelling framework and its potential for community-driven improvement as future developments.

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All authors Ethan Errington, Tom Vinestock, Jaewook Lee, Miao Guo

Neurons and Cognition

Errorless Irrationality: A unified computational account of the inverse base-rate effect across predictive, observational, and unsupervised procedures

Lenard Dome and Andy J. Wills

The inverse base-rate effect is a robust bias in how people resolve ambiguity between competing categories, and the most prominent theories explain it through prediction error. Across two experiments we progressively removed the elements of the predictive-learning design that supply such error signals: first by moving to observational learning, then to an unsupervised procedure in which category labels were not presented. The effect persisted–the irrational bias is independent of supervised learning procedures. We propose a new theory, OSCAR, that integrates core computational principles of the best-validated models and operates on self-generated feedback akin to pattern completion. OSCAR extends the learning dynamics underlying the response bias to observational and unsupervised procedures. Evaluated on a large preexisting supervised dataset in addition to the two new experiments reported here, OSCAR performs competitively against alternatives, and is the first model that reproduces the pattern of individual differences seen in humans across all three procedures. The model provides an explanation of hitherto unexplained eye-tracking data, something none of the alternative accounts provide.

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Populations and Evolution

Simple evolution drives direct reciprocity to maximum payoff in social dilemmas

Martin A Nowak

Direct reciprocity is a mechanism for evolution of cooperation based on repeated interactions between the same individuals. Direct reciprocity can help natural selection to favor cooperators over defectors, but whether or not cooperation prevails depends on the details of the evolutionary dynamics. We describe simple processes of evolution that have the astonishing ability of driving direct reciprocity to maximum payoff in all social dilemmas which we study. The basic process is based on mutation and pairwise comparison. Mutation samples strategies near the boundary of the strategy space. Pairwise comparison includes a parameter for intensity of selection. For large population sizes, intermediate to high mutation rates and intermediate to strong intensities of selection, we find that the process leads to communities of strategies that reach maximum payoff in Prisoner's Dilemma, Snowdrift, Stag Hunt and Harmony games. Maximum payoff in all four games is consistently achieved if players have access to memory-2 strategies. Memory-1 strategies have the capacity to resolve all four social dilemmas, but they are usually defeated by a “Hold-trap” in Snowdrift games.

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Genomics · Cross-listed from cs.CL

EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?

Zirui Wang, Jiaqi Wang, Qinghan Wang et al. (+4 more)

Epitopes determine where antibodies bind antigens and shape downstream therapeutic properties such as functional blockade and escape resistance, making epitope understanding central to antibody drug discovery. Although large language models (LLMs) have shown strong biomedical reasoning ability, it remains unclear whether they can infer epitope information directly from antigen and antibody sequences. Existing epitope resources typically focus on isolated prediction tasks or rely on specialized structural settings, while general protein benchmarks do not evaluate epitope-centered decisions across the antibody development workflow. To address this gap, we introduce EpiBench, a closed-book, sequence-based, and automatically scorable benchmark for evaluating epitope reasoning in LLMs. EpiBench contains 1,609 curated samples grounded in structural antibody–antigen contacts, curated functional B-cell assays, and deep mutational scanning escape measurements. It covers five connected tasks: targetable region discovery, antibody-conditioned epitope identification, epitope binning, functional epitope assessment, and antibody escape assessment, with controlled sampling to reduce shortcut-based evaluation artifacts. We evaluate nine general-purpose LLMs and analyze their behavior through task-specific baselines, antigen length stratification, explicit-reasoning comparison, and failure-mode inspection. The results show that current LLMs capture partial epitope-related signals but remain limited in antibody-specific sequence grounding, long-context residue localization, and biologically grounded reasoning. Therefore, EpiBench provides a diagnostic testbed for measuring and improving sequence-aware biomedical LLMs toward reliable LLM-assisted antibody discovery.

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All authors Zirui Wang, Jiaqi Wang, Qinghan Wang, Yuzhi Xu, Gang Du, Tingjun Hou, Odin Zhang

Neurons and Cognition

Convergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks

Patrick Krauss, Achim Schilling, Andreas Maier et al. (+2 more)

Deep Belief Networks (DBNs) learn hierarchical generative models without class supervision. Here, we ask whether this purely unsupervised process nevertheless organizes internal representations according to the unknown data classes. We analyze successive layers of DBNs trained on MNIST, Fashion-MNIST, and KMNIST using the Generalized Discrimination Value (GDV), supervised probes applied only after training, a reconstruction-based measure of abstraction distance, effective dimensionality, and free sample generation. Remarkably, class-specific clustering generally increases with depth across datasets and network widths, although no label information is available during DBN training. Control experiments show that this effect depends on the learned feature structure and cannot be explained by random transformations, weight marginals, dimensionality reduction, or sigmoid saturation. The first hidden layers also frequently make class identity more accessible to linear and nonlinear probes. With greater depth, representations become increasingly compact and prototype-like as neurons acquire correlated feature directions. At the same time, GDV and probe accuracy reveal complementary aspects of class structure: improved average clustering can coexist with reduced accessibility for a few difficult class pairs. These findings demonstrate that layer-wise generative learning can spontaneously uncover and progressively amplify class-related structure in unlabeled data.

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All authors Patrick Krauss, Achim Schilling, Andreas Maier, Thomas Kinfe, Claus Metzner

Neurons and Cognition

Convergent Evolution in Algorithmic Space

Patrick Krauss, Achim Schilling, Andreas Maier et al. (+4 more)

In evolutionary biology, unrelated organisms can independently evolve similar structures when exposed to similar functional demands. Here we ask whether an analogous form of convergent evolution occurs during neural network training: do networks with different random initializations develop similar internal weight structures when trained on the same task? This question is technically nontrivial because hidden neurons can be arbitrarily permuted without changing the represented function, making direct matrix comparisons misleading. We introduce a matching-based framework for comparing multilayer perceptrons in structural weight space. Hidden neurons are first coarsely aligned using permutation-invariant features and then refined by iterative Hungarian matching. After alignment, networks are compared with structural distance metrics designed to emphasize task-relevant weight patterns. Applying this approach to ensembles of small MLPs trained on MNIST, Fashion-MNIST, and KMNIST, we find that networks trained on the same task remain closer to one another than to networks trained on different tasks. Thus, task-specific training appears to guide initially random networks toward distinct regions, or attractors, in structural network space. The earliest phase of training reveals an additional and unexpected phenomenon. Classification accuracy rises rapidly before the matched structural distances show strong task-specific separation, and before the global weight distribution visibly changes. Nevertheless, individual weight entries already begin to drift in a coordinated manner. This suggests that early learning may first operate through subtle, distributed adjustments that strongly affect function while leaving coarse network morphology almost unchanged. We treat this early morphogenesis as a first glimpse of a richer dynamical process that will be investigated in future work.

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All authors Patrick Krauss, Achim Schilling, Andreas Maier, Thomas Kinfe, Henri Stübner, Niklas Römmelt, Claus Metzner

Quantitative Methods · Cross-listed from cs.LG

THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction

Pui Chung Siu, Claudia Cabrera, Mani Mudaliar et al. (+1 more)

Inadequate target–disease linkage accounts for 40–50% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients. What a programme can be judged on is the evidence that supported its linkage when it entered the clinic. No existing biomedical knowledge graph allows that evidence profile to be assembled as of a past date. We present the Temporal Heterogeneous Biomedical Knowledge Graph (THBKG), which describes and predicts therapeutic target–disease links through time: 110,396 entities and 11.1M edges across nineteen relation types, each edge carrying the year its evidence changed, so a pair's profile can be recovered as it stood when its own decision fell due. On this graph we define a decision-aligned benchmark that predicts, for a target–disease pair entering Phase~II, whether it advances to Phase~III on evidence datable before that decision. Graph propagation over the THBKG outranks every direct-evidence reference scored under the same decision-aligned protocol, reaching a relative success of 4.3–4.5 at the top ten pairs per therapeutic area. The gain concentrates on the 72.8% of pairs with no direct target–disease evidence at their decision point, where a direct-edge model has nothing to read: the encoders still rank five- to sixfold above chance, recovering the signal by propagating over the intervening biology. Adapting a path-based explainer to the decision-time subgraph decomposes each prediction into the evidence landscape behind the hypothesis for explainable prediction. We release the THBKG as a continually updated substrate for studying therapeutic target hypotheses by retrospective validation.

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All authors Pui Chung Siu, Claudia Cabrera, Mani Mudaliar, Arkaitz Zubiaga

Neurons and Cognition

Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease

Junjie Yu, Jianyu Zhang, Zian Pei et al. (+5 more)

Objective: EEG signals fluctuate continuously even within a fixed cognitive state, but an important question is whether the brain still reuses similar activity patterns to represent information over time. Methods: To address this, we model EEG as distributions of windowed activity patterns and quantify their temporal stability using Wasserstein distance, while intrinsic dimensionality captures representational complexity. Results: Across multi-task, lifespan, and clinical EEG datasets, we find that neural representations show constrained, condition-specific stability rather than unconstrained drift. Higher intrinsic dimensionality is consistently associated with lower stability, suggesting that richer representational spaces are less reproducible over time. Both measures exhibit reproducible spatial organization, with posterior regions showing higher dimensionality and lower stability than frontal regions. Healthy aging is characterized by increased dimensionality and reduced stability, whereas mild cognitive impairment and Alzheimer's disease show a joint collapse of both. Conclusions: These findings provide a distribution-level framework for understanding neural stability across cognition, aging, and disease. Significance: This framework offers a principled approach to quantifying neural representational stability, with potential utility as a sensitive biomarker for tracking cognitive aging and neurodegeneration in clinical settings.

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All authors Junjie Yu, Jianyu Zhang, Zian Pei, Xue Shi, Yumei Liu, Xin Jiang, Quanying Liu, Yi Guo