Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Quantitative Biology Quantitative Methods

q-bio.QM Watch Quantitative Methods

Tuesday 25 August

Quantitative Methods · Cross-listed from cs.LG

Evaluating Deep Multivariate Imputation Models on Wearable Device Data

Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges et al. (+5 more)

Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods such as BRITS and SAITS have seen limited evaluation on multimodal physiological data under realistic missingness, and existing benchmarks use random-point holdout protocols that incorrectly assume missingness is independent across features and time. Using data from a person with epilepsy recorded on a Garmin smartwatch, we develop an evaluation protocol that mines contiguous missing-run templates from training data, stratifies them by per-feature gap-length quantiles, and injects them as block masks with preserved co-missingness structure. A matched training protocol exposing models to the same missingness distribution reduces BRITS's severe-gap MAE by 43%, demonstrating the potential benefit of the proposed evaluation and training protocol within this single-participant dataset. We further extend BRITS with time-of-day encoding and a circadian harmonic channel. No single model dominates: linear interpolation is optimal for slow-moving features over short gaps; extended BRITS achieves lower MAE on dynamic cardiac features in moderate and severe gaps; and SAITS better preserves the ground-truth distribution by Jensen-Shannon distance despite higher MAE. Ultimately, model rankings strongly depend on evaluation designs. By exposing how traditional evaluation methods obscure true model capabilities, our transferable protocol establishes critical steps towards developing better imputation strategies for future multi-sensor wearable datasets.
Read the full abstract

All authors Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges, Peter Kissack, Yasser Qureshi, Amberly Brigden, Jeff Clark, Nawid Keshtmand

Quantitative Methods · Cross-listed from physics.med-ph

Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis

Cyrus Mexon Evrard Djindot, Faliang Liu, Sylvain Laborde et al. (+6 more)

Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincaré indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.
Read the full abstract

All authors Cyrus Mexon Evrard Djindot, Faliang Liu, Sylvain Laborde, Yinjia Zhang, Jessie Chen, Ming Li, Congrong Wang, Weixiong Rao, Qinpei Zhao

Monday 24 August

Quantitative Methods

Beyond Tokens: Probing Higher-Order Epistasis in Learned Protein Representations

Maryam Rahimimovassagh, Ivan Garibay and Niloofar Yousefi

Protein fitness landscapes contain nonlinear interactions in which mutation effects depend on other residues. We introduce ORBIT, an Order-Resolved Benchmarking of Interaction Transformations framework that separates interaction presence, representation accessibility, and functional recovery. ORBIT first validates Walsh-based diagnostics on synthetic landscapes with known interaction order, then analyzes the experimentally measured GB1 fitness landscape under the FLIP 2-vs-rest setting. We compare ridge regression, a standard MLP, independent tokens, nonlinear independent tokens, and Residual Interaction Tokenization (RIT). Across 20 paired training seeds, the primary two-hidden-layer comparison found no significant architecture differences in FLIP test R^2, third- or fourth-order functional recovery, or final-layer third- or fourth-order accessibility. However, RIT significantly increased pairwise accessibility at the token stage relative to both independent-token controls (Delta A_tok,2 = 0.2468, d_z = 1.67, Holm-adjusted p = 1.14 x 10^-5), without a detectable downstream higher-order advantage. A pre-specified depth/capacity analysis showed that deeper MLPs improved FLIP prediction, third-order functional recovery, and final-layer third-order accessibility; fourth-order accessibility also improved relative to the shallow MLP but remained below zero in absolute held-out R^2. ORBIT therefore reveals representation-level changes hidden by conventional prediction metrics and distinguishes early interaction-aware encoding from higher-order structure constructed by downstream nonlinear capacity.
Read the full abstract

Quantitative Methods

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

Abdullah Shouaib, John Zapanta, Sean P. Davern et al. (+5 more)

A holistic approach to chemical mixtures is reshaping risk assessment emphasizing mixture testing over single compounds eliminating animal testing and advancing modeling methods. Most computational models still focus on individual chemicals and conventional mixture models like concentration addition and independent action are limited they struggle with multiple Modes of Action and often miss synergistic or antagonistic effects. Regulatory agencies need faster more efficient models that go beyond these constraints. Finch addresses these challenges with a novel workflow. It integrates molecular descriptor based frameworks and deep learning embeddings in multi task quantitative structure activity relationship models for improved chemical exposure prediction. DL embeddings preserve information from diverse inputs molecular descriptors physicochemical properties and large language model embeddings from SMILES by distilling key features into a latent space enhancing machine learning predictions. Finch multi task learning optimizes multiple loss functions simultaneously leveraging all available data to learn generalized representations. This enables effective modeling of complex ingredient interactions within mixtures offering a significant advancement for regulatory safety assessment.
Read the full abstract

All authors Abdullah Shouaib, John Zapanta, Sean P. Davern, Samuel Dixon, Zachary R. Stromberg, Becky Hess, Sydney Schwartz, C Mark Maupin

Sunday 23 August

Quantitative Methods

Spatial-Molecular Information Analysis Based on Histopathological Geometry: KL-Divergence Decomposition of Cell Distribution and Location-Dependent Expression States in PD-1 Immunohistochemistry

Tatsuaki Tsuruyama

Spatial biology enables molecular expression to be analyzed together with the location of cells in tissue. However, it is often difficult to distinguish whether cells preferentially accumulate near a histopathological boundary from whether their molecular expression states vary with that location. To establish informational scientific framework, we define boundary distance as R and a binary molecular expression state as G, and compare the observed joint distribution P\left(R,G\right) with Q\left(R\right)P\left(G\right), where Q\left(R\right) is a null distribution determined by tissue geometry. The resulting Kullback-Leibler divergence decomposes into D_{R}, which quantifies deviation of the spatial distribution of cells from the geometric null, and I\left(R;G\right), which measures how strongly molecular expression state depends on boundary distance. Idealized simulations and semi-synthetic validation using geometry reconstructed from a PD-1 IHC image confirmed that these two effects can be generated separately and recovered by the corresponding terms. Plug-in estimators showed positive finite-sample bias, supporting permutation-based inference. We then applied the framework to five publicly available PD-1 lung cancer IHC images from the Human Protein Atlas. In pooled analysis, I\left(R;G\right)=0.0302 nats and was significant in a stratified within-image label-permutation test (p=0.00040). This framework provides a quantitative way to distinguish where cells are located from how their molecular expression states vary with tissue location. Larger cohorts, independent datasets, additional cancer types, and other molecular markers will be required to establish generalizability and clinical utility.
Read the full abstract

Quantitative Methods · Cross-listed from stat.ME

A general-purpose sensitivity method for multiple simultaneous parameter perturbations in stochastic reaction networks

David F. Anderson and Jingyi Ma

Stochastic reaction networks are continuous-time Markov chain models for interacting populations, with applications in biochemistry, epidemiology, ecology, and related areas. We study finite-difference sensitivity estimation when a single estimator requires several nearby parameterized paths. Existing variance-reducing couplings are typically pairwise, so that repeated use is either inefficient or requires application-specific choices in multi-path settings. We introduce the multi-path stacked coupling (MSC), a space-time Poisson construction that jointly generates any finite collection of parameterized paths. Each pairwise marginal of MSC has the same law as the corresponding split coupling pair, allowing existing variance bounds to transfer directly; in finite-state settings, we also obtain first-order expansions for the mean and second moment of finite-difference numerators. We apply MSC in three settings of practical importance: estimating many first derivatives simultaneously, estimating a single first derivative using a wider finite-difference stencil, and estimating higher-order derivatives. Numerical experiments on a processive phosphorylation network demonstrate strong performance in each of the three application areas considered, consistent with the theoretical advantages of MSC: across all three applications, MSC achieves the smallest root mean square error (RMSE) among the methods considered over the tested computational budgets.
Read the full abstract

Quantitative Methods · Cross-listed from physics.chem-ph

From Steady-State to Ultrafast: Resonance Raman Approaches for Biological Samples

Juan J. Romero, Bruno Robert and Manuel J. Llansola-Portoles

Vibrational spectroscopy reports on molecular structure with chemical-bond specificity, but in biological systems the vibrational signals of a target chromophore are typically buried under contributions from the surrounding matrix. Resonance Raman (RR) spectroscopy addresses this problem by matching the excitation wavelength to an electronic transition of the chromophore of interest, which increases Raman cross sections by up to six orders of magnitude and restricts the enhanced modes to those coupled to the resonant electronic state. This chapter introduces the physical basis of resonance enhancement and shows how RR isolates chromophore-specific vibrational markers in complex biological systems. We then extend the same principle into the time domain with femtosecond stimulated resonance Raman spectroscopy (FSRRS). In FSRRS, the Raman pump is tuned across the visible range and can be placed in resonance with the transient absorption of a chosen excited-state species, which makes the Raman pump wavelength an additional experimental variable. Sampling this variable across the excited-state absorption manifold provides a selection criterion that separates coexisting transient species sharing a common vibrational window.
Read the full abstract

Saturday 22 August

Quantitative Methods · Cross-listed from eess.IV

Pretreatment DCE-MRI Resolves Response Quality Within Pathologic Endpoints in Neoadjuvant Breast Cancer

Dattatreya Kantha and Murray H. Loew

Pathologic complete response (pCR) is a strong neoadjuvant endpoint, yet 5-15% of complete responders recur and clinical/genomic variables do not reliably identify them. We tested whether pretreatment dynamic contrast-enhanced MRI entropy - intratumoral enhancement heterogeneity - resolves response quality hidden within pCR and residual cancer burden (RCB). Across four cohorts (1,200 patients), a prespecified entropy threshold defined favorable and adverse structural states. Crossing structure with pathology yielded a four-tier framework spanning 4.1-fold recurrence in I-SPY1 and 7.7-fold at response extremes. In I-SPY2, 55 of 219 complete responders (25.1%) were structurally adverse, pretreatment. In an external HER2-positive responder synthesis (I-SPY1 pathology-confirmed pCR plus UCSF best-response proxy; n = 33, 10 events), adverse structure was associated with higher recurrence risk (HR = 2.87, 95% CI 1.38-5.96) capturing 7 of 10 recurrences, enriching rather than determining risk. In a HER2-positive RCB-0 subset, recurrence was 12.5% with favorable and 80.0% with adverse structure; Firth Cox regression preserved the association (HR = 8.13, 95% CI 1.71-49.21; n = 21, 6 events). In Duke (n = 908; 76 events), favorable structure remained independently associated with lower distant-recurrence risk (adjusted HR = 0.61, 95% CI 0.41-0.91). RNA linked favorable structure to a directionally reproduced immune-architecture program among non-overlapping patients within ISPY2; EMT-pathway enrichment was favorable-side, while the adverse tier contained a broadly immune-depleted substate. Yet full-cohort RNA models weakly discriminated structural state and did not recover continuous entropy. Pretreatment MRI therefore does not replace pCR or RCB; it reveals response-quality differences that these endpoints compress and identifies a recurrence-enriched group for prospective validation.
Read the full abstract

Quantitative Methods · Cross-listed from cs.LG

ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy

Nhan D. Nguyen and Bao Pham

Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0{\deg} on 100 held-out test structures and 2.5{\deg} on experimental particles, matching dedicated estimators within 0.16 Å in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.
Read the full abstract

Friday 21 August

Quantitative Methods

A nonlinear dynamics approach to assess amplitude-frequency coupling in non-invasive human electrogastrography recordings

Akshita A. Rao and Todd P. Coleman

Conventional electrogastrography (EGG) analyses summarize gastric slow-wave activity using dominant frequency, power, or phase-based metrics, without explicitly modeling amplitude-dependent frequency dynamics. We developed a statistical nonlinear-dynamics framework that represents the gastric-band analytic EGG signal as a stochastic Stuart-Landau oscillator and estimate local effective phase-shear, $\beta_{\mathrm{eff}}$, a normalized measure of amplitude-frequency coupling. In overnight recordings from 60 healthy participants, effective phase-shear was consistently negative, generalized to held-out halves of each recording, and was abolished by amplitude surrogates that disrupted amplitude-phase alignment while preserving amplitude-series structure. We then applied the measure to fasted and fed recordings from 16 healthy participants and 24 participants with gastric dysfunction. Phase-shear magnitude differed between groups and classified dysfunction comparable to the strongest multielectrode phase-based traveling-wave measures. These findings establish amplitude-frequency coupling as a dynamical feature of gastric-band EGG that complements conventional spectral and spatial phase metrics, while motivating future multichannel observation models to determine its relationship to slow-wave propagation and gastric function.
Read the full abstract

Quantitative Methods

Explainable AI-based Decision Support for Nocturnal Hypoglycemia Prevention in Type 1 Diabetes

Valentina Roquemen-Echeverri, Peter G. Jacobs, Leah M. Wilson et al. (+8 more)

Purpose: Nocturnal hypoglycemia (NH) remains a challenge for individuals with type 1 diabetes (T1D), particularly those who are physically active or on multiple daily injections (MDI). We leveraged an explainable evidential neural network model that forecasts minimum overnight glucose to identify NH risk factors and generate recommendations for NH prevention. Methods: We analyzed the impact of glucose and physical activity (PA) factors on predicted NH probability using SHapley Additive exPlanations (SHAP). Data were from 20 adults with T1D on MDI (10 females; mean age 39 years; HbA1c 7%) who participated in a cross-over study (NCT05967260). Results: A total of 502 nights were analyzed. Bedtime glucose was the strongest predictor of NH. Other relevant factors included recent exposure to high or low glucose, glucose variability before bedtime, and timing of PA. Some associations appeared physiologically counterintuitive, possibly reflecting behavioral adjustments. Based on the identified risk factors and their SHAP values, we determined key decision points and developed recommendations that may help prevent NH, such as consuming a bedtime snack or discussing potential adjustments to insulin therapy with a healthcare provider. Conclusion: Identifying predictors of NH offers insights for managing NH risk in clinical decision support.
Read the full abstract

All authors Valentina Roquemen-Echeverri, Peter G. Jacobs, Leah M. Wilson, Joseph Pinsonault, Deborah Branigan, Jae Eom, Daisy Chen, Hantao Ling, Diana Aby-Daniel, Kyle Chen, Clara Mosquera-Lopez

Quantitative Methods

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

Yoshitaka Inoue, Minoh Jeong, Alfred Hero et al. (+2 more)

Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.
Read the full abstract

All authors Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna

Thursday 20 August

Quantitative Methods · Cross-listed from stat.ME

GENIE: Generative Neural Inference for Epidemics

Laura M. Guzmán-Rincón, George R.E. Bradley, Joel Kandiah et al. (+5 more)

The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to use in real-time. Amortized simulation-based inference promises to overcome this difficulty by exploiting the power of machine learning (ML) to perform approximate forecasting at near-real-time using arbitrarily complex models of epidemics. In this work we introduce Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens. GENIE is designed to reflect two key characteristics of outbreaks: (i) shared biological mechanisms across locations and (ii) location-specific characteristics affecting transmission dynamics. This results in the model architecture having two modules: (i) a Local Infection Encoder - which learns to represent disease dynamics shared across all locations and (ii) a Local Profile Encoder - which learns location-specific representations. Using simulations from a high-resolution spatio-temporal ABM, GENIE is trained to generate samples from an approximate posterior predictive distribution of future epidemic trajectories. Benchmarked against established statistical and ML models, GENIE demonstrates superior performance across a range of measures including the timing and magnitude of peak hospitalisations.
Read the full abstract

All authors Laura M. Guzmán-Rincón, George R.E. Bradley, Joel Kandiah, Kyriakos Flouris, Pietro Liò, Paul J. Birrell, Alexander E. Zarebski, Daniela De Angelis

Quantitative Methods

PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models

Sebastian Persson, Branwen Snelling, Maren Philipps et al. (+5 more)

Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications ranging from learning unknown biological processes to integrating auxiliary data modalities into dynamic modelling. To enable reproducible and efficient SciML training, we introduce PEtab SciML, an interoperable data format for specifying parameter estimation problems in which mechanistic and ML model parameters are jointly estimated from time series data. PEtab SciML supports several ML ODE hybridization patterns in realistic problem setups. It is accompanied by a reference Python library and downstream modelling support in Python/JAX and Julia, provided by AMICI and PEtab$.$jl, respectively, and a collection of real data benchmarks. Availability and implementation: PEtab SciML is available on GitHub (https://github.com/PEtab-dev/petab_sciml). The reference Python package is installable from PyPI and is continuously tested and supported on Linux, macOS, and Windows.
Read the full abstract

All authors Sebastian Persson, Branwen Snelling, Maren Philipps, Daniel Weindl, Marija Cvijovic, Jan Hasenauer, Dilan Pathirana, Fabian Fröhlich

Quantitative Methods · Cross-listed from cs.CV

Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging

Sidi Mohamed Sid'El Moctar, Nicolas Vitry and Hélène Bouvrais

Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear segmentation have been proposed, they are often tailored to specific anatomies or modalities, limiting generalization across clinical settings and leaving room for improvement. Recent generative models have shown the benefits of iterative prediction for structured segmentation tasks, yet diffusion-based methods suffer from computationally expensive sampling, hindering their use on high-resolution 3D volumes. We present 3D-CurvSegFlow, a flow matching-based model for 3D curvilinear structure segmentation. The model learns a continuous transformation from a simple source distribution to the target vascular representation, enabling progressive refinement of complex curvilinear geometries with efficient inference. We evaluate our method on Three public challenging datasets covering distinct anatomies and modalities: portal vein, cerebral vessel, and coronary arteries. Using a common architecture and training strategy across all tasks, our method outperforms general-purpose and vessel-specific approaches, with strong preservation of thin branches and vascular continuity. This work not only advances the state-of-the-art in 3D curvilinear segmentation but also opens new avenues for efficient, generalizable, and clinically applicable methods in medical image analysis.
Read the full abstract

Quantitative Methods · Cross-listed from q-bio.PE

A stochastic dose-response framework for environmentally persistent pathogens

Mahmudul Bari Hridoy, Arik Hartmann, Kate E. Langwig et al. (+2 more)

Infectious diseases caused by environmentally persistent pathogens can strongly affect host populations as transmission occurs not only through direct host-host contact but also via indirect exposure to contaminated environments. While in some systems environmental reservoirs help sustain exposure even when infected host numbers are low, reliance on environmental transmission pathway may also increase pathogen extinction risk. Infection risk may depend on both pathogen dose and timing of exposure. Thus, understanding how dose-response, stochasticity, and seasonal changes in host susceptibility or contact rates shape pathogen invasion and persistence remains an important challenge for environmentally transmitted disease systems. To address this, we develop a stochastic dose-response framework that integrates host infection dynamics with an explicit environmental pathogen reservoir. Transmission occurs through both direct contact with infectious hosts and indirect environmental exposure, with infection probability governed by dose-response functions. We focus on stochastic continuous-time Markov chain formulation and use branching process approximation to estimate disease extinction probabilities when infected hosts or environmental pathogen loads are low. We extend the framework to include seasonality in host susceptibility, environmental contact, and host-host contact. As a case study, we apply the model to snake fungal disease. Numerical simulations show that dose-response influences epidemic takeoff and infection levels, while seasonality creates windows of high and low extinction risk. These extinction risks depend strongly on the route and timing of introduction within the seasonal cycle. These results, coupled with global sensitivity analysis, illustrate how stochasticity, nonlinear dose-response, and seasonal timing shape outbreak dynamics for environmentally persistent pathogens.
Read the full abstract

All authors Mahmudul Bari Hridoy, Arik Hartmann, Kate E. Langwig, Joseph R. Hoyt, Lauren M. Childs

Wednesday 19 August

Quantitative Methods · Cross-listed from eess.IV

SatoyamaCT: A Multi-Axis Night-IR Camera-Trap Benchmark for Monitoring Crop-Damaging Wildlife in Japanese Agroforestry

Keito Inoshita, Kohei Hisayama, Haruto Sugeno et al. (+1 more)

Crop and forest damage from sika deer, wild boar, and Japanese macaque is a serious economic problem in Japanese satoyama, where farmland and forest intermingle. Camera traps enable scalable monitoring, yet existing benchmarks evaluate recognition in-distribution, rarely prioritize night infrared imagery, and do not jointly address regional domain shift, novel-species detection, and uncertainty-based triage on a single dataset. We introduce the Satoyama Camera Trap Dataset (SatoyamaCT), 12,642 expert-verified crops from night-IR-dominant camera traps across three satoyama regions. An iterative annotation protocol combining BioCLIP embeddings with confidence-ordered confirmation reduced expert effort while all labels were verified by domain ecologists; species-level inter-annotator agreement reached Cohen's kappa = 0.900. A multi-axis protocol jointly evaluates domain generalization across region, camera placement, and illumination; open-set novel-species detection; and selective prediction, all under capture-event-based leakage control. Difficulty separates into distinct failure modes: a data-inherent regional gap of 16 to 27 percentage points persists in Wakayama across four backbones and domain-generalization methods including CORAL, DANN, and GroupDRO, and open-set detection reaches AUROC 0.93 to 0.96 overall yet degrades jointly with classification in Wakayama. Selective prediction recovers Wakayama accuracy from 0.593 to 0.815 at 50% coverage, supporting an uncertainty-aware triage workflow for practical pest monitoring in agroforestry.
Read the full abstract

All authors Keito Inoshita, Kohei Hisayama, Haruto Sugeno, Kota Nojiri

Quantitative Methods · Cross-listed from cs.LG

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Yingying Zhang, Kun Zhao, Guodong Liu et al. (+10 more)

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cross-sectional imaging. In this work, we propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that continuously estimates disease severity from longitudinal diffusion tensor imaging (DTI). Specifically, DCP models disease severity as a low-dimensional probabilistic latent variable by jointly integrating longitudinal observations with weak clinical supervision, from which the proposed Disease Continuum Score (DCS) is derived to quantify an individual's position along the Alzheimer's disease continuum together with its associated uncertainty. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods. More importantly, comprehensive validation analyses show that DCS accurately characterizes disease severity, exhibits strong clinical relevance, preserves longitudinal disease evolution, and predicts future disease conversion. These results suggest that DCS provides a quantitative imaging-derived representation for continuous assessment of Alzheimer's disease progression beyond conventional diagnostic labels and clinical scores.
Read the full abstract

All authors Yingying Zhang, Kun Zhao, Guodong Liu, Qi Huang, Pengfei Gu, Dongchul Kim, Erik Enriquez, Alex D. Leow, Paul M. Thompson, Heng Huang, Hongchang Gao, Liang Zhan, Haoteng Tang

Quantitative Methods · Cross-listed from cs.LG

Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection

Hamed Javidi, Alex Zajichek, Hakan Doga et al. (+3 more)

Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges. Blood-based cell-free DNA (cfDNA) biomarkers offer a complementary approach, although early detection remains difficult because of lung cancer heterogeneity and high-dimensional, nonlinear molecular signals. We evaluated quantum-classical hybrid machine learning for lung cancer detection using DNA fragmentomics and DNA methylation. After feature selection, models were trained using 20- and 40-feature subsets. Features were encoded into quantum Hilbert space using angle and dense-angle feature maps with multiple entanglement strategies. Fidelity-based quantum kernels were computed with exact statevector simulation and integrated with precomputed-kernel SVM and kernel-PCA logistic regression and compared with an SVM model trained on the original features. This framework enabled systematic evaluation of how encoding and entanglement design affect classification. Across repeated held-out evaluations, quantum-kernel models achieved competitive performance on both datasets. For fragmentomics, several 20-feature configurations improved AUC relative to a classical SVM baseline, suggesting effective capture of nonlinear cfDNA fragmentation structure. For methylation, the classical SVM achieved the highest AUC, although selected quantum models remained competitive and improved specificity in some cases. Increasing features from 20 to 40 did not consistently improve performance and often increased variability. Overall, these results support quantum kernel methods as a promising approach for cfDNA-based lung cancer detection.
Read the full abstract

All authors Hamed Javidi, Alex Zajichek, Hakan Doga, Laxmi Parida, Filippo Utro, Peter J. Mazzone

Quantitative Methods · Cross-listed from cs.LG

Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference

Blazej Banaszewski and Andrew W. Fitzgibbon

Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research into molecular foundation models (MFMs), which aim to encode general-purpose chemical knowledge into molecular representations that generalize well in data-constrained scenarios. This paper presents Monroe, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding decorrelation; improved multi-task learning; and the use of a prior-data-fitted model (TabPFN) for downstream in-context prediction. Our evaluations use a principled pairwise comparison framework that measures statistically significant performance differences. Across established Polaris benchmarks, Monroe matches or exceeds existing MFMs, while on activity cliff benchmarks, designed to assess utility for molecular discovery, it achieves significant improvements over prior methods. Finally, ablation and transfer experiments show that PFN-based downstream predictors also substantially improve two leading existing models, MiniMol and CheMeleon, yielding new state-of-the-art variants we call MiniMol_PFN and CheMeleon_PFN, suggesting that our downstream adaptation strategy generalizes beyond Monroe. Source code is at github.com/blazejba/monroe.
Read the full abstract

Quantitative Methods

Polarization controlled second harmonic generation imaging of stretched collagen fibrils reveals collagen deformation pathway in situ

MacAulay Harvey, Konstantin Roeder, Richard Cisek et al. (+2 more)

The tensile properties of single collagen fibrils, the building block of load-bearing tissues, have been studied extensively by nanomechanical techniques and molecular dynamics simulation. However, the deformation pathway of collagen molecules within fibrils has not yet been observed experimentally. In addition, the role played by divalent and trivalent enzymatic crosslinks in modulating this deformation pathway is poorly understood. Here we used polarization controlled second harmonic generation (SHG) imaging combined with atomic force microscopy (AFM) to characterize the molecular state of collagen triple helices within stretched single collagen fibrils. The fibrils were extracted from a pair of bovine leg tendons from the same animal in order to compare fibrils with a high amount of immature divalent crosslinks to fibrils with a high amount of mature trivalent crosslinks. By selecting fibrils with a large SHG intensity gradient along their length and then imaging the same fibrils by AFM we were able to link the observed intensity gradient with a gradient in D-band strain and a gradient in molecular strain as estimated from the SHG anisotropy parameter rho. In contrast to previous studies at the tendon scale, we observed that the SHG molecular strain is always larger than the D-band strain for all fibrils with this difference being largest for fibrils rich in divalent crosslinks. By analyzing the behavior of the relative density of SHG emitters as a function of molecular strain, we observe a two-state transition from an SHG producing to a non SHG producing state with a free energy barrier between 6 and 10 kBT that we propose corresponds to the local untwisting of the collagen triple helix superhelical twist which likely preceded bond rupture and loss of the SHG signal. We also show that trivalent crosslinks tend to delay the transition onset compared to divalent crosslinks.
Read the full abstract

All authors MacAulay Harvey, Konstantin Roeder, Richard Cisek, Danielle Tokarz, Laurent Kreplak

Quantitative Methods · Cross-listed from cs.LG

Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework

Hongtao Li, Jia Wei, Guoyao Li et al. (+6 more)

Background and Objective: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world clinical settings due to variable lead configurations, cross-dataset domain shifts, and pervasive physiological and technical artifacts. So we develop a robust and generalizable deep learning model for accurate AF detection.\\ Methods: We propose the Dual-Codebook Graph Collaborative Network (DCGCNet), a novel end-to-end vector-quantized variational autoencoder that jointly performs AF classification and ECG reconstruction. DCGCNet introduces two key components: (1) a Local-Global Contrastive Module for learning noise-invariant representations, and (2) an Adaptive Codebook Vector Quantizer that dynamically refines codebook prototypes to better align with input data distributions, thereby preventing codebook collapse and enhancing generalization.\\ Results: DCGCNet achieves state-of-the-art performance in standard intra-dataset 12-lead evaluation and demonstrates exceptional cross-dataset generalization across seven diverse settings, consistently attaining AUC > 0.98 in all cases. Furthermore, it maintains high diagnostic accuracy under realistic noisy conditions, including baseline wander, powerline interference, and EMG artifacts.\\ Conclusions: DCGCNet establishes a new benchmark for robust, generalizable, and noise-resilient AF detection, showing strong potential for deployment in real-world clinical environments.
Read the full abstract

All authors Hongtao Li, Jia Wei, Guoyao Li, Yuchen Lei, Guangnian Ma, Jia Xiao, Yuanjun Lai, Shuzhen Lv, Xueqiang Ouyang

Tuesday 18 August

Quantitative Methods

Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utility

Marvellous O. Ajala, Zainab Ashimiyu-Abdusalam and Comfort Adesina

We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, and conduct a systematic evaluation of five open-source LLMs; Gemma-2 2B/9B, TxGemma-2B/9B, and LlaSMol-Mistral-7B, on a rigorous out-of-distribution data split. Performance was benchmarked against classical ML models (Random Forest, XGBoost) and frontier proprietary models (Gemini 2.5, OpenAI o3) under few-shot conditions. Fine-tuned LLMs substantially outperformed all baselines: TxGemma-9B achieved the highest ROC-AUC ($0.731 \pm 0.005$) and LlaSMol-Mistral-7B the best enrichment factor (EF@1% $\approx$ 4.99). Domain-specific fine-tuning proved categorically indispensable with TxGemma-9B collapsing from ROC-AUC 0.731 to 0.499, under its best few-shot condition, and neither Gemini 2.5 (ROC-AUC $\approx$ 0.53) nor o3 (ROC-AUC $\approx$ 0.59) achieved reliable discrimination without fine-tuning. Biomedical pretraining conferred a measurable advantage at equivalent scale, while chemistry-aware pretraining yielded superior prospective enrichment. Fine-tuned open-source LLMs represent a compelling, resource-efficient paradigm for antimalarial VS, outperforming both classical pipelines and proprietary reasoning models under structurally challenging conditions.
Read the full abstract

Quantitative Methods · Cross-listed from cs.AI

GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks

Arefin Amin, Labiba Faiza Karim and M. Monir Uddin

Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, including Sequence Alignment, Phylogenetic Analysis, and Mutation Frequency Statistics. These approaches use pairwise codon or nucleotide distance matrices to analyze gene sequences, treating them as linear strings rather than capturing their complex contextual interdependencies. We proposed GenEx, a pipeline that converts raw gene sequences into codon co-occurrence graphs and extracts more than 25 graph features. Our two most prominent techniques for graph generation and feature extraction are MSCG (Multi-Scale Codon Co-occurrence Graph) and LAPCG (Linear-time Adjacency PMI Codon Graph). Using these algorithms, we treated codon sequences as structured symbolic vocabularies interpretable to codon co-occurrence graph analysis, a representational paradigm borrowed from computational linguistics. Another major contribution includes implementing a spectral graph feature extraction using Singular Value Decomposition (SVD), using the squared singular value ($\sigma^2$) instead of the traditionally used eigenvalue, which helped us to amplify the separation between dominant and subdominant spectral components, thereby enhancing inter-class separability in downstream classification. And to further demonstrate that our method works, we trained 23 benchmarked ML models against the latest SARS-CoV-2 variants, achieving remarkable results in detecting all SARS-CoV-2 variants.
Read the full abstract

Quantitative Methods

A Leakage-Proof Benchmark and Conformal Selective Triage for Electrohysterogram-Based Preterm Birth Prediction

Sunday A. Adetunji and Rhoda O. Oyewusi

Preterm birth remains a major cause of neonatal morbidity and mortality worldwide. Electrohysterography (EHG), a noninvasive measure of uterine myoelectrical activity, has been studied for preterm-birth prediction, but performance estimates may be biased when segments from the same maternal record are split across training and validation data. We formalized the distinction between segment-level and patient-independent record-grouped validation, established a patient-independent benchmark on the Term-Preterm Electrohysterogram Database, and evaluated class-conditional conformal selective prediction. All 300 records (38 preterm) were analyzed across three prespecified regimes. A 92-feature elastic-net logistic model was evaluated with record-grouped nested cross-validation; preprocessing, model fitting, Platt calibration, and conformal estimation were confined to training data, and performance was calculated from strictly out-of-fold record-level predictions with 1,000 bootstrap resamples. Under patient-independent evaluation, AUROC was 0.493 (95% CI 0.467-0.520), AUPRC 0.122 (0.095-0.152), and Brier score 0.115 (0.094-0.137). AUROC was 0.514 at or before 26 weeks and 0.469 thereafter. At miscoverage alpha=0.10, marginal coverage was 0.897, abstention 72.7%, and singleton-prediction accuracy 0.624. These findings establish a record-separated reference benchmark for EHG prediction and a broader validation principle for segmented physiological data: the unit of resampling should correspond to the unit at which predictive performance is intended to generalize. Conformal prediction further quantifies when the available information supports a singleton classification and when uncertainty warrants deferral.
Read the full abstract

Quantitative Methods · Cross-listed from eess.SP

Empirical mode decomposition and interpretable machine learning for preterm birth classification from electrohysterography

Umesha Tilakarathna, Senith Jayakody, Kalana Jayasooriya et al. (+4 more)

Preterm birth (PTB) remains a major global health problem, and reliable non-invasive risk assessment remains difficult. Electrohysterography (EHG) records uterine electrical activity from the maternal abdomen and may support PTB assessment, but performance can be inflated when segments from the same recording are split across training and validation folds. We evaluated empirical mode decomposition (EMD) for term-versus-preterm classification using 26 pregnancy recordings (13 preterm, 13 term) from the public TPEHGT dataset. Annotated intervals and non-overlapping fixed 3-minute windows were compared, and the first four intrinsic mode functions (IMFs) were evaluated. Fourteen features from each of three EHG channels were assessed with nine classifiers using repeated five-fold recording-grouped cross-validation and recording-level aggregation. IMF1 gave the strongest mean performance. With fixed 3-minute IMF1 features, Random Forest achieved mean accuracy 0.8308, F1 0.7969, balanced accuracy 0.8308, MCC 0.6998, ROC-AUC 0.8157, and average precision 0.8877. IMF1 also outperformed matched filtered time-domain features across all reported mean metrics. Preterm recordings showed smaller, more regularly spaced peak-like events, lower temporal-energy measures, and higher entropy. These findings support further evaluation of IMF1-based EHG classification in larger independent cohorts.
Read the full abstract

All authors Umesha Tilakarathna, Senith Jayakody, Kalana Jayasooriya, Roshan Godaliyadda, Parakrama Ekanayake, Isuru Nawinne, Chathura Rathnayake

Quantitative Methods

DMT-Dens: Density-preserving manifold visualization for biological data

Ruizhe Wang, Yixuan Dong, Bolin Yang et al. (+3 more)

Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many methods preserve local neighborhoods, they may distort the apparent sampling density of processed observations, altering the visual contrast between dense and sparse regions and complicating the interpretation of rare, transitional, or continuous cell-state populations. Results: We present DMT-Dens, a parametric manifold-visualization method built on a latent-token Transformer encoder. The model integrates rank-based manifold alignment with hard-pair aggregation. To preserve density, it optimizes a loss based on the Pearson correlation between k-nearest-neighbor log-radius estimates in the processed input and two-dimensional embedding spaces. Benchmark evaluations demonstrate strong density preservation, particularly on biological datasets, while retaining competitive label separability. Availability: Source code, data-processing scripts, and resolved experiment configurations are available at https://github.com/Ruizhe-wang/DMT-Dens.
Read the full abstract

All authors Ruizhe Wang, Yixuan Dong, Bolin Yang, Bingo Wing-Kuen Ling, Fuji Yang, Zelin Zang

Quantitative Methods

Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design

Xuefeng Liu, Mingxuan Cao, Xiao Luo et al. (+5 more)

Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heterogeneous complex data and sharper geometric and chemical constraints. We introduce MCTH (Monte Carlo Tree Hallucination), an inference-only framework that casts all-atom sequence-structure co-design as uncertainty-aware planning over hallucinated states from pretrained folding and inverse-folding models, with optional biophysical control within the same decision loop. MCTH treats these models as frozen black-box operators and uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence and uncertainty, as well as cross-expert consensus/disagreement when multiple predictors are available. Across protein-RNA, protein-DNA, protein-protein, and protein-ligand design, matched-budget experiments show that adaptive search improves over simpler sampling and cycling strategies, while held-out AlphaFold3 and Chai-1 evaluations demonstrate transfer beyond the search-time oracle. MCTH provides a shared planning layer across modalities while allowing task-specific folding, inverse-folding, and biophysical modules, requiring no fine-tuning or backpropagation through component models.
Read the full abstract

All authors Xuefeng Liu, Mingxuan Cao, Xiao Luo, Songhao Jiang, Tobin Sosnick, Jinbo Xu, Louis Maher, Rick Stevens

Quantitative Methods

scDNM-VAE enables directly inspectable deep clustering of single-cell RNA-seq data through signed dendritic gating

Melih Agraz, Deniz Karapinar, Aysel Topsir et al. (+3 more)

Deep clustering models for single-cell RNA sequencing often assign cells through latent or centroid-based mechanisms that are difficult to inspect. We introduce scDNM-VAE (single-cell Dendritic Neuron Model Variational Autoencoder), a deep clustering framework that combines a variational autoencoder with a dendritic neuron-inspired head. Cluster assignments are governed by learnable signed synaptic weights and thresholds: the weight sign determines the direction of a gate's response to a latent coordinate, its magnitude controls steepness, and the weight-threshold pair determines the transition location. The trained clustering function can therefore be inspected directly without fitting a post-hoc explanation model. We benchmark scDNM-VAE on four datasets spanning immune, cortical, cardiac, and hematopoietic cells against scVI followed by KMeans and an MLP-DEC ablation. scDNM-VAE performs better than scVI on PBMC3k, comparably on the Human Heart Cell Atlas and Paul15, and worse on Zeisel, while producing biologically coherent marker-gene signatures. Ablating each cluster's three highest-magnitude synaptic dimensions causes numerically greater reassignment than random-dimension ablation across all datasets, but the margins are modest and negligible on Zeisel. These results show that signed dendritic gating supports competitive clustering with a parameter-inspectable decision function, while indicating that decision-relevant information is distributed across the latent space.
Read the full abstract

All authors Melih Agraz, Deniz Karapinar, Aysel Topsir, Qianying Cao, Erol Egrioglu, Gaurav Choudhary

Monday 17 August

Quantitative Methods · Cross-listed from cs.CV

Unsupervised Learning of Cell Instances with Generative Routing Pyramids

Ziwen Liu and Martin Weigert

Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources. The resulting pixel-to-latent associations yield instance masks, while the source latents encode cell morphology. We demonstrate competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, as well as generative modeling of cellular phenotypes under perturbations. Source code and checkpoints are available at https://github.com/weigertlab/routing-pyramids.
Read the full abstract