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Quantitative Biology Quantitative Methods

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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

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

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

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

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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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

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

Quantitative Methods

Curriculum Multiple Shooting for Robust Training of Neural and Universal Differential Equations

Sebastian Persson, Giacomo Fabrini, Branwen Snelling et al. (+1 more)

Neural ordinary differential equations (NODEs) and universal differential equations (UDEs) provide flexible and popular frameworks for learning interpretable dynamical systems from noisy time-series data. However, training these models remains challenging, and versatile methods that robustly handle sparse and noisy data as well as partially observed models are lacking. To address this, we introduce curriculum multiple shooting (CMS), a general-purpose training strategy for fitting ordinary differential equation (ODE) models to time-series data by integrating curriculum learning with multiple shooting. Across twelve benchmarks spanning simulated and real data, and covering NODEs, UDEs, and mechanistic ODEs, CMS accelerates and stabilises training convergence, outperforms state-of-the-art training strategies, and ranks among the best methods in generalisation. Finally, we discuss possible explanations for the strong performance of CMS in light of contemporary theories of what makes training on time-series challenging.
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All authors Sebastian Persson, Giacomo Fabrini, Branwen Snelling, Fabian Fröhlich

Quantitative Methods · Cross-listed from stat.ME

Multiparametric MRI Radiomics and Machine Learning Framework for Predicting Treatment Response in Glioblastoma

Suchibrata Patra

Distinguishing True Progression (TP) from Pseudo-Progression (PsP) after chemoradiotherapy remains a major diagnostic challenge in GBM, as both entities present near-identical appearances on conventional contrast-enhanced post-treatment MRI. This distinction carries substantial clinical weight, since TP and PsP demand divergent management yet cannot be reliably separated on routine imaging alone. We investigated whether radiomic features derived from a parsimonious, voxel-wise pharmacokinetic model of dynamic contrast-enhanced (DCE) MRI, combined with MGMT status, could discriminate between the two. The cohort comprised 82 adults with IDH-wildtype GBM who developed a new contrast-enhancing lesion within six months of chemoradiotherapy; classification (53 TP, 29 PsP) was established by histopathology where available (n=52) and modified RANO criteria otherwise (n=30). At every voxel, contrast-concentration time courses were fitted to five candidate pharmacokinetic models, and the best fit was retained by AIC minimisation, yielding parsimonious Ktrans, Ve, Vp, and taui maps adapting to local heterogeneity rather than a single fixed model across the tumour. Following segmentation, 1,073 radiomic descriptors were extracted and reduced via Mann-Whitney U filtering and Elastic Net, then used to train five classifiers across four feature configurations. A Random Forest classifier combining parsimonious DCE-MRI radiomics with MGMT status achieved the best discrimination (mean AUC 0.89, sensitivity 0.93, specificity 0.76, F1 0.90), outperforming features without MGMT (0.84), a T1-post-contrast baseline (0.72), and a single-model extended-Tofts analysis (0.68). Shape and textural descriptors of the Ktrans map, with tumour volume, were the strongest predictors, MGMT contributing a smaller, independent effect. Allowing the model to vary voxel-wise improves non-invasive discrimination.
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Quantitative Methods

A Low-Power Wearable Respiratory Sensor for Non-Invasive Stress Monitoring

Mohammad Hosseini, Hamed Khatounabadi and Mohammad Fakharzadeh

Respiration provides a continuously available window into physiological state and behavior. However, monitoring it outside controlled settings remains challenging because a wearable system must capture small body deformations while remaining comfortable, low power, and robust to changes in posture and motion. We present a compact non-invasive respiratory sensing system based on a force-sensitive resistor (FSR) embedded in an abdominal belt and integrated with a custom Bluetooth Low Energy acquisition board. The system combines a simple piezoresistive readout with a mechanical holder designed to transfer abdominal expansion to the sensor without analog amplification. We evaluate the complete sensing pipeline across multiple breathing patterns and body positions. In stationary settings, the recorded signals exhibit consistent amplitude changes and recurring peak-to-peak timing across breathing maneuvers; under light movement, these variations remain visible despite motion-induced baseline shifts. We further design a five-phase stress-induction protocol and collect respiratory recordings from 12 participants. Using interpretable time-domain features and standard classifiers, we examine whether the acquired signals distinguish relaxation from stress-induction phases. In this preliminary experiment, the best-performing model achieves 88.0% test accuracy, indicating that the extracted respiratory features distinguish stress-induced phases from relaxation phases in this dataset. Overall, our results show that the proposed platform enables real-time respiratory monitoring across diverse daily-life scenarios and captures respiratory changes that distinguish stress-induction from relaxation phases, supporting its potential for affective-computing applications.
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Wednesday 5 August

Quantitative Methods · Cross-listed from stat.AP

Uncertainty-Aware Missing-Data Multimodal Latent for Fetal-Growth Analysis

Tiago Cortinhal, César Díaz-Parga, Gabriel Bernardino et al. (+1 more)

Objective: Routine third-trimester examination yields fetal biometry, maternal, Doppler and fetal-cardiac measurements, acquired at clinical discretion and therefore often incomplete. We show that these four measurement blocks are close to mutually uninformative, and that this single property determines what a representation of them can impute, what it can audit, and what fetal size alone cannot indicate. Methods: A linear-Gaussian factor model (K = 8 by parallel analysis, VARIMAX-rotated) was fitted to 25 measurements in four blocks from 977 fetuses (169 SGA, 61 severe; 77 LGA). Posterior precision sums contributions from observed measurements only, so missing values are marginalized rather than imputed. Data quality was screened using the standardized residual between each measurement and its reconstruction. Results: Predicting any one block from the other three gives an out-of-fold R2 of 0.023. The representation is a continuous growth spectrum with no cluster structure (Hartigan dip p = 0.99, gap statistic k = 1, three-cluster silhouette 0.07) ordering fetuses by birthweight centile (Spearman rho = 0.55). Among 169 SGA fetuses the haemodynamic redistribution axis separated the 25 adverse outcomes (AUC 0.70, 0.585-0.808) where measured size did not (0.60, 0.451-0.738). With Doppler censored, the marginalized interval covered held-out measurements in 97% and 93% of cases against nominal 95% and 90%. The reconstruction residual flagged 38 of 977 records, 36 confirmed transcription errors in the registry. Conclusion: Marginalizing missing measurements yields a representation whose uncertainty reflects the available data, and whose reconstruction residual doubles as a data-quality screen. Because the blocks are nearly independent, confirmed flags are within-block errors, and a synthetic benchmark gives the coupling needed before cross-block detection becomes available.
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All authors Tiago Cortinhal, César Díaz-Parga, Gabriel Bernardino, Marta Nuñez-Garcia

Quantitative Methods · Cross-listed from cs.LG

MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

Anirudh Rayas, Yuan Wang and Pavan Turaga

Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction. However, effective learning remains challenging because EHRs encode heterogeneous, temporally ordered clinical interactions. In particular, EHRs contain: (i) heterogeneous clinical entities, including patients, visits, diagnoses, prescriptions, and procedures, together with their heterogeneous interactions, (ii) longitudinal patient trajectories across hospital visits and (iii) shared statistical dependencies across related clinical prediction tasks. Existing EHR learning methods capture only a subset of these properties. To bridge this gap, we propose Multi-task Graph transformer for Heterogeneous Temporal EHRs (MiGHT-EHR), which jointly models all three within a unified representation learning method. MiGHT-EHR constructs a heterogeneous graph from EHRs in which nodes represent clinical entities and edges connect statistically associated entities identified via normalized point-wise mutual information. Across MIMIC-III and MIMIC-IV datasets, MiGHT-EHR outperforms state-of-the-art methods on average across four tasks: drug recommendation, prediction of length-of-stay, mortality, and readmission, with particularly strong improvements in mortality and readmission prediction. Furthermore, a post-hoc analysis of the learned representations reveals that patient neighborhoods are organized by clinical outcomes, salient medical concepts are recoverable as linear directions in the representation space, and task probabilities are well calibrated. Collectively, these findings demonstrate that MiGHT-EHR representations support diverse prediction tasks while preserving clinically interpretable structure.
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Quantitative Methods

IL-10 rs1800896 polymorphism predicts biochemical remission in IBD patients undergoing biologic therapy

Michela Helga Falzone, Davide Giuseppe Ribaldone, Martina Buglione et al. (+8 more)

Background. Genetic factors, including single-nucleotide polymorphisms (SNPs), may modulate disease course and therapeutic efficacy in patients with inflammatory bowel disease (IBD). Aim. We investigated the association between four SNPs in cytokine genes and clinical phenotype as well as the response to molecular-targeted drugs in patients with IBD. Materials and Methods. A total of 197 IBD patients (142 Crohn's disease [CD], 55 ulcerative colitis [UC]) undergoing targeted treatment were enrolled. The SNPs analyzed were TNF-alpha rs1800629 (-308 G>A), TGF-beta rs1800471 (codon 10 C>T), IL-6 rs1800795 (-174 G>C), and IL-10 rs1800896 (-1082 G>A). Biochemical response at 12 months (T12) was defined as CRP <5.0 mg/L and fecal calprotectin <250 microg/g at T12, in the absence of ongoing corticosteroid therapy. Results. The IL-6 rs1800795 C allele was significantly associated with a younger age at diagnosis (p=0.049), while the TNF-alpha rs1800629 A allele was more frequently observed in patients with CD than UC (p=0.036). Among 134 patients included in the per-protocol analysis, 41.0% achieved biochemical remission at T12. The IL-10 rs1800896 variant allele was associated with remission (OR 2.15, 95% CI 1.03-4.44; p=0.041), and the association remained significant in multivariable analysis (adjusted OR 4.15, 95% CI 1.49-11.56; p=0.007). Conclusions. Genotyping of cytokine-related SNPs may help identify patients with distinct disease phenotypes and support personalized treatment strategies in IBD.
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All authors Michela Helga Falzone, Davide Giuseppe Ribaldone, Martina Buglione, Irene Cottone, Marta Vernero, Demis Pitoni, Angelo Armandi, Francesca Saba, Eleonora Dileo, Alfredo Santovito, Gian Paolo Caviglia

Quantitative Methods · Cross-listed from stat.ME

Parameter identification for predator-prey system with sparse data

Eduard Campillo-Funollet and James Van Yperen

Parameter identification from observations of dynamical systems is a fundamental problem in population biology. Mechanistic models of ecological systems rely on optimization methods that require accurate initial guesses to guarantee convergence. In ecological applications, datasets contain observation noise and are collected at sparse time points. This sparsity creates irregular likelihoods that cause standard optimization methods to struggle, while the ordinary differential equation solvers can become stiff or unstable in certain regions of the parameter space. These instabilities cause long running times or runtime errors. Here we present a computational framework for parameter identification that addresses these numerical instabilities by employing Natural Gradient Ascent, and we apply it to the classical Lotka-Volterra predator-prey model. We exploit the non-dimensionalization of the ordinary differential equations to treat scaling factors as nuisance parameters, reducing the dimensionality of the optimization problem. To prevent the solver step from becoming small, we implement an adaptive solver that switches between two independent second-order equations derived from the two components of the model. This approach allows Natural Gradient Ascent to converge in fewer iterations and with more stability than standard gradient ascent or BFGS methods. This framework provides a reliable method for parameter estimation in ecology when data is limited. The method can be generalized to other dynamical systems as long as the different components of the system do not become numerically problematic at the same time.
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Tuesday 4 August

Quantitative Methods · Cross-listed from eess.IV

CrossScale-GLIO: Topology-Preserving Vision-Language Alignment of MRI and Whole-Slide Histopathology for Diffuse Glioma

Yantong Liu, Zheyu Zhang, Runpeng Liu et al. (+3 more)

Magnetic resonance imaging and histopathology observe the same glioma at radically different scales. We present CrossScale-GLIO, a visual multimodal framework that represents MRI as a tumor-habitat graph and histology as a cell-niche graph, then aligns them with a structure-aware optimal transport objective anchored by diagnostic language. Across paired and external glioma cohorts, CrossScale-GLIO achieved a paired-test subtype macro-F1 of 0.789, IDH AUROC of 0.934, 1p/19q AUROC of 0.884, and MGMT AUROC of 0.802. The subtype gain over feature-only transport was 2.8 percentage points (95% CI: 1.2 to 4.4, adjusted p = 0.0019). Bidirectional patient retrieval reached Recall@1 values of 0.286 and 0.278, and Recall@5 values of 0.621 and 0.608. Pathologists rated 81.2% of high-mass habitat-niche pairs as biologically plausible. Deleting the highest-mass pair reduced correct-class probability by 0.184, compared with 0.049 under random deletion. Degree-preserving graph rewiring reduced subtype macro-F1 by 0.034 and retrieval Recall@1 by 0.090, directly confirming that preserved relational topology drives cross-scale correspondence.
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All authors Yantong Liu, Zheyu Zhang, Runpeng Liu, Mu Xitang, Seong-Yoon Shin, Hyun-Ae Lee

Quantitative Methods

SureRoute: Toward a Hallucination-Free Self-Improving Platform for Retrosynthesis

Jieli Zhou, Naiwu Chen, Longzhang Liu et al. (+1 more)

AI models, including large language models, are increasingly integrated into scientific discovery workflows, yet they remain prone to hallucination. In experimental sciences, such errors translate directly into failed wet-lab validations and wasted resources; in self-improving agentic systems, confident errors risk being reinforced rather than corrected. Retrosynthesis provides a representative example of this failure mode: existing models can generate chemically plausible routes, but cannot reliably determine which routes are experimentally feasible. We define Chemical Hallucination as a route that appears valid yet fails under competing reactive sites, unresolved selectivity, or missing mechanistic support, a failure largely invisible to the Recall@$K$ metric. We introduce SureRoute, a chemical verifier-anchored retrosynthesis platform that suppresses Chemical Hallucination. SureRoute combines a multi-model ensemble, data asset retrieval, and ChemHarness, an executable chemical intuition engine for route verification and reliability-first ranking. On a benchmark of 350 real-world industrial targets, SureRoute reaches 74.3% recall@1, 2.2–3.5$\times$ that of seven single-step models and three frontier LLMs, while cutting top-1 Chemical Hallucination to 4.6%, a 4–6$\times$ reduction relative to frontier LLMs. As a model-agnostic reranker, ChemHarness drives detectable hallucination toward near-zero across arbitrary backbone candidates. SureRoute shows that reliable scientific AI requires not only strong generation, but executable verification.
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All authors Jieli Zhou, Naiwu Chen, Longzhang Liu, Peiyu Zhang

Quantitative Methods · Cross-listed from cs.LG

MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification

Adam Simson, Ankush Dutta and Quang Bui

Multiple sclerosis (MS) is diagnosed through clinical assessment, magnetic resonance imaging, laboratory evidence when appropriate, and exclusion of better explanations. Blood RNA expression data may contain disease associated immune signal, but a blood RNA classifier cannot be treated as a replacement for clinical diagnosis. This paper presents MS-MLB (Multiple Sclerosis Machine Learning Benchmark), a reproducible open benchmark for machine learning based MS research classification from whole blood RNA expression data. MS-MLB uses the public GSE17048 cohort, converts it into an MS versus healthy control task, and evaluates multiple algorithms under a shared, leakage controlled pipeline that a researcher can rerun without reconfiguring the evaluation. The evaluation includes nested cross-validation, an untouched stratified holdout set, bootstrap confidence intervals, ROC and precision recall analysis, calibration measurement, and an exploratory MS Research Score. In the final benchmark summary, Gradient Boosting ranked first by MS Research Score on the holdout set, with an MS Research Score of 93.83, AUC-ROC of 0.989, sensitivity of 0.950, specificity of 0.778, $F_{1}$ score of 0.927, and Brier score of 0.050. Prior studies have applied machine learning to MS blood transcriptomic data, including PBMC stage classification and whole blood diagnostic signature modeling. The contribution here is different and narrower. To our knowledge, MS-MLB is the first open benchmark focused on MS versus healthy control classification from GSE17048 whole blood RNA expression data with a documented external model submission pathway built into the framework. The score is intended for research comparison only and has not been clinically validated. The benchmark is accessible here: https://github.com/duckyquang/MS-MLB.
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Quantitative Methods

The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling

Shashank Yadav, David M. Routman and Andrew Y.K. Foong

Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training. This practice excludes censored patients, collapses temporal information into a single threshold, and can affect which features are selected as prognostically relevant. We examine the cost of this binarization in the context of Bayesian network (BN) feature selection, using two recent publications as case studies: one that applies BN-based feature selection to a head-and-neck cancer cohort and a second surgical cohort study that, while not BN-based, likewise binarizes its survival endpoint. We replace the binary scoring function with the Cox partial log-likelihood for feature-to-outcome edges, a modification we call the Survival-Aware Bayesian network, and recover prognostic features that binarization misses. Our ablation experiment confirms that the improvement is driven by the time-to-event scoring formulation rather than by retaining more patients. The results generalize across five endpoint-cohort combinations in head-and-neck cancer and extend to three further cancer types (breast, colorectal, and kidney). We propose that clinical studies with survival outcomes should use time-to-event methods by default, as binarization discards the prognostic signal retained by survival analysis.
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Quantitative Methods · Cross-listed from q-bio.CB

Population Structures with Positive Feedback and Asymmetric Division

Gabriel Dooley, Camden Kilton, Brynley Needham et al. (+2 more)

In bioreactor experiments, budding yeast can manifest stable metabolic oscillations. In some instances these oscillations involve the cell cycle and are associated with a dynamical phenomenon called temporal clustering. Since yeast divide asymmetrically, mother cells may be able to divide sooner than the smaller daughter cells. We show that asymmetric division plus positive feedback can result in stable temporal clustering. In this scenario, the cells self-organize into $p$ clusters of mother cells and $q$ clusters of daughter cells, $p \le q$. In simple numerical simulations of a population model with asymmetric division and positive feedback, we show that $p:q$ periodic arrangements form spontaneously from random populations of cells. Our main result is that these structures can be stable.
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All authors Gabriel Dooley, Camden Kilton, Brynley Needham, Graham Walther, Todd R. Young

Quantitative Methods

Stochastic partial differential equation model for environmental DNA dynamics in river environments

Hidekazu Yoshioka

Environmental DNA (eDNA) has emerged as a novel tool for quantifying the seasonal abundance of aquatic species in water bodies; however, its mathematical modeling is still at a germinating stage because of its mechanistic uncertainties. We propose a first-step mathematical and computational framework for the eDNA dynamics of migratory fish based on a novel stochastic partial differential equation model with a delayed source input. The model governs spatiotemporal eDNA concentration in rivers where the source comes from a stochastic differential equation for the migration dynamics of the fish. The affine nature of the model facilitates its theoretical analysis, including the guarantee of well-posedness and the closed-form derivation of the Laplace functional despite the proportionality coefficient of the multiplicative noise term being non-Lipschitz. We also propose a discretization scheme for the model that theoretically generates nonnegative numerical solutions. We finally apply the proposed model to eDNA concentration data sampled from midstream reaches of a river system and perform sensitivity analysis.
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Quantitative Methods · Cross-listed from q-bio.NC

Persistent homology broadens the controllable subspace in human structural connectomes

Carter Sale, Marco Coraggio, Mengsen Zhang et al. (+1 more)

Network control theory applied to structural connectomes typically ranks brain regions as candidate driver nodes by their structural connectivity strength, and evaluates performance through scalar control energy. We test whether this framing captures the most relevant information about how driver-node selection shapes brain network control. We introduce an alternative criterion based on the persistent topological cycles in which each node participates—a measure of mesoscale integration that captures features beyond local connectivity—and compare it to standard degree-based selection across 70 human structural connectomes at three parcellation scales. Topology- and degree-informed driver sets achieve nearly identical scalar control energy, differing by approximately 0.2%. The geometry of the controllable subspace, however, differs substantially: topology-informed sets distribute controllability across more dimensions of state space and produce better-conditioned controllability matrices. This geometric advantage is preserved when high-degree hub nodes are removed, and it carries a functional signature: because the two criteria place driver nodes in different cortical territory, each most efficiently reaches a different class of target state. The choice of node-ranking criterion therefore shapes which brain-state transitions are energetically favored even when average control cost is unchanged. The results reveal a dissociation between control cost and control geometry, and demonstrate that persistent topology captures information about brain network control that scalar energy summaries miss.
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All authors Carter Sale, Marco Coraggio, Mengsen Zhang, Michael J. Richardson

Quantitative Methods · Cross-listed from cs.AI

Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer

Yesung Cho, Ji Hwan Park, Chanil Kim et al. (+27 more)

Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured. Here, we developed an outcome informed spatial pathology framework in TNBC that integrates AI generated recurrence risk heatmaps with mass spectrometry based spatial proteomics. In a cohort of 156 patients, distribution based aggregation of high scoring patches achieved an AUC of 0.77 and a C-index of 0.77 in an independent test cohort. Bulk proteomics associated high image derived risk with cell cycle and genome maintenance programs and low risk with immune activation. High and low risk patches coexisted within the same tumor compartment and displayed distinct nuclear and architectural features, revealing intratumoral heterogeneity beyond tissue compartment identity. We then used the heatmaps as coordinate level guides to physically isolate and profile 46 AI defined tumor regions from two recurrence patients. Spatial proteomic profiling revealed a concordant molecular contrast across both patients: mitotic programs were enriched in high risk regions and immune and antigen presentation programs in low risk regions. A 13 protein composite derived from these spatial contrasts showed a trend toward poorer recurrence-free survival with increasing scores in an expanded cohort, while the corresponding transcript based composite stratified recurrence free survival in the independent METABRIC TNBC cohort. Integrating the protein composite with the H&E derived risk score improved the out of bag C-index from 0.679 to 0.739 and enhanced time dependent discrimination at 3 and 5 years. Together, these findings define a new role for outcome trained AI models as spatially explicit experimental guides that connect prognostic morphology with localized molecular states and advance biologically grounded, multiscale biomarker discovery in TNBC.
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All authors Yesung Cho, Ji Hwan Park, Chanil Kim, Hyewon Kim, Honglan Li, Yumin Lee, Geongyu Lee, Sujeong Hong, Seong Min Park, Yoonyoung Lee, Hee Sool Rho, Sumin Lee, Amos Chungwon Lee, Changhwan Lee, Hwanyoung Shim, Hyunwook Kim, Hyeji Shin, Sanha Park, Jihoon Yu, Yoon Hee Shin, Sooheon Kim, Hyunjin Park, Seung Min Park, Sangwan Kim, Yujung Kim, Sung-Im Do, Eun-Young Kim, Dongmyung Shin, Jongbae Park, In-Gu Do

Monday 3 August

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

CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queries

Pratyush Kumar Shukla, Manveer Singh Tib and Siddhant Garg

A natural language interface can be used to make cancer genomics databases easier to use, but even if a question is perfectly fluent, its scientific meaning can be ambiguous. We propose CLARA, a framework that represents a question as a typed scientific query specification, considers a few possible interpretations, executes them, and asks for clarification when the estimates diverge. CLARA was assessed on mutation-prevalence contrasts among eight TCGA PanCancer Atlas cohorts and a 30-gene panel. This benchmark consisted of 330 unique executable contrasts varying in mutation scope, assay denominator, and sample context; 115 contrasts were result-sensitive and 215 were result-stable, per the preregistered definition of relative divergence greater than 0.10 or absolute divergence greater than 5 percentage points. An independently implemented pandas execution engine perfectly replicated all 660 results from the SQLite engine. In a separate 120-question LLM-generated, manually vetted language stress test, CLARA recognized all 60 result-sensitive contrasts and needlessly clarified 13 of 60 stable contrasts (accuracy 89.2%, sensitivity/recall 100%, specificity 78.3%). Standalone machine learning had superior overall accuracy (97.5%) but missed one critical contrast. This demonstrates that downstream execution can distinguish consequential from inconsequential ambiguity and reveal an explicit trade-off between safety and burden.
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Quantitative Methods

REDE: A Quantitative Framework for Differential-Expression Reproducibility and Diagnostic Transfer Across Nine Cohorts and Three Cancers

Athanasios Angelakis

Differential-expression analyses often turn cohort-specific significance into claims of stable gene signatures or diagnostic biomarkers. We evaluated which layers of evidence reproduce across independent datasets and whether discovery-derived panels retain locked tumor-versus-non-tumor classification performance. Nine public microarray cohorts were organized into fixed discovery, validation, and external-test experiments for pancreatic ductal adenocarcinoma, breast cancer, and lung cancer. Reproducibility was assessed for DEG burden, exact membership, top-rank overlap, signed effects, prespecified gene confirmation, and Hallmark pathways. We also introduced REDE-2Fold, in which each discovery cohort is split once at patient level, differential expression is performed independently in both folds, and only same-direction genes selected in both are retained. Four training-only panels were then evaluated with locked logistic models and thresholds: all discovery DEGs, the top 19 discovery DEGs, all REDE-2Fold genes, and the top 19 REDE-2Fold genes. Broad DEG-list confirmation in both independent cohorts ranged from 15.5% to 39.5%, rising to 50.1% to 84.3% for large effects. Pathway replication ranged from 52.2% to 88.9%. Compact 19-gene panels retained high external ROC-AUC, but locked operating points were often unstable: some models with ROC-AUC near 1.0 showed zero specificity or very low sensitivity. These results define a hierarchy from thresholded membership through effect, pathway, discrimination, and operating-point transfer. REDE provides a seven-level quantitative framework for matching transcriptomic claims to the evidence actually tested, while REDE-2Fold offers a minimum internal feature-stability procedure that strengthens but does not replace independent validation.
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Quantitative Methods

A method for comparing inferred evolutionary accumulation dynamics across covariates and model structures

Jakob Fernø, Michelle B. Verstraaten, Lila Gravellier et al. (+3 more)

In this note we describe a method for comparing inferred dynamics in evolutionary accumulation models (EvAMs). These models involve the acquisition of multiple, potentially codependent, binary features over time – for example, mutations in cancer development, or phenotypes in evolutionary biology. As the set of methods for inferring EvAM dynamics expands, approaches for comparing inference across algorithms, datasets, and covariates are required. In particular, a comparison method supporting reversible, stochastic dynamics, interactions between feature sets, and potentially non-independent samples (as well as simpler cases) has yet to be established. It is possible for EvAM with similar relative feature orderings to produce completely different sets of observed states due to “frameshift”-like differences; methods distinguishing state and transition similarity are therefore also desirable. Here we suggest a method focussed on ordering matrices, describing the probability that a feature is acquired under different conditions on other features, and is thus generally comparable across methods and datasets. We demonstrate how the approach captures both statistical robustness (given EvAM uncertainty) and scientifically meaningful differences in inferred dynamics. The method is applied to synthetic and real-world data on the evolution of chromosomal aberrations in different tumour types and of drug-resistant bacteria in different countries.
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All authors Jakob Fernø, Michelle B. Verstraaten, Lila Gravellier, Ellen C. Røyrvik, Ramon Diaz-Uriarte, Iain G. Johnston

Quantitative Methods

A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models

Shu Quan, Tianfang Hao, Sitong Fang et al. (+8 more)

Large Language Models (LLMs) are accelerating biological research, yet this same capability poses a critical biosecurity threat: models that assist in protein engineering can equally be prompted to generate predicted toxin-like sequences, potentially lowering the barrier to biological misuse. Current safety evaluations, however, operate in natural language and cannot determine whether a model-generated amino acid sequence is biological gibberish or a computational risk signal. To address this evaluation blind spot, we introduce SPIKE-Bench, coupling 631 curated toxin-design prompts across seven functional categories with the SPIKE funnel, a three-stage protocol that filters output through compliance, biological plausibility, and predicted toxicity, producing stage-level diagnostics and an aggregate function-aware metric: the Functional Harmfulness Rate (FHR). An audit of 32 LLMs reveals that most models freely comply with toxin-design requests; FHR is driven primarily by biological generation capability rather than safety alignment, reaching 50.7%; and Refusal Rate fails to predict functional risk. As a first step toward mitigation, we provide BioSafe-Guard, a domain-specialized classifier that substantially reduces predicted functional risk while preserving benign utility. We release SPIKE-Bench and BioSafe-Guard at https://github.com/PKU-Alignment/SPIKE-Bench to support more rigorous biosecurity evaluation of LLMs.
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All authors Shu Quan, Tianfang Hao, Sitong Fang, He Geng, Jiayi Zhou, Boyuan Chen, Kaile Wang, Donghai Hong, Juntao Dai, Yaodong Yang, Jiaming Ji

Quantitative Methods

A curvature-based criterion for harmonic circadian waveforms

Yusuke Yamada, Yutaro Kabata, Ryoya Fukasaku et al. (+1 more)

Experimental and theoretical studies of circadian rhythms have focused largely on the period, on mutants that alter it and on phase shifts, and this focus has driven the identification of clock genes and clarified how clocks entrain to light–dark cycles. The waveform of the oscillation itself, by contrast, has attracted little attention as an indicator of the properties of the underlying oscillator. To assess the waveform directly, we focus on whether the trajectory in the plane spanned by a variable and its time derivative possesses an inflection point, and we define an oscillation to be harmonic when no inflection point is present. Bioluminescence recordings from cyanobacteria and from the mammalian SCN were harmonic in this sense, as were most of the core clock components in mathematical models of the circadian clock. Numerical analysis of the Goodwin model, a minimal representation of the core circadian oscillator, yielded harmonic oscillation throughout. We confirmed this numerical trend semi-analytically using a piecewise-linearized Goodwin model. Because it evaluates the properties of a waveform without assuming a model structure, the approach we propose offers a new perspective on the waveform analysis of biological rhythms in general, not only circadian ones.
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All authors Yusuke Yamada, Yutaro Kabata, Ryoya Fukasaku, Hiroshi Ito

Quantitative Methods

NeuroInspector: A Local-First Environment for Inspecting and Annotating Hierarchical Neuroscience Datasets

Zihan Yang

The growing scale and structural complexity of neuroscience datasets have made dataset inspection an increasingly distinct stage of the research workflow. Existing inspection workflows, however, remain fragmented, often relying on exploratory scripts, manual documentation, and repeated navigation of unfamiliar file structures before meaningful scientific analysis can begin. Here we present NeuroInspector, a lightweight, browser-based environment for inspecting HDF5 and NWB files. Using WebAssembly-based HDF5 parsing (h5wasm), NeuroInspector runs entirely client-side: files are opened directly from local disk, and the application implements no file-upload endpoint, including in its hosted deployment. The tool combines structural navigation, metadata inspection, sampled data previews, and path-level annotation into portable, fingerprinted "project packs" that preserve inspection decisions without modifying the original file. Rather than functioning as an analysis or validation platform, NeuroInspector provides a dedicated, traceable environment for the inspection stage that precedes formal analysis.
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