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Quantitative Biology Quantitative Methods

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Wednesday 9 September

Quantitative Methods

Discovering Subtypes of Neurodegenerative Progression with a Scalable Connectome-Constrained Dynamic Model

Daniel Semchin, Emile d'Angremont, Hao Ding et al. (+6 more)

Parkinson's disease is clinically and biologically heterogeneous, yet its spatiotemporal progression remains poorly characterized. We present a connectome-constrained disease progression model that jointly estimates subject-specific disease time and data-driven subtypes from longitudinal morphometry. Applied to 85 imaging and clinical biomarkers from the Parkinson's Progressive Markers Initiative (PPMI) cohort, the model recovers four morphologically distinct progression subtypes. We validate the model on a hold-out cross-sectional dataset and benchmark it against SuStaIn under a matched training and validation protocol. Only our method recovers subtypes that correspond significantly to clinical motor subtypes and genetic variants of Parkinson's Disease.
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All authors Daniel Semchin, Emile d'Angremont, Hao Ding, Alan Antar, Marco Lorenzi, Konstantinos Arfanakis, Ysbrand van der Werf, Paul Thompson, Boris Gutman

Quantitative Methods

scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning

Murthy Devarakonda

Longitudinal single cell atlases now capture matched pre treatment and post treatment states from responders and non responders, presenting an opportunity to mechanistically explain why two patients on the same drug diverge. We introduce scDEFT (single cell Drug EFfect Transducer), which treats a drug as a conditioning operator on cell representations, enabling prediction and explanation. In scDEFT, feature wise linear modulation produces drug conditioned cell latents, learned under abundant per cell supervision and then frozen. Two independent heads aggregate those latents over shared transcriptional neighborhoods to predict drug induced state change and responder status. A backward stage ranks the latent dimensions by how strongly they separate responders from non responders and maps them to genes under a cell composition control. On a harmonized inflammatory bowel disease atlas of 1.16 million cells, three cohorts and two drug classes, scDEFT predicts state change at 45% of the baseline to reproducibility ceiling headroom and stratifies responders before treatment at AUROC 0.70, where standard predictors remain at chance. These predictions and the drivers behind them support target and co target nomination, patient stratification, and counterfactual prediction of unseen drug cohort effects.
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Quantitative Methods

ADMET-EvO: a self-evolving scientific agent for sustained research across heterogeneous tasks

Yiling Zhou, Yilin Wang, Jianmin Wang et al. (+8 more)

Scientific agents can move beyond automated model building by using accumulated evidence to revise both their questions and experimental strategies. The challenge is sustaining this adaptation across heterogeneous tasks without overfitting decisions to internal validation. Absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction provides a demanding setting across diverse assays, datasets and chemical domains. We therefore developed ADMET-EvO, an evidence-gated agent that formalizes endpoints, generates falsifiable hypotheses and tests interventions across data, feature and model axes. It carries supported, rejected and inconclusive outcomes forward to guide each new cycle. Across the 22-task Therapeutics Data Commons (TDC) ADMET benchmark, ADMET-EvO achieved the highest task-normalized score of 96.77. Evidence-guided selection reduced cumulative fitting time by 72.2% within a predefined non-inferiority margin. It also formalized 43 toxicity-related tasks and constructed endpoint-specific predictors. Together, these results show how ADMET-EvO can accumulate evidence, revise its strategy and expand its research scope over time.
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All authors Yiling Zhou, Yilin Wang, Jianmin Wang, Heqin Zhu, Zirui Wang, Chang-yu Hiesh, Kejun Ying, Jiaqi Wang, Yuzhi Xu, Tingjun Hou, Odin Zhang

Quantitative Methods · Cross-listed from cs.LG

ProMeta: Few-shot PROTAC-targeted degradation prediction across E3 ligases

Yuansheng Liu, Yufei Ye, Tao Tang et al. (+3 more)

Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically ”undruggable” targets via the ubiquitin-proteasome system. Despite growing efforts to develop computational predictors of PROTAC degradation activity, existing supervised approaches remain severely challenged by data scarcity and imbalance across E3 ligases, limiting their ability to generalize beyond well-studied ligase contexts. In practice, labeled data are heavily concentrated on a few ligases (e.g., CRBN and VHL), while the majority of E3 ligases remain underexplored yet are critical for expanding the design space of targeted degraders. Developing methods that enable robust cross-ligase generalization with minimal labeled data is therefore essential for improving the practical utility of computational PROTAC discovery. We reformulate PROTAC degradation activity prediction across E3 ligases as a few-shot meta-learning problem and present ProMeta, a prototype-based graph neural network trained through episodic meta-learning on source-E3 tasks and evaluated on held-out target-E3 tasks through support-conditioned inference. ProMeta performs inference without updating the encoder by dynamically estimating class prototypes from minimal target-ligase support samples. On the CRBN-to-VHL benchmark, ProMeta achieves AUROC values of 0.796 under K=2, Q=3 and 0.883 under K=2, Q=5, improving by 19.9% and 6.8%, respectively, over the corresponding supervised GNN baseline. Reverse VHL-to-CRBN transfer under the same protocol yielded AUROC values of 0.702 (K=2, Q=3) and 0.821 (K=2, Q=5), confirming bidirectional applicability while revealing direction and data-regime dependence. Together, these results support ProMeta as a practical framework for cross-ligase few-shot prediction under the evaluated support/query protocols.
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All authors Yuansheng Liu, Yufei Ye, Tao Tang, Jiawei Luo, Wen Tao, Xiao Luo

Quantitative Methods

Multi-Task Bacterial Colony Detection and Classification Using YOLOv8 with Edge Optimization for Resource-Constrained Deployment

Belaguppa Manjunath Ashwin Desai, Rohan Rajesh, Shreyas Murthy et al. (+2 more)

Manual counting and classification of bacterial colonies are critical yet labor-intensive tasks in microbiology, prone to human error particularly on densely populated plates. This work proposes a multi-task deep learning framework trained on the Annotated Germs for Automated Recognition (AGAR) dataset (18,000 images; 9,202 training / 3,067 testing) to automate Colony Forming Unit (CFU) enumeration and species classification. A custom multi-task CNN employing global regression served as the baseline, but demonstrated limited performance in clustered colony environments due to the absence of spatial localization. To address this, a YOLOv8 object detection architecture was adopted with high-resolution 1024x1024 inputs, enabling instance-level colony detection and label assignment. The model achieved a classification accuracy of 98.13% and a counting accuracy of 98.27% (within a 10-colony margin), demonstrating strong predictive capability. To bridge the gap between model performance and practical deployability, the trained model was optimized through unstructured and structured pruning, ONNX conversion, and reduced-precision inference (FP32, FP16, INT8). On a Raspberry Pi 4B, ONNX FP32 and FP16 variants offered the best balance between inference speed (~6.4s) and accuracy (MAE ~2.20). Unstructured pruning preserved predictive accuracy (MAE ~2.01) without runtime gains, while structured pruning resulted in significant accuracy degradation (MAE ~6.3), revealing the sensitivity of instance-level colony detection to architectural compression. These findings provide practical guidance for selecting optimization strategies in resource-constrained laboratory deployments.
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All authors Belaguppa Manjunath Ashwin Desai, Rohan Rajesh, Shreyas Murthy, Pronama Biswas, Revathi Vaithiyanathan

Tuesday 8 September

Quantitative Methods · Cross-listed from cs.SD

Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition

Frank Fundel and Alexandra Howard

Passive acoustic monitoring produces far more bat recordings than experts can label. We show that simple model-generated pseudo-labels turn this surplus into effective supervision. We compare pseudo-labeling with other semi-supervised learning methods on an 18-species European corpus using only 10% of its training labels, then transfer the strongest approaches to South African field audio containing nine bat taxa and a nuisance class. Pseudo-labeling outperforms the other semi-supervised learning methods on every European measure, recovering up to 61.5% of the gap to full supervision. It transfers to field audio with gains of 10.69 points in species accuracy and 4.96 points in species macro-F1. We also introduce genus-aware smoothing, which directs uncertain target mass toward congeneric species. Combined with uniform smoothing, it reaches 79.16 species macro-F1, 4.73 points above hard targets. Simple pseudo-labels are therefore highly effective at this ecological data scale, while genus-aware targets inject useful biological structure at no annotation cost. https://code4conservation.github.io/UnlabeledEchoes/
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Quantitative Methods · Cross-listed from physics.chem-ph

Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

Weichi Yao, Cameron Gruich, Bryan R. Goldsmith et al. (+1 more)

Molecular size is coupled to composition, structure, and function, yet most 3D molecular generators require a predefined atom count. We introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage framework that samples a fixed-dimensional latent vector using flow matching and uses an autoregressive Transformer to determine molecular size, atom types, coordinates, and chemical attributes. Canonical atom ordering and rigid-pose alignment enable Transformers without equivariant layers, while decoded attributes guide bond reconstruction. On PCQM4Mv2, unconditional generation yields 87.9% unique, novel molecules passing sanitization and PoseBusters checks, exceeding baselines with lower end-to-end training and sampling time and higher end-to-end throughput. Across ten target HOMO-LUMO gaps, internal ranking retains 30% of screened candidates and increases the density functional theory-verified hit rate within 0.1 eV from 25.0% to 52.4%, while largely preserving novelty and diversity. These results demonstrate fixed-dimensional latent generation with autoregressive decoding as a practical approach to molecular design without prespecifying size.
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All authors Weichi Yao, Cameron Gruich, Bryan R. Goldsmith, Yixin Wang

Quantitative Methods · Cross-listed from eess.IV

Reliability assessment and multicenter clinical application of magnetic resonance methods for knee cartilage quantification

Binbin Yang, Yongmei Jian, Chenglei Liu et al. (+10 more)

Background: This study evaluated interreader agreement and longitudinal performance of MRI methods for knee cartilage volume, thickness, and defect-area quantification. Methods: AI-presegmented masks from 1,189 phase III examinations underwent independent correction by two readers and adjudication. Cartilage volume, three-dimensional ray-tracing thickness (3D-RT), and ray-based defect area (3D-RBA), defined by a 1.5-mm thickness threshold, were calculated. Agreement was assessed using segmentation metrics, intraclass correlation coefficients (ICCs), repeated-measures Bland-Altman analysis, and minimal detectable change at 95% confidence (MDC95). The 3D-RBA framework was evaluated in 120 digital-phantom experiments from 40 participants. Longitudinal analyses included 374 participants, alternative-method comparisons included 65, and retrospective phase II analysis included 24 participants with four visits. Results: Overall AI-to-adjudicated-mask Dice was 0.964 +/- 0.029. Interreader ICCs for volume, thickness, and defect area were 0.956, 0.904, and 0.932; corresponding MDC95 values were 1,596.9 mm^3, 0.227 mm, and 147.4 mm^2. Geometric mean absolute percentage error for defect area was 5.62%, with spatial Dice of 0.961. In 374 participants, volume changes correlated positively with thickness changes (rho=0.431) and negatively with defect-area changes (rho=-0.221). Within-participant phase II correlations followed the same directions in both groups. Conclusions: The workflow demonstrated good interreader agreement. Controlled geometric results and longitudinal associations supported the feasibility of threshold-based defect-area estimation. Volume, thickness, and defect area provide complementary measures of cartilage structure.
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All authors Binbin Yang, Yongmei Jian, Chenglei Liu, Rui Huang, Hongda Shao, Linjun Tong, Yuanjing Xu, Jingshu Wu, Chengzhang He, Suting Peng, Ming Xiao, Yinan Chen, Qi Duan

Monday 7 September

Quantitative Methods

MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification

Honghan Shen

Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioprocessing, and the discovery of acid-stable enzymes. However, their identification relies heavily on time-consuming experimental screening methods. With the rapid growth of protein sequence databases, the need for computational identification methods that are both accurate and efficient has become stronger. The emergence of protein language models (PLMs) has significantly improved the sequence representation of downstream biological prediction tasks. This paper proposes MI-PEFT, a mixture-of-experts integrated parameter-efficient fine-tuning framework. Built on the ESM C-600M backbone, the framework incorporates LoRA-based PEFT methods and a DeepSeekMoE-based classification head to resolve the limitations of PEFT and significantly improve computational efficiency. Notably, this task is characterized by a significant class imbalance in the dataset, making high specificity particularly challenging. The experimental results demonstrate that MI-PEFT on PLMs, especially {\text{C}}^{\text{3}}\text{A}, serves as an efficient tool for identifying acidophilic proteins and a constrained pathway that helps resolve class-imbalance by preserving the pretrained representations.
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Quantitative Methods · Cross-listed from math.AP

Completion of DNA replication is constrained by the spatiotemporal organisation of origin firing

Ahmad Alkhaled, Francisco Berkemeier, Michael A. Boemo et al. (+1 more)

DNA replication requires the coordination of origin firing and fork progression to ensure the entire genome is timely duplicated before cell division. Yet origin firing is stochastic, giving rise to the classical random completion problem of how probabilistic local events can nevertheless ensure reliable genome duplication. Although several biological mechanisms have been proposed to resolve this problem, a quantitative account of how heterogeneous initiation and fork speed govern the persistence of the final unreplicated regions is still lacking. To address this gap, we introduce a population-level kinetic framework that extends KJMA nucleation-and-growth models by tracking unreplicated intervals over size, genomic position and time. We establish well-posedness of the resulting mean-field system and global existence for compatible data. Notably, by introducing a local initiation mass function, we quantify how the density and spatial organisation of origin firing constrain replication completion, yielding novel and sharp upper bounds on both the worst-locus unreplicated fraction and locuswise near-completion time. These results provide a rigorous and computable foundation for mapping vulnerabilities in replication completion and relating persistent unreplicated regions to replication stress and genome instability.
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All authors Ahmad Alkhaled, Francisco Berkemeier, Michael A. Boemo, Katerina Nik

Quantitative Methods · Cross-listed from cs.LG

The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]

Bilal Ahmad and Rajed Mehmood

Automated prediction of Enzyme Commission (EC) numbers plays a central role in functional annotation and computational drug discovery. However, standard multi-label machine learning pipelines frequently rely on default decision thresholds (t=0.50), assuming balanced prior distributions across target heads. In this study, we present a systematic empirical diagnostic of uncalibrated fixed decision boundaries operating under severe class imbalance across N = 14,096 annotated compounds categorized into six primary EC classes (EC1-EC6). Our results highlight a pronounced Accuracy Paradox: while the multi-label system achieves a deceivingly high mean accuracy of 77.16%, the macro F1-score (0.3976) and macro recall (0.3872) reveal severe predictive breakdown. Majority target classes suffer from hyper-sensitivity and over-prediction, whereas minority classes exhibit sharp recall decay, culminating in a total decision boundary collapse for EC6 (Recall = 0.00%) despite underlying discriminative power (ROC-AUC = 0.5857). Feature correlation analysis further reveals high linear redundancy among topological indices relative to fingerprint density metrics. Ultimately, this diagnostic study demonstrates that standard point predictions mask critical errors in bioinformatics workflows. We establish target-specific threshold optimization and post-hoc conformal calibration as essential, open-source post-processing safeguards for reliable applied machine learning and deep learning architectures.
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2609.07897 · 7 Sep · 11 pages, 4 figures,Enzyme Commission prediction, multi-label classification, class imbalance, threshold optimization, conformal calibration, accuracy paradox, bio-data, applied machine learning, bioinformatics. imbalanced bio-data, model diagnostic, performance metrics, open-source pipeline

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Quantitative Methods · Cross-listed from cs.LG

Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers

Jakob Snel and Marc-Andre Schulz

Normative age models are trained to predict chronological age in a nominally healthy cohort. Applied to patients, they deviate, and the gap between predicted and chronological age is read as disease risk. Here, we attribute the disease-related effect size of the age gap directly to individual training samples, rather than using a prediction-level loss as the attribution target. For Cohen's $d$, the resulting closed-form influence functional, validated against leave-one-out retraining, ranks training samples by their effect on held-out case-control separation. Across four diseases and two biomarker modalities in UK Biobank, removing the 10% most influential training samples raises held-out disease-related effect size in every seed. It more than doubles the metabolomic-age effect for type-2 diabetes and raises the brain-age effect for multiple sclerosis by roughly a third. Random removal leaves effect size flat even at 50% removal, confirming the gain comes from which samples are removed, not how many. Flagged subjects carry subclinical cardiometabolic burden that diagnosis-based exclusion misses, on markers the model never sees. For type-2 diabetes, where the method gains most, the marker recovered is HbA1c, the standard measure of blood sugar control. We release pyinfluence, our influence-function package, for reproducibility and reuse.
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Sunday 6 September

Quantitative Methods · Cross-listed from eess.IV

MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting

Chengkai Wang, Luoyu Hong, Yiting Zhao et al. (+7 more)

High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at https://william2ai.github.io/medgssr
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All authors Chengkai Wang, Luoyu Hong, Yiting Zhao, Jiamin Wang, Xiang Feng, Feiwei Qin, Zhenzhong Kuang, Xuefei Yin, Ali Bashashati, Yanming Zhu

Saturday 5 September

Quantitative Methods · Cross-listed from cs.AI

Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools

Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi et al. (+2 more)

We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use protein design workflows. ABLE assesses agent performance through a set of tasks spanning structure retrieval, sequence generation, and design validation. We evaluate 15 frontier models and find that seven refuse all tasks, while the remaining models exhibit substantial performance differences. Claude Sonnet 4 and Gemini 3 Pro achieve the highest scores across information retrieval, tool selection, and tool use. We further compare model performance on a subset of tasks against an expert human baseline. Our results suggest that current LLMs can substantially lower barriers to protein design, but remain inconsistent in planning, strategy generation, and integrating biological knowledge with tool use.
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All authors Bryce Cai, Geetha Jeyapragasan, Samira Nedungadi, Jake Yukich, Seth Donoughe

Quantitative Methods · Cross-listed from stat.AP

A Network-Structured Bayesian Hierarchical Model for Sparse Mutation-Drug Response Associations: Application to Cancer Pharmacogenomics

Hammed A. Olayinka and Saheed O. Olayemi

We develop a network-structured Bayesian hierarchical model for sparse association mapping between genomic alterations and quantitative treatment-response phenotypes. The framework combines a Gaussian Markov random field prior that borrows strength across pathway-connected genes, a global-local horseshoe prior inducing sparsity, and a conjugate Gibbs sampler requiring no Metropolis-Hastings steps. Though broadly applicable to high-dimensional settings with known predictor networks, we validate it using cancer cell-line drug-sensitivity data. Applied to GDSC2 ($N=951$ cell lines, $G=219$ driver genes, $D=295$ drugs), the model identifies 126 gene-drug associations (0.195% of 64{,}605 pairs), concentrated in EZH2 (45 drugs, all sensitivity-direction, mean effect $-0.911$ $\ln$IC50) and KMT2D (36 drugs, all sensitivity-direction, mean effect $-0.496$ $\ln$IC50). These markers show external support in an independent PRISM screen (1{,}518 compounds), with KMT2D achieving complete directional replication (36/36) and EZH2 partial replication (8/12). Five-fold cross-validated predictive log-likelihood confirms each prior layer's value: the full model outperforms the no-network ablation by $+3{,}109$ log-units per fold and the no-horseshoe ablation by $+14{,}039$ log-units, consistently across folds. Simulations under three scenarios show the full model achieves the highest precision and lowest false-discovery rate throughout, while the network prior improves sensitivity recovery under network-structured signal. A tissue-stratified extension identifies coherent subgroup refinements, including lung-specific EGFR-inhibitor sensitivity and skin-specific BRAF-Dabrafenib sensitivity. These results show the framework identifies sparse, interpretable, externally supported drug-sensitivity markers while enabling principled investigation of tissue-specific departures from shared effects.
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Friday 4 September

Quantitative Methods · Cross-listed from cs.CV

A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks

Reza Rajabli and D. Louis Collins

When there is not enough labeled data to properly train deep learning models, transfer learning can help. We still do not fully understand how effective it is in neuroimaging, especially for Alzheimer's disease research. It is also not clear if these transferred models can work on new datasets without being retrained for each specific task. We evaluate whether a compact, supervised pretrained model can serve as a reusable foundation model for downstream neuroimaging tasks. We freeze the 7.18 million weights of a 3D CNN previously trained for brain-age prediction, and adapt it to each task using Low-Rank Adaptation (LoRA), requiring only ~1% additional trainable parameters. We evaluate generalizability in six experiments. Adapting the model to classify cognitively normal versus Dementia on ADNI gave an AUC of 0.964 on held-out folds (Experiment #1). Applying that adapted model unchanged to OASIS-3, with no retraining, gave an AUC of 0.871 (Experiment #2). Reusing its output logit together with age and a cognitive score distinguished stable from progressing MCI with an AUC of 0.828 (Experiment #3). Adapting the same backbone to predict amyloid positivity from structural MRI gave an AUC of 0.804 (Experiment #4). Finally, the same approach estimated ICV-normalized hippocampal and white matter hypointensity volumes directly from the T1w image, with R^2 of 0.80 and 0.91 respectively, tasks normally addressed with much larger U-Net networks (Experiments #5 and #6). A compact model supervised on brain age can therefore serve as a reusable backbone, adapting to each task with ~1% additional parameters and transferring to an unseen cohort without any training. Our findings suggest that a carefully trained brain age model can serve as an effective foundation model for Alzheimer's related tasks, even under strict data constraints.
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Quantitative Methods · Cross-listed from cs.LG

Simulation-free Unbalanced Dynamic Optimal Transport with General Growth Penalty

Junda Ying, Yuxuan Wang, Bowen Yang et al. (+2 more)

Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and population growth or death. Unbalanced dynamic optimal transport (UDOT) addresses this by penalizing growth along transport paths, making the choice of growth penalty a key way to encode biological priors on proliferation and apoptosis. However, existing UDOT solvers either rely on computationally expensive NeuralODE simulations or depend on analytical solutions of conditional paths, restricting their efficiency solely to quadratic penalties, i.e. Wasserstein-Fisher-Rao (WFR) geodesics. To enable an efficient UDOT solver for general growth penalties, we first show that concave growth penalties lead to degenerate solutions where growth and transport are separated. We then introduce Simulation-free Unbalanced Dynamic Optimal transport (SUDO), a simulation-free framework for UDOT with general non-quadratic convex growth penalties. SUDO learns the conditional paths and transport costs, solves the induced semi-coupling problem, and subsequently leverages unbalanced flow matching to achieve a simulation-free solution. On WFR benchmarks, SUDO matches the accuracy of efficient, analytical solution-driven algorithms while outperforming simulation-based methods in computational speed. Beyond WFR, SUDO supports asymmetric penalties that encode proliferation-dominant priors and produce more plausible trajectories and growth estimates on synthetic and single-cell datasets.
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All authors Junda Ying, Yuxuan Wang, Bowen Yang, Peijie Zhou, Lei Zhang

Thursday 3 September

Quantitative Methods

A Network-Based Biomarker of Morphological Disruption Associated with Breast Cancer Malignancy

Reza Bozorgpour

Breast cancer diagnosis commonly considers individual nuclear morphological characteristics, whereas their joint organization is less frequently evaluated. We developed the Morphological Network Disruption Index (MNDI), a patient-level measure of morphological abnormality relative to a benign reference state. Using the Wisconsin Diagnostic Breast Cancer dataset, ten nuclear characteristics were represented as network nodes, with MNDI summarizing disruption across 45 pairwise feature configurations. Performance was evaluated using repeated stratified five-fold cross-validation. Malignant lesions exhibited substantially higher MNDI than benign lesions (mean: 3.454 vs. 1.165; p = 2.94e-69). MNDI achieved an AUC of 0.941 (95% CI: 0.918-0.961), with 85.9% sensitivity and 91.6% specificity. A classifier using network-derived descriptors achieved an AUC of AUC of 0.928 +/- 0.024. Independent evaluation in the BreaKHis histopathology cohort showed limited discrimination (AUC = 0.538, 95% CI: 0.401-0.668), indicating dependence on the underlying morphological representation. MNDI provides an interpretable framework for quantifying patient-specific morphological disruption, while individual implementations require validation within compatible feature spaces.
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Quantitative Methods · Cross-listed from cs.AI

When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference

Ismail Erbas, Xavier Intes and Vikas Pandey

Quantization is widely used to reduce the computational and memory demands of neural-network inference. In recurrent networks, however, the quantized state is stored and returned at the next time step, so the rule used to store that state can alter subsequent computations. Here, we introduce recurrent-state write-back to denote this rule and isolate its effect in a compact GRU encoder–decoder for fluorescence lifetime imaging, a molecular imaging modality used in quantitative biological imaging. A central task is estimating two lifetime parameters, the short-lived component {\tau}1 and the long-lived component {\tau}2, from high-noise time-resolved fluorescence signals. Holding the trained model fixed, replacing continuous state propagation with deterministic 4-bit state storage increases estimation errors for {\tau}1 and {\tau}2 by approximately 70x and 300x, respectively. Failure occurs when repeated small updates remain below the write threshold, leaving the stored state nearly fixed while the network continues to propose change. Error feedback, residual memory, and direction memory carry information from these suppressed updates across time and recover accuracy without retraining. Precision sweeps show that increasing state precision can worsen a fixed recurrent solution, while matched training shows that compatibility with the state interface can be learned. To test whether this behavior extends beyond the GRU, we repeat the post-training intervention in an independently trained LSTM, where coarse write-back reproduces the failure, error feedback restores accuracy, and state-specific interventions reveal greater sensitivity of the cell state than the hidden state. Our results establish recurrent-state write-back as a key determinant of low-precision recurrent dynamics and identify the state-storage interface as a central design consideration for quantized recurrent inference.
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Quantitative Methods

Towards AI-Driven Nanomedicine Discovery: A Benchmark and Multimodal Learning Framework for Nano Self-Assembly Prediction

Quan Hao, Mengyue Fan, Zifan Dong et al. (+9 more)

Nano self-assembly organizes molecular components into bioactive nanoscale structures. Self-assembled nanoparticles (NAPs) derived from Chinese herbal formulas and applications such as anti-lung-cancer therapy demonstrate the substantial potential of self-assembly for nanomedicine discovery. Yet discovery still relies on costly wet-lab screening, while existing machine learning approaches lack standardized tasks, effective pairwise compatibility modeling, and public benchmarks with unified evaluation. To address these limitations, we formalize NSA prediction as a binary classification task for predicting self-assembly between molecular pairs and then establish NSA-Bench, the first public benchmark with curated molecular combinations, experimental conditions, self-assembly labels, and standardized evaluation protocols. We further develop NSA-Net, an interaction-aware multimodal framework that integrates complementary molecular evidence from graph topology, sequence semantics, and physicochemical descriptors to learn molecular-pair representations for self-assembly prediction. Extensive experiments on NSA-Bench show that NSA-Net achieves a ROC-AUC of $0.9470\pm0.0112$ (Small) and $0.9492\pm0.0062$ (Large). On the Small track, it surpasses the strongest machine-learning and graph-based baselines by 3.9 and 17.1 percentage points, respectively. Representation analyses reveal interpretable molecular characteristics associated with self-assembly prediction captured by the learned representations. Moreover, an NSA-Agent case study further demonstrates how NSA-Net predictions can support formulation refinement through experimental-condition-aware reasoning. Our code is available at https://github.com/developer-hq/NSA-Net.
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All authors Quan Hao, Mengyue Fan, Zifan Dong, Jianduo Zhao, Changhao Xiao, Shangqing Jiao, Hao Zhang, Yudong Wang, Fei Xia, Jigang Wang, Liguo Zhang, Chong Qiu

Quantitative Methods

The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning

Anna Rosenberg, Stéphanie Laurent, Esther Morandeau et al. (+2 more)

Forensic body fluid identification is crucial for reconstructing crime scene events. While DNA analysis provides individualization, it lacks information about the fluid's origin. We developed and evaluated three complementary proteomic approaches using LC-HRMS/MS to identify blood, saliva, semen, urine, and vaginal fluid, including complex mixtures. The first method utilized fluid-specific peptide biomarkers, achieving high accuracy for pure fluids. The second employed peptide abundance ratios, demonstrating effectiveness in body fluid mixtures. The third, a machine learning model using Classifier Chain Random Forest, achieved 100% accuracy for pure fluids and promising results for mixtures. Our results revealed the complementarity of different tests, with the peptide-specific biomarker and machine-learning approaches being the most robust. This study demonstrates the potential of proteomics for comprehensive body fluid identification, offering valuable tools for forensic investigations.
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All authors Anna Rosenberg, Stéphanie Laurent, Esther Morandeau, Alix Munoz, Joelle Vinh

Wednesday 2 September

Quantitative Methods

Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA

Michał Kulczykowski and Rafał Łabędzki

Self-supervised pretraining has transformed language and vision, but its value for molecular graph neural networks remains contested. We ask whether pretraining on a large unlabelled corpus improves molecular property prediction. We adapt LeJEPA, a predictor-free joint-embedding predictive architecture regularised by Sketched Isotropic Gaussian Regularisation (SIGReg), to molecular graphs, evaluating GPS and Chemprop-style D-MPNN encoders on the Wong et al. [1] antibiotic-activity dataset and ogbg-molhiv using a multi-seed, bootstrap-based protocol. Pretraining improves learned representations but does not robustly improve finetuning. A frozen probe on pretrained embeddings exceeds random initialisation on both tasks (ogbg-molhiv ROC-AUC 0.788 vs 0.665; +0.123), reaching the published self-supervised band, but this does not translate into finetuning gains. On the antibiotic scaffold split, a canonical partition is significant (delta AUPRC +0.041, p = 0.010), but the effect vanishes across five partitions (pooled +0.013, p = 0.095). Finetuning is null on the random split, ogbg-molhiv, and D-MPNN. The representational edge is nevertheless recoverable. Embeddings saturate at ~16-32 effective dimensions, whereas Morgan fingerprints improve to 1024 bits. At matched dimensionality, fingerprints lead validation (0.799 vs 0.782 at 128 dimensions) but trail shifted test scaffolds (0.759 vs 0.788). Truncating embeddings and combining them with a 1024-bit Morgan fingerprint raises ogbg-molhiv ROC-AUC from 0.805 to 0.832 (delta +0.027; 95% CI [+0.003, +0.054]; p = 0.014); an untrained encoder gains nothing (delta -0.003). Thus, pretraining supplies complementary information best realised through feature-level combination, while finetuning gains are weak and partition-dependent.
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Quantitative Methods

Sparse concept attribution for histomorphological hypothesis generation from whole-slide classifiers

Tristan Lazard, Kenza Bouzid, Julius Hense et al. (+6 more)

Histology images contain rich morphological information and can provide insights into pathological processes. However, deriving hypotheses relating morphological phenotypes to clinical attributes is bottlenecked by a manual image interpretation step. Here, we demonstrate that this process can be automated through interpretable deep learning. We present SCOPE, a method to interpret slide-level classifiers by combining pathology-specific vision–language models with sparse concept attribution onto a generalist histomorphological concept bank. To measure whether such explanations recover known morphology, we introduce MorphoRecoveryBench, a benchmark of seven tasks with pathologist-curated reference descriptions. On this benchmark, dense concept attribution is indistinguishable from a random baseline, whereas sparse attribution recovers substantial known morphology; decomposing the pooled slide embedding reaches similar explanation correctness at a fraction of the computational cost. Post-hoc interpretation of whole-slide classifiers can thus generate morphological hypotheses at scale, for expert validation.
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All authors Tristan Lazard, Kenza Bouzid, Julius Hense, Shruthi Bannur, Daniel Coelho de Castro, Daniel Shao, Rajesh Jena, Drew Williamson, Stephanie Hyland

Quantitative Methods

SurfSpec: Enhancing Off-Target-Agnostic Specificity by Bounding Pocket-Ligand Geometric Mismatch

Minyeong Hwang, Yoorim Gang, Ziseok Lee et al. (+5 more)

Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. However, existing affinity-driven methods do not explicitly control specificity, whereas current specificity-aware approaches commonly require prior knowledge of off-target structures. We address off-target-agnostic specificity-aware lead optimization by analyzing the geometric mismatch between a ligand and the target pocket. We provide a conservative specificity lower bound for geometrically separated off-targets without requiring access to off-target structures. By metricizing pocket–ligand mismatch, the triangle inequality shows that reducing target–ligand mismatch improves a conservative lower bound on mismatch to a separated off-target class, which can be translated into a specificity lower bound through an empirical geometry–affinity calibration. Motivated by this analysis, we introduce SurfSpec, an off-target-agnostic lead optimization framework that iteratively grows ligands toward under-occupied regions of the target pocket surface. SurfSpec alternates between linker generation toward selected target-surface patches, which provides geometric pseudo-labels, and refinement under a pocket-conditioned ligand prior, which restores these pseudo-labels into valid ligands. On the CrossDocked2020 test set, SurfSpec reduces geometric mismatch and outperforms evaluated off-target-agnostic lead optimization baselines in empirical specificity, while maintaining competitive target-affinity improvement.
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All authors Minyeong Hwang, Yoorim Gang, Ziseok Lee, Wooyeol Lee, Young Bin Park, Jae-Mun Choi, Kyungsu Kim, Eunho Yang

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

An adaptive time-tree transition kernel for Bayesian phylogenetic inference

Marius Brusselmans, Guy Baele, Samuel L. Hong et al. (+4 more)

Bayesian phylogenetic and phylodynamic analyses can be very time-consuming, owing to the combination of complex models that are used to estimate key parameters from increasingly large genomic data sets and their associated metadata. The use of high-performance computer hardware can – to a certain extent – alleviate the computational burden and markedly decrease the time to results. Still, even converging to the posterior can be a lengthy endeavour, with the burn-in aspect of such analyses potentially taking days or even weeks for large data sets. One of the key aspects that hampers performance in Bayesian phylogenetic inference is the efficiency with which tree topology proposals explore tree space. We here propose a novel adaptive tree transition kernel, which we call `subTreeLeap' (STL), which involves modifying the phylogeny by walking along patristic distance paths in the tree according to an adaptable radius parameter. STL is a general proposal, which can be used with contemporaneous or time-calibrated sequence data, being particularly suited to the latter due to respecting temporal precedence constraints. We carefully assess its impact on convergence and statistical mixing of the exploration of posterior tree space, by comparison to replicate “golden runs” obtained from lengthy analyses of empirical data under standard tree transition kernels. We find that STL successfully explores the same posterior tree space as standard kernels, but often does so in a more efficient manner. We discuss limitations as well as future potential improvements to STL that could substantially increase the speed at which Bayesian phylogenetic inferences are obtained.
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All authors Marius Brusselmans, Guy Baele, Samuel L. Hong, Jiansi Gao, Marc A. Suchard, Andrew Rambaut, Luiz Max Carvalho

Tuesday 1 September

Quantitative Methods

A mechanistic modeling framework to interpret ACTH stimulation tests across HPA axis adaptation states and glucocorticoid feedback dynamics

Mamta Yadav and Phool Singh

The hypothalamic pituitary adrenal (HPA) axis is a key regulatory system coordinating endocrine responses to physiological and psychological stress. While the ACTH stimulation test remains a cornerstone of adrenal function assessment, its interpretation is complicated by the dynamic and adaptive nature of the HPA axis under chronic stress exposure. In particular, prolonged stress induces glandular remodeling, glucocorticoid receptor (GR) resistance and delayed feedback recovery, all of which may alter test outcomes without indicating primary adrenal failure. In this study, we present a mechanistic modeling framework that integrates hormonal kinetics, feedback inhibition and functional mass adaptation of the corticotroph and adrenal compartments. We simulate the HPA axis over $180$ days encompassing three phases - baseline, chronic stress and recovery, while introducing a time varying GR resistance function to mimic feedback desensitization and its resolution. Using this framework, we evaluate both low dose ($1 \mu g)$ and high dose ($250 \mu g)$ ACTH stimulation tests across physiological phases. Our simulations show that cortisol responses are highly sensitive to both the magnitude and timing of stress exposure and that ACTH responsiveness is phase dependent and often blunted during recovery due to persistent feedback resistance. Low dose ACTH testing more reliably reflects partial adrenal adaptation, while high dose tests risks masking dysfunction due to supraphysiological drive. These results highlight the limitations of static testing paradigms and suggest that accounting for glandular plasticity and GR feedback dynamics is essential for effective endocrine diagnosis particularly in stress related or treatment induced adrenal disorders.
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Quantitative Methods · Cross-listed from math.NA

Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking

Siming Liang, Jacob Hauck, Minglei Yang et al. (+3 more)

Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance data. Agent-based models (ABMs) are attractive for this task because they represent individual behavior, contact heterogeneity, and localized interventions, but these same features make them difficult to calibrate online. We develop a generative AI-based data-assimilation (GenDA) framework for partially observed epidemic ABMs that estimates both the epidemic state and a heterogeneous parameter field while respecting the gap between observable macrostates and latent agent-level microstates. GenDA combines a training-free, score-based generative update for macrostate correction with a direct parameter update based on macrostate discrepancies, followed by a macro-micro reassignment step that restores consistency with the ABM. In controlled and geographically explicit synthetic experiments, the framework recovers regional epidemic burden, dominant hotspot structures, and effective transmission heterogeneity from aggregated observations, while improving post-assimilation forecasts relative to state-only assimilation.
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All authors Siming Liang, Jacob Hauck, Minglei Yang, Adam Spannaus, Heidi Hanson, Guannan Zhang

Quantitative Methods

On the interpretation of the kinetics of ligand-receptor binding

David Colquhoun and James P Higham

When the rates of ligand binding are measured by methods such as surface plasmon resonance, it is common practice to use the observed rate constants for the onset and offset of binding to estimate an equilibrium constant for ligand binding. If this agrees with the equilibrium constant found as the EC50 for binding at equilibrium, this is taken as validation of the measured rates. This is correct only when binding produces no conformation change in the receptor, and ligand binding follows a single exponential time course. Here, we investigate a simple 3 state model in which binding is followed by a conformation change in the receptor. Three special cases of this model in which the time course of onset and offset of ligand binding are close to being single exponentials are analysed. These cases are (1) when binding is much faster than the conformation change, (2) when the conformation change is much faster than binding, and (3) when the rates of ligand dissociation and receptor activation are both fast. It is concluded that the measured rates will often yield an estimate of the equilibrium constant for ligand binding that is close to the effective, or macroscopic, equilibrium constant, the EC50 found by measuring binding at equilibrium, which depends on both of the underlying microscopic equilibrium constants describing ligand binding and the conformation change. The exception to this conclusion is the case when binding is much faster than the subsequent conformation change, though the estimate of the equilibrium constant for ligand binding still depends on both of the underlying microscopic equilibrium constants.
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Quantitative Methods

Efficiently classifying shocks in complex systems requires dormant reporters

David A. Brewster and Philippe Cluzel

Many natural and engineered systems are large complex networks of interacting components, and external perturbations drive them along different dynamical paths. Identifying which perturbation occurred matters for diagnosis, control, and prediction. Yet often times only a few components can be jointly monitored. Which components should be monitored? Experimental practice usually favors placing reporters at the most sensitive sites, where perturbations produce the largest effects. Using a simple dynamical model for complex systems with heterogeneous connectivity, we ask how sparse reporter panels should be chosen to classify shocks from partial trajectories when repeated trials only approximately reproduce an ideal initial condition. Once that reproduction is imperfect, sensitivity ranked panels fall far short of optimal, and the shortfall grows with the noise. We find that the best panels mix two kinds of reporters. A promiscuous reporter responds to most shocks, so it separates them mainly by degree, and degree fluctuates from trial to trial. A dormant reporter responds to only a few shocks but strongly, and its answers do not scatter as much between trials. As noise grows, the cost of losing a dormant reporter rises to meet the cost of losing a promiscuous one. Panels of either kind alone classify worse than the mixture, and no property of the members collected individually explains the ordering. Most of all, we find that only a minuscule number of reporters are needed on a panel to accurately identify which shock hit the system. We implement an efficient algorithm to assemble such a panel. Together these results provide a low cost practical design principle for monitoring large complex dynamical systems.
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Quantitative Methods

Operationalizing open-ended biological discovery across single-cell representations

Ningxuan Zhang, Ziwei Wang, Ning Xie et al. (+1 more)

Single-cell studies are typically initiated from predefined research questions, leaving much of the biological information encoded within existing data unexplored. We formalize open-ended discovery as an analytical paradigm, in which data-derived signals are identified before biological context is interrogated and subsequently evaluated according to their potential to justify prospective experimental investment. Here we develop PROSPECTor, an end-to-end framework that searches for reproducible biological structures across conventional expression representations and diverse foundation-model embeddings, translating robust signals into quantitatively testable candidate hypotheses. Projection into unseen datasets then evaluates their generalizability and phenotype association, providing a scalable screen for candidates that warrant prospective validation. Supported signals emerged from different representation spaces and search strategies. PROSPECTor-nominated hypotheses were then examined in independent biological settings: fibroblast extracellular-matrix programmes demonstrated transferability to an independent mouse cohort with an intervention context, while a patient-resolved gastric-cancer T-cell programme recurred across single-cell, bulk and spatial cohorts. PROSPECTor establishes an auditable framework for systematically revisiting single-cell datasets across expanding representation spaces, turning retrospective collections into prospective resources for biological discovery that can motivate new research questions.
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All authors Ningxuan Zhang, Ziwei Wang, Ning Xie, Na Liu