pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the effectiveness of multimodal data in forecasting SARS-COV-2 case surges
Authors: Palur Venkata Raghuvamsi, Siyuan Brandon Loh, Prasanta Bhattacharya, Joses Ho, Raphael Lee Tze Chuen, Alvin X. Han, Sebastian Maurer-Stroh
Date: 2025-05-28 00:00:00

The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence and fatality rates. These predictions were instrumental in enabling timely public health interventions that helped break transmission cycles. While most existing models are grounded in traditional epidemiological data, the potential of alternative datasets, such as those derived from genomic information and human behavior, remains underexplored. In the current study, we investigated the usefulness of diverse modalities of feature sets in predicting case surges. Our results highlight the relative effectiveness of biological (e.g., mutations), public health (e.g., case counts, policy interventions) and human behavioral features (e.g., mobility and social media conversations) in predicting country-level case surges. Importantly, we uncover considerable heterogeneity in predictive performance across countries and feature modalities, suggesting that surge prediction models may need to be tailored to specific national contexts and pandemic phases. Overall, our work highlights the value of integrating alternative data sources into existing disease surveillance frameworks to enhance the prediction of pandemic dynamics.
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly
Authors: Rohan V. Koodli, Alexander S. Powers, Ayush Pandit, Chiho Im, Ron O. Dror
Date: 2025-05-27 00:00:00
Tasks: Denoising, Drug Design, Drug Discovery, Scene Understanding

A common starting point for drug design is to find small chemical groups or "fragments" that form interactions with distinct subregions in a protein binding pocket. The subsequent challenge is to assemble these fragments into a molecule that has high affinity to the protein, by adding chemical bonds between atoms in different fragments. This "molecule assembly" task is particularly challenging because, initially, fragment positions are known only approximately. Prior methods for spatial graph completion-adding missing edges to a graph whose nodes have associated spatial coordinates-either treat node positions as fixed or adjust node positions before predicting edges. The fact that these methods treat geometry and topology prediction separately limits their ability to reconcile noisy geometries and plausible connectivities. To address this limitation, we introduce EdGr, a spatial graph diffusion model that reasons jointly over geometry and topology of molecules to simultaneously predict fragment positions and inter-fragment bonds. Importantly, predicted edge likelihoods directly influence node position updates during the diffusion denoising process, allowing connectivity cues to guide spatial movements, and vice versa. EdGr substantially outperforms previous methods on the molecule assembly task and maintains robust performance as noise levels increase. Beyond drug discovery, our approach of explicitly coupling geometry and topology prediction is broadly applicable to spatial graph completion problems, such as neural circuit reconstruction, 3D scene understanding, and sensor network design.
Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Authors: Esra Adiyeke, Tianqi Liu, Venkata Sai Dheeraj Naganaboina, Han Li, Tyler J. Loftus, Yuanfang Ren, Benjamin Shickel, Matthew M. Ruppert, Karandeep Singh, Ruogu Fang, Parisa Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti
Date: 2025-05-27 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning (RL)

Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system generating treatment recommendations based on patient states can be a substantial asset in perioperative decision-making, as in cases of intraoperative hypotension, for which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. We developed a Reinforcement Learning (RL) model to recommend optimum dose of intravenous (IV) fluid and vasopressors during surgery to avoid intraoperative hypotension and postoperative AKI. We retrospectively analyzed 50,021 surgeries from 42,547 adult patients who underwent major surgery at a quaternary care hospital between June 2014 and September 2020. Of these, 34,186 surgeries were used for model training and 15,835 surgeries were reserved for testing. We developed a Deep Q-Networks based RL model using 16 variables including intraoperative physiologic time series, total dose of IV fluid and vasopressors extracted for every 15-minute epoch. The model replicated 69% of physician's decisions for the dosage of vasopressors and proposed higher or lower dosage of vasopressors than received in 10% and 21% of the treatments, respectively. In terms of IV fluids, the model's recommendations were within 0.05 ml/kg/15 min of the actual dose in 41% of the cases, with higher or lower doses recommended for 27% and 32% of the treatments, respectively. The model resulted in a higher estimated policy value compared to the physicians' actual treatments, as well as random and zero-drug policies. AKI prevalence was the lowest in patients receiving medication dosages that aligned with model's decisions. Our findings suggest that implementation of the model's policy has the potential to reduce postoperative AKI and improve other outcomes driven by intraoperative hypotension.
Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics
Authors: Marie Steinacker, Yuri Kheifetz, Markus Scholz
Date: 2025-05-27 00:00:00

Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challenge in clinical practice due to its high inter-patient variability and limited predictability. Current mechanistic models often struggle to accurately forecast outcomes for patients with irregular or atypical trajectories. In this study, we develop and compare hybrid mechanistic and data-driven approaches for individualized time series modeling of platelet counts during chemotherapy. We consider hybrid models that combine mechanistic models with neural networks, known as universal differential equations. As a purely data-driven alternative, we utilize a nonlinear autoregressive exogenous model using gated recurrent units as the underlying architecture. These models are evaluated across a range of real patient scenarios, varying in data availability and sparsity, to assess predictive performance. Our findings demonstrate that data-driven methods, when provided with sufficient data, significantly improve prediction accuracy, particularly for high-risk patients with irregular platelet dynamics. This highlights the potential of data-driven approaches in enhancing clinical decision-making. In contrast, hybrid and mechanistic models are superior in scenarios with limited or sparse data. The proposed modeling and comparison framework is generalizable and could be extended to predict other treatment-related toxicities, offering broad applicability in personalized medicine.
Mathematical Modelling and Optimisation of Athletic Performance: Tapering and Periodisation
Authors: David Ceddia, Howard Bondell, Peter Taylor
Date: 2025-05-27 00:00:00

We conduct a mathematical optimisation of the training load to maximise performance for two seminal athletic performance models: the Banister et al. 1975 Fitness-Fatigue Impulse Response Model and the Busso 2003 Variable Dose-Response Model. We discuss discrepancies in the general trends of the optimised training loads compared to common training practices recommended in the sports science literature, such as tapering and periodisation. We then propose a set of interpretable nonlinear modifications in the magnitude and time response to training in the fitness-fatigue model such that the optimised training load demonstrates these trends.
Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction
Authors: Mahdi Pourmirzaei, Farzaneh Esmaili, Salhuldin Alqarghuli, Mohammadreza Pourmirzaei, Ye Han, Kai Chen, Mohsen Rezaei, Duolin Wang, Dong Xu
Date: 2025-05-26 00:00:00
Tasks: Decoder, Multi-Task Learning

The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions, from sequence-level properties and residue-specific attributes to complex inter-protein interactions, into a standardized next-token prediction format. At its core, Prot2Token employs an autoregressive decoder, conditioned on embeddings from pre-trained protein encoders and guided by learnable task tokens, to perform diverse predictions. This architecture uniquely facilitates multi-task learning, enabling a single model to master numerous tasks with improved efficiency. We present extensive experimental validation across a variety of benchmarks, demonstrating Prot2Tokens strong predictive power in different types of protein-prediction tasks. Key results include significant speedups (e.g., near 1000x over AlphaFold2 with MSA) and performance often matching or exceeding specialized approaches. Beyond that, we introduce an auxiliary self-supervised decoder pre-training approach to improve spatially sensitive task performance. Prot2Token thus offers a significant step towards a versatile, high-throughput paradigm for protein modeling, promising to accelerate biological discovery and the development of novel therapeutics. The code is available at https://github.com/mahdip72/prot2token .
A Comparison of Bacterial Colonies Count from Petri Dishes Utilizing Hough Transform and Traditional Manual Counting
Authors: Areesha Rehman, Zikria Saleem, Jarrar Amjad, Syed Rehan Shah, Kamran Siddique
Date: 2025-05-26 00:00:00

Bacterial colony enumeration is an essential stage in microbiological research, allowing susceptibility to antibiotics assessment, monitoring of the environment, and clinical diagnostics. Traditional manual counting methods are costly and susceptible to human mistakes, prompting the creation of automated detection systems. This research compares the efficacy of the Hough Circle Transform method for automated colony detection to hand counting of E. coli, S. aureus, and P. aeruginosa colonies on 200 petri plates. These bacteria are among the most clinically relevant pathogens, with E. coli frequently causing urinary tract infections, S. aureus connected with skin and bloodstream infections, and P. aeruginosa a significant issue in hospital-acquired infections. When colonies were counted automatically without visual correction, the mean difference from manual counts was 59.7%, with overestimation and underestimation occurring in 29% and 45% of cases, whereas S. aureus and P. aeruginosa had higher error rates. The proposed methodology achieved an overall accuracy of 95% for E. coli, 90% for S. aureus, and 84% for P. aeruginosa, with associated recall values of 95%, 91%, and 86%. The F-measure remained continuously high, ranging between 0.85 and 0.95. Regarding efficiency, manual counting required an average of 70 seconds per plate, while automated counting without and with visual correction took 30 seconds. Despite issues with segmentation in high-density plates, automated approaches offer a potential approach to high-throughput bacterial enumeration by decreasing labor-intensive operations while retaining adequate accuracy. Future research should enhance colony algorithmic segmentation and picture preprocessing approaches to improve detection performance, especially on crowded petri plates.
Data-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares
Authors: Aurora Poggi, Giuseppe Alessio D'Inverno, Hjalmar Brismar, Ozan Öktem, Matthieu Barreau, Kateryna Morozovska
Date: 2025-05-23 00:00:00
Tasks: Density Estimation, parameter estimation, Uncertainty Quantification

Data-driven discovery of dynamics in biological systems allows for better observation and characterization of processes, such as calcium signaling in cell culture. Recent advancements in techniques allow the exploration of previously unattainable insights of dynamical systems, such as the Sparse Identification of Non-Linear Dynamics (SINDy), overcoming the limitations of more classic methodologies. The latter requires some prior knowledge of an effective library of candidate terms, which is not realistic for a real case study. Using inspiration from fields like traffic density estimation and control theory, we propose a methodology for characterization and performance analysis of calcium delivery in a family of cells. In this work, we compare the performance of the Constrained Regularized Least-Squares Method (CRLSM) and Physics-Informed Neural Networks (PINN) for system identification and parameter discovery for governing ordinary differential equations (ODEs). The CRLSM achieves a fairly good parameter estimate and a good data fit when using the learned parameters in the Consensus problem. On the other hand, despite the initial hypothesis, PINNs fail to match the CRLSM performance and, under the current configuration, do not provide fair parameter estimation. However, we have only studied a limited number of PINN architectures, and it is expected that additional hyperparameter tuning, as well as uncertainty quantification, could significantly improve the performance in future works.
Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures
Authors: Yu-Chien Ning, Xin Zhou, Francine Laden, Molin Wang
Date: 2025-05-23 00:00:00
Tasks: Ensemble Learning, Epidemiology

We introduce the SoftBart approach from Bayesian ensemble learning to estimate the relationship between multipollutant mixtures and health on chronic exposures in epidemiology research. This approach offers several key advantages over existing methods: (1) it is computationally efficient and well-suited for analyzing large datasets; (2) it is flexible in estimating various correlated nonlinear functions simultaneously; and (3) it accurately identifies active variables within highly correlated multipollutant mixtures. Through simulations, we demonstrate the method's superiority by comparing its accuracy in estimating and quantifying uncertainties for both main and interaction effects with the commonly used method, BKMR. Last, we apply the method to analyze a multipollutant dataset with 10,110 participates from the Nurses' Health Study.
A graph-based approach for modification site assignment in proteomics
Authors: Dafni Skiadopoulou, Lukas Käll, Harald Barsnes, Veit Schwämmle, Marc Vaudel
Date: 2025-05-23 00:00:00

Background In proteomics, the most probable localizations of post-translational modifications are assessed by localization scores evaluating the likelihood of a given modification to occupy a site on a peptide sequence. When identifying highly modified peptides, localization scores for different modifications can return conflicting results, stacking modifications on the same amino acid. Here, we propose a graph-based approach that assigns modifications to sites in a way that maximizes localization scores while avoiding conflicting assignments. Results The algorithm is implemented as both a standalone Python program and in the compomics-utilities Java library. Our graph-based approach showed the ability to match complex combinations of modifications and acceptor sites, allowing the processing of thousands of peptides in a few seconds. Conclusions Our graph-based approach to modification site assignment allows distributing multiple modifications in a way that maximizes individual localization scores. Having an optimal modification site assignment is important for spectrum annotation and biological interpretation.
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
Authors: Alyson East, Elizabeth G. Campolongo, Luke Meyers, S M Rayeed, Samuel Stevens, Iuliia Zarubiieva, Isadora E. Fluck, Jennifer C. Girón, Maximiliane Jousse, Scott Lowe, Kayla I Perry, Isabelle Betancourt, Noah Charney, Evan Donoso, Nathan Fox, Kim J. Landsbergen, Ekaterina Nepovinnykh, Michelle Ramirez, Parkash Singh, Khum Thapa-Magar, Matthew Thompson, Evan Waite, Tanya Berger-Wolf, Hilmar Lapp, Paula Mabee, Graham Taylor, Sydne Record
Date: 2025-05-22 00:00:00

Biological collections house millions of specimens documenting Earth's biodiversity, with digital images increasingly available through open-access platforms. Most imaging protocols were developed for human visual interpretation without considering computational analysis requirements. This paper aims to bridge the gap between current imaging practices and the potential for automated analysis by presenting key considerations for creating biological specimen images optimized for computer vision applications. We provide conceptual computer vision topics for context, addressing fundamental concerns including model generalization, data leakage, and comprehensive metadata documentation, and outline practical guidance on specimen imagine, and data storage. These recommendations were synthesized through interdisciplinary collaboration between taxonomists, collection managers, ecologists, and computer scientists. Through this synthesis, we have identified ten interconnected considerations that form a framework for successfully integrating biological specimen images into computer vision pipelines. The key elements include: (1) comprehensive metadata documentation, (2) standardized specimen positioning, (3) consistent size and color calibration, (4) protocols for handling multiple specimens in one image, (5) uniform background selection, (6) controlled lighting, (7) appropriate resolution and magnification, (8) optimal file formats, (9) robust data archiving strategies, and (10) accessible data sharing practices. By implementing these recommendations, collection managers, taxonomists, and biodiversity informaticians can generate images that support automated trait extraction, species identification, and novel ecological and evolutionary analyses at unprecedented scales. Successful implementation lies in thorough documentation of methodological choices.
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modeling
Authors: Jiale Tan, Marisa C. Eisenberg
Date: 2025-05-22 00:00:00
Tasks: Model Selection, parameter estimation, Time Series, Time Series Analysis

To be fully useful for public health practice, models for epidemic response must be able to do more than predict -- it is also important to incorporate the mechanisms underlying transmission dynamics to enable policymakers and practitioners to be able to evaluate what-if scenarios and intervention options. However, most mechanistic models suffer from uncertainty in both the parameters (e.g., parameter unidentifiability) and the model structure itself, which can hinder both successful parameter estimation and model interpretation. To enable rapid development of interpretable and parsimonious mechanistic models, we use penalized regression and covariate selection methods to integrate parameter identifiability and model selection directly into the parameter estimation procedure for (in this case) traditional ordinary differential equation (ODE) models. For both simulated and real-world epidemiological data, we demonstrate that the LASSO-ODE framework is highly effective in selecting a parsimonious, identifiable model from larger, more realistic but potentially unidentifiable models, from realistically sparse data with only a single measured compartment and multiple latent (unobserved) variables. While we focus on epidemic models in this paper as a case study, these same approaches are applicable to a wide range of application areas that are faced with relatively sparse data but a need for realistic mechanistic models (e.g. mathematical oncology and mathematical biology more broadly). Additionally, the cross-validation techniques designed for time series data introduced in our study can be used across a range of time series analysis and modeling approaches.
Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio
Authors: Gertrude Hattoh, Jeremiah Ayensu, Nyarko Prince Ofori, Solomon Eshun, Darlington Akogo
Date: 2025-05-22 00:00:00
Tasks: Drug Discovery

Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This reinforces concerns about the potential misuse of LLMs in biodesign, posing a significant threat to research and development (R&D). The accessibility of such technology to individuals with limited technical expertise raises serious biosecurity risks. Our findings underscore the critical need for robust governance and technical safeguards to balance rapid biotechnological innovation with biosecurity imperatives.
Learning collective multi-cellular dynamics from temporal scRNA-seq via a transformer-enhanced Neural SDE
Authors: Qi Jiang, Lei Zhang, Longquan Li, Lin Wan
Date: 2025-05-22 00:00:00
Tasks: Benchmarking, Time Series

Time-series single-cell RNA-sequencing (scRNA-seq) datasets offer unprecedented insights into the dynamics and heterogeneity of cellular systems. These systems exhibit multiscale collective behaviors driven by intricate intracellular gene regulatory networks and intercellular interactions of molecules. However, inferring interacting cell population dynamics from time-series scRNA-seq data remains a significant challenge, as cells are isolated and destroyed during sequencing. To address this, we introduce scIMF, a single-cell deep generative Interacting Mean Field model, designed to learn collective multi-cellular dynamics. Our approach leverages a transformer-enhanced stochastic differential equation network to simultaneously capture cell-intrinsic dynamics and intercellular interactions. Through extensive benchmarking on multiple scRNA-seq datasets, scIMF outperforms existing methods in reconstructing gene expression at held-out time points, demonstrating that modeling cell-cell communication enhances the accuracy of multicellular dynamics characterization.Additionally, our model provides biologically interpretable insights into cell-cell interactions during dynamic processes, offering a powerful tool for understanding complex cellular systems.