pwc-archive/papers-with-abstracts huggingface dataset:
Applied Antifragility in Natural Systems: Evolutionary Antifragility
Authors: Cristian Axenie, Roman Bauer, Oliver Lopez Corona, Elvia Ramirez-Carrillo, Ari Barnett, Jeffrey West
Date: 2025-04-21 00:00:00

This chapter introduces evolutionary antifragility as the time-scale interaction characteristics of a natural dynamic system. It describes the benefit derived from input distribution unevenness, based on the emergent system dynamics and its uncertain and volatile interactions with the operating environment described by unknown disturbances. We consider methods for the detection, analysis, and modelling of cancer, environmental, microbiota, and social systems antifragility. This is a preprint of the following work Cristian Axenie, Roman Bauer, Oliver Lopez Corona, Jeffrey West, Applied Antifragility in Natural Systems From Principles to Applications, Springer Nature reproduced with the permissions of Springer Nature. The final authenticated version is available online at https://link.springer.com/book/9783031903908
Optimizing chemoradiotherapy for malignant gliomas: a validated mathematical approach
Authors: Miguel Perales-Patón, Matteo Italia, María Castelló-Pons, Irene Gómez-Soria, Juan Belmonte-Beitia, Pilar Sánchez-Gómez, Víctor M. Pérez-García
Date: 2025-04-24 00:00:00
Tasks: Prognosis

Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis. Maximal safe resection and the so-called Stupp protocol are the standard first-line therapies. Despite combining radiotherapy and chemotherapy in an intensive manner, it provides limited survival benefits over radiation therapy alone, underscoring the need for innovative therapeutic strategies. Emerging evidence suggests that alternative dosing schedules, such as less aggressive regimens with extended intervals between consecutive treatment applications, may improve outcomes, enhancing survival, delaying the emergence of resistance, and minimizing side effects. In this study, we develop, calibrate, and validate in animal models a novel ordinary differential equation-based mathematical model, using in vivo data to describe MG dynamics under combined chemoradiotherapy. The proposed model incorporates key biological processes, including cancer cell dormancy, phenotypic switching, drug resistance through persister cells, and treatment-induced effects. Through in silico trials, we identified optimized combination treatment protocols that may outperform the standard Stupp protocol. Finally, we computationally extrapolated the results obtained from the in vivo animal model to humans, showing up to a four-fold increase in median survival with protracted administration protocols in silico. Although further experimental and clinical validation is required, our framework provides a computational foundation to optimize and personalize treatment strategies for MG and potentially other cancers with similar biological mechanisms.
A guide to modeling plasmid co-infection dynamics
Authors: Berit Siedentop, Jana S. Huisman, Claudia Igler
Date: 2025-04-23 00:00:00

Mobile genetic elements like conjugative plasmids play a crucial role in shaping the genetic content and population dynamics of bacterial species. Bacterial populations often contain not one, but multiple co-circulating MGEs, which modify each other's population dynamics in a myriad of ways. Mathematical modeling is a powerful tool to gain intuition about the expected eco-evolutionary dynamics in a biological system with many interacting players. Here, we detail how to develop a mathematical model of plasmid co-infection, how to implement this computationally, and we give examples of what can be learned from such models.
SLiM-Gym: Reinforcement Learning for Population Genetics
Authors: Niko Zuppas, Bryan C. Carstens
Date: 2025-04-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce SLiM-Gym, a Python package for integrating reinforcement learning (RL) with forward-time population genetic simulations. Wright-Fisher evolutionary dynamics offer a tractable framework for modeling populations across discrete generations, yet applying RL to these systems requires a compatible training environment. SLiM-Gym connects the standardized RL interface provided by Gymnasium with the high-fidelity evolutionary simulations of SLiM, allowing agents to interact with evolving populations in real time. This framework enables the development and evaluation of RL-based strategies for understanding evolutionary processes.
Generating heterogeneous data on gene trees
Authors: Martí Cortada Garcia, Adrià Diéguez Moscardó, Marta Casanellas
Date: 2025-04-22 00:00:00

We introduce GenPhylo, a Python module that simulates nucleotide sequence data along a phylogeny avoiding the restriction of continuous-time Markov processes. GenPhylo uses directly a general Markov model and therefore naturally incorporates heterogeneity across lineages. We solve the challenge of generating transition matrices with a pre-given expected number of substitutions (the branch length information) by providing an algorithm that can be incorporated in other simulation software.
Quantifying scale-free behaviors in Rock-Paper-Scissors Models as a function of Mobility
Authors: D. Bazeia, M. Bongestab, M. J. B. Ferreira, B. F. de Oliveira, J. E. B. Santos
Date: 2025-04-20 00:00:00

We investigate the scale-free behavior of the spatial rock-paper-scissors model with May-Leonard dynamics, analyzing specific quantifiers that engender the power-law feature. The main results show that an important parameter that drives the scale-free behavior is the mobility, which can be used to quantitatively describe several scale-free aspects of the model, such as the number of clusters, the characteristic length, the individuals' lifespan and its corresponding mean traveled distance. All of these are novel quantifiers of current practical interest for the study of biodiversity.
Leveraging Protein Language Model Embeddings for Catalytic Turnover Prediction of Adenylate Kinase Orthologs in a Low-Data Regime
Authors: Duncan F. Muir, Parker Grosjean, Margaux M. Pinney, Michael J. Keiser
Date: 2025-05-05 00:00:00
Tasks: Language Modeling, Language Modelling, Prediction, Protein Language Model

Accurate prediction of enzymatic activity from amino acid sequences could drastically accelerate enzyme engineering for applications such as bioremediation and therapeutics development. In recent years, Protein Language Model (PLM) embeddings have been increasingly leveraged as the input into sequence-to-function models. Here, we use consistently collected catalytic turnover observations for 175 orthologs of the enzyme Adenylate Kinase (ADK) as a test case to assess the use of PLMs and their embeddings in enzyme kinetic prediction tasks. In this study, we show that nonlinear probing of PLM embeddings outperforms baseline embeddings (one-hot-encoding) and the specialized $k_{cat}$ (catalytic turnover number) prediction models DLKcat and CatPred. We also compared fixed and learnable aggregation of PLM embeddings for $k_{cat}$ prediction and found that transformer-based learnable aggregation of amino-acid PLM embeddings is generally the most performant. Additionally, we found that ESMC 600M embeddings marginally outperform other PLM embeddings for $k_{cat}$ prediction. We explored Low-Rank Adaptation (LoRA) masked language model fine-tuning and direct fine-tuning for sequence-to-$k_{cat}$ mapping, where we found no difference or a drop in performance compared to zero-shot embeddings, respectively. And we investigated the distinct hidden representations in PLM encoders and found that earlier layer embeddings perform comparable to or worse than the final layer. Overall, this study assesses the state of the field for leveraging PLMs for sequence-to-$k_{cat}$ prediction on a set of diverse ADK orthologs.
Minimax entropy: The statistical physics of optimal models
Authors: David P. Carcamo, Nicholas J. Weaver, Purushottam D. Dixit, Christopher W. Lynn
Date: 2025-05-02 00:00:00

When constructing models of the world, we aim for optimal compressions: models that include as few details as possible while remaining as accurate as possible. But which details -- or features measured in data -- should we choose to include in a model? Here, using the minimum description length principle, we show that the optimal features are the ones that produce the maximum entropy model with minimum entropy, thus yielding a minimax entropy principle. We review applications, which range from machine learning to optimal models of biological networks. Naive implementations, however, are limited to systems with small numbers of states and features. We therefore require new theoretical insights and computational techniques to construct optimal compressions of high-dimensional datasets arising in large-scale experiments.
Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms
Authors: ShiTing Huang, Sanjay Pant, Sean McGinty, Richard Good, Ben Shelley, Ankush Aggarwal
Date: 2025-05-02 00:00:00

Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has been hypothesized as a contributing reason. However, the mechanisms of RV dysfunction after lung resection remain unclear, particularly whether change in afterload or contractility is the main cause. This study is the first to employ a lumped parameter model to simulate the effects of lung resection. The implementation of a computational model allowed us to isolate certain mechanisms that are difficult to perform clinically. Specifically, two mechanisms were compared: afterload increase and RV contractility loss. Furthermore, our rigorous approach included local and global sensitivity analyses to evaluate the effect of parameters on our results, both individually and collectively. Our results demonstrate that contractility and afterload exhibited consistent trends across various pressure and volume conditions, pulmonary artery systolic pressure, pulmonary artery diastolic pressure, and right ventricular systolic pressure showed opposite variations. The results show that post-operative RV dysfunction may result from a combination of RV contractility loss and afterload increase. Further exploration and refinement of this first computational model presented herein will help us predict RV dysfunction after lung resection and pave the way towards improving outcomes for lung cancer patients.
Digital Pathway Curation (DPC): a pipeline able to assess the reproducibility, consensus and accuracy in biomedical search retrieval by comparing Gemini, PubMed, and Scientific Reviewers
Authors: Flavio Lichtenstein, Daniel Alexandre de Souza, Carlos Eduardo Madureira Trufen, Victor Wendel da Silva Gonçalves, Juliana de Paula Bernardes, Vinicius Miranda Baroni, Carlos DeOcesano-Pereira, Leonardo Fontoura Ormundo, Fabio Augusto Labre de Souza, Olga Celia Martinez Ibañez, Nancy Starobinas, Luciano Rodrigo Lopes, Aparecida Maria Fontes, Sonia Aparecida de Andrade, Ana Marisa Chudzinski-Tavassi
Date: 2025-05-02 00:00:00

A scientific study begins with a central question, and search engines like PubMed are the first tools for retrieving knowledge and understanding the current state of the art. Large Language Models (LLMs) have been used in research, promising acceleration and deeper results. However, besides caution, they demand rigorous validation. Assessing complex biological relationships remains challenging for SQL-based tools and LLM models. Here, we introduce the Digital Pathway Curation (DPC) pipeline to evaluate the reproducibility and accuracy of the Gemini models against PubMed search and human expert curation. Using two omics experiments, we created a large dataset (Ensemble) based on determining pathway-disease associations. With the Ensemble dataset, we demonstrate that Gemini achieves high run-to-run reproducibility of approximately 99% and inter-model reproducibility of around 75%. Next, we calculate the crowdsourced consensus using a smaller dataset. The CSC allows us to calculate accuracies, and the Gemini multi-model consensus reached a significant accuracy of about 87%. Our findings demonstrate that LLMs are reproducible, reliable, and valuable tools for navigating complex biomedical knowledge.
Essential Workers at Risk: An Agent-Based Model (SAFE-ABM) with Bayesian Uncertainty Quantification
Authors: Elizabeth B. Amona, Indranil Sahoo, Ya Su, Edward L. Boone, Gwendoline Nelis, Ryad Ghanam
Date: 2025-05-02 00:00:00
Tasks: Uncertainty Quantification

Essential workers face elevated infection risks due to their critical roles during pandemics, and protecting them remains a significant challenge for public health planning. This study develops SAFE-ABM, a simulation-based framework using Agent-Based Modeling (ABM), to evaluate targeted intervention strategies, explicitly capturing structured interactions across families, workplaces, and schools. We simulate key scenarios such as unrestricted movement, school closures, mobility restrictions specific to essential workers, and workforce rotation, to assess their impact on disease transmission dynamics. To ensure robust uncertainty assessment, we integrate a novel Bayesian Uncertainty Quantification (UQ) framework, systematically capturing variability in transmission rates, recovery times, and mortality estimates. Our comparative analysis demonstrates that while general mobility restrictions reduce overall transmission, a workforce rotation strategy for essential workers, when combined with quarantine enforcement, most effectively limits workplace outbreaks and secondary family infections. Unlike other interventions, this approach preserves a portion of the susceptible population, resulting in a more controlled and sustainable epidemic trajectory. These findings offer critical insights for optimizing intervention strategies that mitigate disease spread while maintaining essential societal functions.
MovementVR: An open-source tool for the study of motor control and learning in virtual reality
Authors: Cristina Rossi, Rini Varghese, Amy J Bastian
Date: 2025-04-30 00:00:00

Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation.
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection
Authors: Luoting Zhuang, Seyed Mohammad Hossein Tabatabaei, Ramin Salehi-Rad, Linh M. Tran, Denise R. Aberle, Ashley E. Prosper, William Hsu
Date: 2025-04-30 00:00:00
Tasks: Language Modeling, Language Modelling, Lung Cancer Diagnosis, parameter-efficient fine-tuning

Objective: A number of machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists' assessments of nodules, allowing the model to learn clinically relevant, robust, and explainable features for predicting lung cancer. Methods: We obtained 938 low-dose CT scans from the National Lung Screening Trial with 1,246 nodules and semantic features. The Lung Image Database Consortium dataset contains 1,018 CT scans, with 2,625 lesions annotated for nodule characteristics. Three external datasets were obtained from UCLA Health, the LUNGx Challenge, and the Duke Lung Cancer Screening. We finetuned a pretrained Contrastive Language-Image Pretraining model with a parameter-efficient fine-tuning approach to align imaging and semantic features and predict the one-year lung cancer diagnosis. Results: We evaluated the performance of the one-year diagnosis of lung cancer with AUROC and AUPRC and compared it to three state-of-the-art models. Our model demonstrated an AUROC of 0.90 and AUPRC of 0.78, outperforming baseline state-of-the-art models on external datasets. Using CLIP, we also obtained predictions on semantic features, such as nodule margin (AUROC: 0.81), nodule consistency (0.81), and pleural attachment (0.84), that can be used to explain model predictions. Conclusion: Our approach accurately classifies lung nodules as benign or malignant, providing explainable outputs, aiding clinicians in comprehending the underlying meaning of model predictions. This approach also prevents the model from learning shortcuts and generalizes across clinical settings.
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
Authors: David Choi, Milos Vukadinovic, Bryan He, Christina Binder, Yuki Sahashi, David Ouyang
Date: 2025-04-29 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising, Left Ventricle Segmentation

Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and non-invasive nature. However, ultrasound acquisitions are often limited by artifacts and noise that hinder diagnostic interpretation in clinical settings. Existing methodologies for denoising echos consist solely of traditional filtering-based algorithms or deep learning methods developed on radio-frequency (RF) signals which prevents clinical applicability and scalability. To address these limitations, we introduce the first deep generative model capable of simulating ultrasound noise developed on B-mode data. Using this generative model, we develop a synthetic dataset of paired clean and noisy echo images to train a downstream model for real-world image denoising and demonstrate state-of-the-art performance in both internal and external experiments. In both held-out test sets, our method results in echo images with higher gCNR in comparison to noisy image counterparts and images derived from a comparable method which is consistent with provided visual comparisons. Our experiments showcase the potential of our method for future clinical use to improve the quality of echo acquisitions. To encourage further research into the field, we release our source code and model weights at https://github.com/echonet/image_quality.
ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation
Authors: Xiao Liang, Wentao Ma, Eric Paquet, Herna Lydia Viktor, Wojtek Michalowski
Date: 2025-04-29 00:00:00
Tasks: Drug Discovery, Protein Design

This work introduces the generative fractional diffusion model for protein generation (ProT-GFDM), a novel generative framework that employs fractional stochastic dynamics for protein backbone structure modeling. This approach builds on the continuous-time score-based generative diffusion modeling paradigm, where data are progressively transformed into noise via a stochastic differential equation and reversed to generate structured samples. Unlike classical methods that rely on standard Brownian motion, ProT-GFDM employs a fractional stochastic process with superdiffusive properties to improve the capture of long-range dependencies in protein structures. Trained on protein fragments from the Protein Data Bank, ProT-GFDM outperforms conventional score-based models, achieving a 7.19% increase in density, a 5.66% improvement in coverage, and a 1.01% reduction in the Frechet inception distance. By integrating fractional dynamics with computationally efficient sampling, the proposed framework advances generative modeling for structured biological data, with implications for protein design and computational drug discovery.
Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?
Authors: Junggu Choi, Chansu Yu, Kyle L. Jung, Suan-Sin Foo, Weiqiang Chen, Suzy AA Comhair, Serpil C. Erzurum, Lara Jehi, Jae U. Jung
Date: 2025-04-29 00:00:00
Tasks: Diagnostic, regression

Identifying key biomarkers for COVID-19 from high-dimensional multi-omics data is critical for advancing both diagnostic and pathogenesis research. In this study, we evaluated the applicability of the Quantum Support Vector Machine (QSVM) algorithm for biomarker-based classification of COVID-19. Proteomic and metabolomic biomarkers from two independent datasets were ranked by importance using ridge regression and grouped accordingly. The top- and bottom-ranked biomarker sets were then used to train and evaluate both classical SVM (CSVM) and QSVM models, serving as predictive and negative control inputs, respectively. The QSVM was implemented with multiple quantum kernels, including amplitude encoding, angle encoding, the ZZ feature map, and the projected quantum kernel. Across various experimental settings, QSVM consistently achieved classification performance that was comparable to or exceeded that of CSVM, while reflecting the importance rankings by ridge regression. Although the experiments were conducted in numerical simulation, our findings highlight the potential of QSVM as a promising approach for multi-omics data analysis in biomedical research.
Modelling collective cell migration in a data-rich age: challenges and opportunities for data-driven modelling
Authors: Ruth E. Baker, Rebecca M. Crossley, Carles Falco, Simon F. Martina-Perez
Date: 2025-04-28 00:00:00

Mathematical modelling has a long history in the context of collective cell migration, with applications throughout development, disease and regenerative medicine. The aim of modelling in this context is to provide a framework in which to mathematically encode experimentally derived mechanistic hypotheses, and then to test and validate them to provide new insights and understanding. Traditionally, mathematical models have consisted of systems of partial differential equations that model the evolution of cell density over time, together with the dynamics of any associated biochemical signals or the underlying substrate. The various terms in the model are usually chosen to provide simplified, phenomenological descriptions of the underlying biology, and follow long-standing conventions in the field. However, with the recent development of a plethora of new experimental technologies that provide quantitative data on collective cell migration processes, we now have the opportunity to leverage statistical and machine learning tools to determine mathematical models directly from the data. This perspectives article aims to provide an overview of recently developed data-driven modelling approaches, outlining the main methodologies and the challenges involved in using them to interrogate real-world data relating to collective cell migration.