pwc-archive/papers-with-abstracts huggingface dataset:
HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
Authors: Niklas Kormann, Masoud Ramuz, Zeeshan Nisar, Nadine S. Schaadt, Hendrik Annuth, Benjamin Doerr, Friedrich Feuerhake, Thomas Lampert, Johannes F. Lutzeyer
Date: 2025-06-03 00:00:00
Tasks: graph construction, Graph Neural Network, whole slide images

Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not been extensively explored, is the classification of glomeruli health as an important indicator in nephropathology. This task presents unique difficulties, particularly for the graph construction, i.e., the identification of nodes, edges, and informative features. In this work, we propose a pipeline composed of different traditional and machine learning-based computer vision techniques to identify nodes, edges, and their corresponding features to form a heterogeneous graph. We then proceed to propose a novel heterogeneous GNN architecture for glomeruli classification, called HIEGNet, that integrates both glomeruli and their surrounding immune cells. Hence, HIEGNet is able to consider the immune environment of each glomerulus in its classification. Our HIEGNet was trained and tested on a dataset of Whole Slide Images from kidney transplant patients. Experimental results demonstrate that HIEGNet outperforms several baseline models and generalises best between patients among all baseline models. Our implementation is publicly available at https://github.com/nklsKrmnn/HIEGNet.git.
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Authors: Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
Date: 2025-06-02 00:00:00
Tasks: feature selection, Imputation, Interpretable Machine Learning

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in-hospital stroke using clinical data from the first 24 hours of Intensive Care Unit (ICU) stay. The preprocessing pipeline included removal of high-missingness features, iterative Singular Value Decomposition (SVD) imputation, z-score normalization, one-hot encoding, and class imbalance correction via the Adaptive Synthetic Sampling (ADASYN) algorithm. A two-stage feature selection process-combining Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP)-reduced the initial 80 variables to 20 clinically informative predictors. Among eight ML models evaluated, CatBoost achieved the best performance with an AUROC of 0.8868 (95% CI: 0.8802--0.8937). SHAP analysis and ablation studies identified prior cerebrovascular disease, serum creatinine, and systolic blood pressure as the most influential risk factors. Our results highlight the potential of interpretable ML approaches to support early detection of postoperative stroke and inform decision-making in perioperative critical care.
Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
Authors: Giuseppe Di Caro, Vahagn Kirakosyan, Alexander G. Abanov, Luca Candelori, Nadine Hartmann, Ernest T. Lam, Kharen Musaelian, Ryan Samson, Dario Villani, Martin T. Wells, Richard J. Wenstrup, Mengjia Xu
Date: 2025-06-02 00:00:00
Tasks: Dimensionality Reduction, feature selection

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-predicted chromosomal instability in CTCs from patients with metastatic breast cancer. QCML leverages quantum mechanical principles to represent data as state vectors in a Hilbert space, enabling context-aware feature modeling, dimensionality reduction, and enhanced generalization without requiring curated feature selection. QCML outperforms conventional machine learning methods when tested on out of sample verification CTCs, achieving higher accuracy in identifying predicted large-scale state transitions (pLST) status from CTC-derived morphology features. These preliminary findings support the application of QCML as a novel machine learning tool with superior performance in high-dimensional, low-sample-size biomedical contexts. QCML enables the simulation of cognition-like learning for the identification of biologically meaningful prediction of chromosomal instability from CTC morphology, offering a novel tool for CTC classification in liquid biopsy.
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
Authors: Christian Marius Lillelund, Sanjay Kalra, Russell Greiner
Date: 2025-06-02 00:00:00
Tasks: counterfactual, Survival Analysis

Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life; however, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R<=2) with respect to five common functions: speaking, swallowing, handwriting, walking and breathing. We formulate this task as a multi-event survival problem and validate our approach in the PRO-ACT dataset by training five covariate-based survival models to estimate the probability of an event over a 500-day period after a baseline visit. We then predict five event-specific individual survival distributions (ISDs) for each patient, each providing an interpretable and meaningful estimate of when that event will likely take place in the future. The results show that covariate-based models are superior to the Kaplan-Meier estimator at predicting time-to-event outcomes. Additionally, our method enables practitioners to make individual counterfactual predictions, where certain features (covariates) can be changed to see their effect on the predicted outcome. In this regard, we find that Riluzole has little to no impact on predicted functional decline. However, for patients with bulbar-onset ALS, our method predicts considerably shorter counterfactual time-to-event estimates for tasks related to speech and swallowing compared to limb-onset ALS. The proposed method can be applied to current clinical examination data to assess the risk of functional decline and thus allow more personalized treatment planning.
Recent Developments in GNNs for Drug Discovery
Authors: Zhengyu Fang, Xiaoge Zhang, Anyin Zhao, Xiao Li, Huiyuan Chen, Jing Li
Date: 2025-06-02 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Property Prediction

In this paper, we review recent developments and the role of Graph Neural Networks (GNNs) in computational drug discovery, including molecule generation, molecular property prediction, and drug-drug interaction prediction. By summarizing the most recent developments in this area, we underscore the capabilities of GNNs to comprehend intricate molecular patterns, while exploring both their current and prospective applications. We initiate our discussion by examining various molecular representations, followed by detailed discussions and categorization of existing GNN models based on their input types and downstream application tasks. We also collect a list of commonly used benchmark datasets for a variety of applications. We conclude the paper with brief discussions and summarize common trends in this important research area.
Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists
Authors: Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic
Date: 2025-06-01 00:00:00

The study expands the application of scikit-learn-based machine learning (ML) to the prediction of small biomolecule functionalities based on Carbon 13 isotope (13C) NMR spectroscopy data derived from Simplified Molecular Input Line Entry System (SMILES) notations. The methodology previously demonstrated by predicting dopamine D1 receptor antagonists was upgraded with addition of new molecular features derived from the PubChem database. The enhanced ML model obtained 75.8% Accuracy, 84.2% Precision, 63.6% Recall, 72.5% F1-score, 75.8 % ROC, when is trained by 25,532 samples and tested by 5,466 samples. To evaluate the applicability of the methodology for a variety of case studies, a comparison was conducted between the prediction capabilities of the ML models based on the human dopamine D1 receptor antagonists and on the neuronal Transthyretin (TTR) transcription activators. Since the TTR bioassay did not contain the needed for the comparison number of samples, the results were obtained hypothetically. Gradient Boosting classifier was the optimal model for TTR transcription activators achieving hypothetical 67.4% Accuracy, 74.0% Precision, 53.5% Recall, 62.1% F1-score, 67.4 % ROC, if it would be trained with 25,532 samples and tested with 5,466 samples. Beyond the main study, the CID_SID ML model that can predict if a small biomolecule has TTR transcription activation capabilities based solely on its PubChem CID and SID achieved 81.5% Accuracy, 94.6% Precision, 66.8% Recall, 78.3% F1-score, 81.5 % ROC.
ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation
Authors: Xinyi Liu, Lipeng Ma, Yixuan Li, Weidong Yang, Qingyuan Zhou, Jiayi Song, Shuhao Li, Ben Fei
Date: 2025-06-01 00:00:00
Tasks: Natural Language Understanding

Large Language Models (LLMs) are widely used across various scenarios due to their exceptional reasoning capabilities and natural language understanding. While LLMs demonstrate strong performance in tasks involving mathematics and coding, their effectiveness diminishes significantly when applied to chemistry-related problems. Chemistry problems typically involve long and complex reasoning steps, which contain specific terminology, including specialized symbol systems and complex nomenclature conventions. These characteristics often cause general LLMs to experience hallucinations during the reasoning process due to their lack of specific knowledge. However, existing methods are struggling to effectively leverage chemical expertise and formulas. Moreover, current uncertainty estimation methods, designed to mitigate potential reasoning errors, are unable to precisely identify specific steps or key knowledge. In this work, we propose a novel framework called ChemAU, which incorporates our adaptive uncertainty estimation method that applies different uncertainty values based on the position of reasoning steps within the whole reasoning chain. Leveraging this method, ChemAU identifies gaps in chemistry knowledge and precisely supplements chemical expertise with the specialized domain model, thereby correcting and updating the previously flawed reasoning chain. Our experiments with three popular LLMs across three chemistry datasets demonstrate that ChemAU significantly enhances both reasoning accuracy and uncertainty estimation.
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning
Authors: Peijin Guo, Minghui Li, Hewen Pan, Bowen Chen, Yang Wu, Zikang Guo, Leo Yu Zhang, Shengshan Hu, Shengqing Hu
Date: 2025-06-01 00:00:00
Tasks: Contrastive Learning, Prediction, Uncertainty Quantification

Accurate prediction of molecular metabolic stability (MS) is critical for drug research and development but remains challenging due to the complex interplay of molecular interactions. Despite recent advances in graph neural networks (GNNs) for MS prediction, current approaches face two critical limitations: (1) incomplete molecular modeling due to atom-centric message-passing mechanisms that disregard bond-level topological features, and (2) prediction frameworks that lack reliable uncertainty quantification. To address these challenges, we propose TrustworthyMS, a novel contrastive learning framework designed for uncertainty-aware metabolic stability prediction. First, a molecular graph topology remapping mechanism synchronizes atom-bond interactions through edge-induced feature propagation, capturing both localized electronic effects and global conformational constraints. Second, contrastive topology-bond alignment enforces consistency between molecular topology views and bond patterns via feature alignment, enhancing representation robustness. Third, uncertainty modeling through Beta-Binomial uncertainty quantification enables simultaneous prediction and confidence calibration under epistemic uncertainty. Through extensive experiments, our results demonstrate that TrustworthyMS outperforms current state-of-the-art methods in terms of predictive performance.
Improving statistical learning methods via features selection without replacement sampling and random projection
Authors: Sulaiman Khan, Muhammad Ahmad, Fida Ullah, Carlos Aguilar Ibañez, José Eduardo Valdez Rodriguez
Date: 2025-05-28 00:00:00
Tasks: feature selection

Cancer is fundamentally a genetic disease characterized by genetic and epigenetic alterations that disrupt normal gene expression, leading to uncontrolled cell growth and metastasis. High-dimensional microarray datasets pose challenges for classification models due to the "small n, large p" problem, resulting in overfitting. This study makes three different key contributions: 1) we propose a machine learning-based approach integrating the Feature Selection Without Re-placement (FSWOR) technique and a projection method to improve classification accuracy. 2) We apply the Kendall statistical test to identify the most significant genes from the brain cancer mi-croarray dataset (GSE50161), reducing the feature space from 54,675 to 20,890 genes.3) we apply machine learning models using k-fold cross validation techniques in which our model incorpo-rates ensemble classifiers with LDA projection and Na\"ive Bayes, achieving a test score of 96%, outperforming existing methods by 9.09%. The results demonstrate the effectiveness of our ap-proach in high-dimensional gene expression analysis, improving classification accuracy while mitigating overfitting. This study contributes to cancer biomarker discovery, offering a robust computational method for analyzing microarray data.
Llama-Affinity: A Predictive Antibody Antigen Binding Model Integrating Antibody Sequences with Llama3 Backbone Architecture
Authors: Delower Hossain, Ehsan Saghapour, Kevin Song, Jake Y. Chen
Date: 2025-05-17 00:00:00
Tasks: Computational Efficiency

Antibody-facilitated immune responses are central to the body's defense against pathogens, viruses, and other foreign invaders. The ability of antibodies to specifically bind and neutralize antigens is vital for maintaining immunity. Over the past few decades, bioengineering advancements have significantly accelerated therapeutic antibody development. These antibody-derived drugs have shown remarkable efficacy, particularly in treating cancer, SARS-CoV-2, autoimmune disorders, and infectious diseases. Traditionally, experimental methods for affinity measurement have been time-consuming and expensive. With the advent of artificial intelligence, in silico medicine has been revolutionized; recent developments in machine learning, particularly the use of large language models (LLMs) for representing antibodies, have opened up new avenues for AI-based design and improved affinity prediction. Herein, we present an advanced antibody-antigen binding affinity prediction model (LlamaAffinity), leveraging an open-source Llama 3 backbone and antibody sequence data sourced from the Observed Antibody Space (OAS) database. The proposed approach shows significant improvement over existing state-of-the-art (SOTA) methods (AntiFormer, AntiBERTa, AntiBERTy) across multiple evaluation metrics. Specifically, the model achieved an accuracy of 0.9640, an F1-score of 0.9643, a precision of 0.9702, a recall of 0.9586, and an AUC-ROC of 0.9936. Moreover, this strategy unveiled higher computational efficiency, with a five-fold average cumulative training time of only 0.46 hours, significantly lower than in previous studies.
Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL): Immunotherapy and Cell Therapy approaches
Authors: Sierra Haile, Benjamin C. Balzer, Emily Egan, Cheryl L. Jorcyk, Nilufar Ali
Date: 2025-06-10 00:00:00
Tasks: Diagnostic

This article focuses on current and emerging therapeutics for CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). CADASIL is an inherited vascular disease that impairs blood flow in the small cerebral vessels of the brain, leading to strokes and other neurological deficits. The disease is caused by a mutation in the NOTCH3 gene located on chromosome 19. NOTCH3 encodes a transmembrane receptor expressed on vascular smooth muscle cells. In CADASIL, mutations in the NOTCH3 gene lead to the accumulation and deposition of the receptor, affecting the number of cysteine residues in its extracellular domain. These mutations result in the loss or gain of a cysteine residue within the epidermal growth factor-like repeat (EGFr) domains of the NOTCH protein. Beyond traditional symptomatic treatments for stroke, this work highlights advances in disease modifying approaches including gene editing, cell therapies, and immune-based interventions aimed at altering the course of CADASIL. It also examines ongoing clinical trials and recent patents related to these novel strategies. In addition to summarizing diagnostic methods and molecular mechanisms, the article emphasizes the translational potential of current research and the experimental models driving therapeutic development. The goal is to offer a comprehensive overview of CADASIL and emerging interventions that hold promise for improving long-term outcomes.
AI-Driven Early Detection of Cardiovascular Diseases: Reducing Healthcare Costs and improving patient Outcomes
Authors: Ahasan Ahmed, Albatoul Khaled, Muhammad Waqar, DrJavaid Akhtar Hashmi, Hazem AbdulKareem Alfanash, Wesam Taher Almagharbeh, Amine Hamdache, Ilias Elmouki
Date: 2025-06-09 00:00:00
Tasks: Management

The main goal from this study is to discuss the main features of Artificial intelligence (AI) as well as their applicability for early cardiovascular Disease (CVDs) Detection, Material and Method : Systematic review approach Results : It was seen that integrating AI algorithm the diagnosis of CVDs become more accurate and lee time consuming. Conclusion: Now the concept of using AI technologies in cardiovascular health care holds the potential to transform disease management .
Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction
Authors: Mitchel J. Colebank
Date: 2025-06-05 00:00:00
Tasks: Dimensionality Reduction, Experimental Design, Sensitivity

Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires sensitivity analysis, where parameters of the model are ranked based on their influence on the specific quantities of interest. This is especially important for simulators used to build medical digital twins, as the amount of data is typically limited. For expensive models, such as blood flow models, emulation is employed to expedite the simulation time. Parameter ranking and fixing using sensitivity analysis are often heuristic, though, and vary with the specific application or simulator used. The present study provides an innovative solution to this problem by leveraging polynomial chaos expansions (PCEs) for both multioutput global sensitivity analysis and formal parameter identifiability. For the former, we use dimension reduction to efficiently quantify time-series sensitivity of a one-dimensional pulmonary hemodynamics model. We consider both Windkessel and structured tree boundary conditions. We then use PCEs to construct profile-likelihood confidence intervals to formally assess parameter identifiability, and show how changes in experimental design improve identifiability. Our work presents a novel approach to determining parameter identifiability and leverages a common emulation strategy for enabling profile-likelihood analysis in problems governed by partial differential equations.
Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery
Authors: Maria Francesca Abbate, Pierre Toxe, Nicolas Maestrali, Marie Gagnaire, Emmanuelle Vigne, Melody A. Shahsavarian, Thierry Mora, Aleksandra M. Walczak
Date: 2025-05-19 00:00:00

The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key time points. By mapping the output from STAR to single-cell data taken at the last time point, we successfully identified a pool of antibodies, of which 50% demonstrated binding capabilities. We observed convergent selection, where responding sequences with identical amino acid complementarity determining regions 3 (CDR3) were found in different mice. We provide a catalog of 67 experimentally validated antibodies against GFRAL. The potential of these antibodies as antagonists or agonists against GFRAL suggests therapeutic solutions for conditions like cancer cachexia, anorexia, obesity, and diabetes. This study underscores the utility of integrating computational methods and experimental validation for antibody discovery in therapeutic contexts by reducing time and increasing efficiency.
Simulation of a mathematical model of tumoral growth using finite differences
Authors: Jesika Maganin, Neyva Maria Lopes Romeiro, Eliandro Rodrigues Cirilo, Paulo Laerte Natti
Date: 2025-05-15 00:00:00

The work presents a study of the non-linear mathematical model of tumor growth, proposed by Kolev and Zubik-Kowal (2011). The model is described by a system composed of four partial differential equations that represent the evolution of the density of cancer cells, density of the extracellular matrix (ECM), concentration of matrix-degrading enzyme (MDE) and concentration of tissue metalloproteinase inhibitors. For numerical simulations, the finite difference method is used, in which the temporal terms of the equations are discretized using a two-stage method. In spatial terms, finite central differences are used. A study of numerical convergence for the proposed scheme is presented, using analytical solutions manufactured in a rectangular geometry. Finally, simulations of the tumor growth model are performed, using a non-regular mesh that represents the geometry of a female breast. To simulate the model in non-regular geometry, the technique used is to approximate the contour of the physical domain by mesh segments. The simulations showed that the model has important characteristics of the interactions between tumor cells and the surrounding tissue.