pwc-archive/papers-with-abstracts huggingface dataset:
SBMLtoOdin and Menelmacar: Interactive visualisation of systems biology models for expert and non-expert audiences
Authors: Leonie J. Lorenz, Antoine Andréoletti, Tung V. N. Nguyen, Henning Hermjakob, Richard G. FitzJohn, Rahuman S. Malik Sheriff, John A. Lees
Date: 2025-04-28 00:00:00

Motivation: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Implementation: Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use web interface. Availability: SBMLtoOdin is published under open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.
OmicsQ: A User-Friendly Platform for Interactive Quantitative Omics Data Analysis
Authors: Xuan-Tung Trinh, André Abrantes da Costa, David Bouyssié, Adelina Rogowska-Wrzesinska, Veit Schwämmle
Date: 2025-04-28 00:00:00
Tasks: Experimental Design, Imputation, Missing Values

Motivation: High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values and individually fluctuating variances. This requires sophisticated analytical methods for accurate, deep and insightful biological interpretations, capable of dealing with a large variety of data properties and different amounts of completeness. Software to handle such data complexity is rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise. Results: We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ integrates established statistical processing tools with an intuitive, browser-based visualization interface. It provides robust batch correction, automated experimental design annotation, and missing-data handling without imputation, which ensures data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable tov data from different domains. Availability and Implementation: OmicsQ is implemented in R and R Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions can be found at https://github.com/computproteomics/OmicsQ
m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training
Authors: Meng Xiao, Xunxin Cai, Qingqing Long, Chengrui Wang, Yuanchun Zhou, HengShu Zhu
Date: 2025-04-28 00:00:00
Tasks: Question Answering

Corpus distillation for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source annotated scientific corpora, which remains a bottleneck for effective LLM training in biomedical research. This paper proposes a knowledge-driven, agentic framework for scientific corpus distillation, tailored explicitly for LLM training in the biomedical domain, addressing the challenge posed by the complex hierarchy of biomedical knowledge. Central to our approach is a collaborative multi-agent architecture, where specialized agents, each guided by the Medical Subject Headings (MeSH) hierarchy, work in concert to autonomously extract, synthesize, and self-evaluate high-quality textual data from vast scientific literature. This agentic framework collectively generates and refines domain-specific question-answer pairs, ensuring comprehensive coverage and consistency with biomedical ontologies while minimizing manual involvement. Extensive experimental results show that language models trained on our multi-agent distilled datasets achieve notable improvements in biomedical question-answering tasks, outperforming both strong life sciences LLM baselines and advanced proprietary models. Notably, our AI-Ready dataset enables Llama3-70B to surpass GPT-4 with MedPrompt and Med-PaLM-2, despite their larger scale. Detailed ablation studies and case analyses further validate the effectiveness and synergy of each agent within the framework, highlighting the potential of multi-agent collaboration in biomedical LLM training.
Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning
Authors: Wenfeng Dai, Yanhong Wang, Shuai Yan, Qingzhi Yu, Xiang Cheng
Date: 2025-04-27 00:00:00
Tasks: Contrastive Learning, Graph Neural Network, Prediction

Drug-target interaction (DTI) prediction is a core task in drug development and precision medicine in the biomedical field. However, traditional machine learning methods generally have the black box problem, which makes it difficult to reveal the deep correlation between the model decision mechanism and the interaction pattern between biological molecules. This study proposes a heterogeneous network drug target interaction prediction framework, integrating graph neural network and multi scale signal processing technology to construct a model with both efficient prediction and multi level interpretability. Its technical breakthroughs are mainly reflected in the following three dimensions:Local global feature collaborative perception module. Based on heterogeneous graph convolutional neural network (HGCN), a multi order neighbor aggregation strategy is designed.Multi scale graph signal decomposition and biological interpretation module. A deep hierarchical node feature transform (GWT) architecture is proposed.Contrastive learning combining multi dimensional perspectives and hierarchical representations. By comparing the learning models, the node representations from the two perspectives of HGCN and GWT are aligned and fused, so that the model can integrate multi dimensional information and improve the prediction robustness. Experimental results show that our framework shows excellent prediction performance on all datasets. This study provides a complete solution for drug target discovery from black box prediction to mechanism decoding, and its methodology has important reference value for modeling complex biomolecular interaction systems.
HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction
Authors: Qingzhi Yu, Shuai Yan, Wenfeng Dai, Xiang Cheng
Date: 2025-04-27 00:00:00
Tasks: Graph Neural Network, Prediction

Protein-protein interactions (PPIs) are fundamental for deciphering cellular functions,disease pathways,and drug discovery.Although existing neural networks and machine learning methods have achieved high accuracy in PPI prediction,their black-box nature leads to a lack of causal interpretation of the prediction results and difficulty in capturing hierarchical geometries and multi-scale dynamic interaction patterns among proteins.To address these challenges, we propose HyboWaveNet,a novel deep learning framework that collaborates with hyperbolic graphical neural networks (HGNNs) and multiscale graphical wavelet transform for robust PPI prediction. Mapping protein features to Lorentz space simulates hierarchical topological relationships among biomolecules via a hyperbolic distance metric,enabling node feature representations that better fit biological a priori.HyboWaveNet inherently simulates hierarchical and scale-free biological relationships, while the integration of wavelet transforms enables adaptive extraction of local and global interaction features across different resolutions. Our framework generates node feature representations via a graph neural network under the Lorenz model and generates pairs of positive samples under multiple different views for comparative learning, followed by further feature extraction via multi-scale graph wavelet transforms to predict potential PPIs. Experiments on public datasets show that HyboWaveNet improves over both existing state-of-the-art methods. We also demonstrate through ablation experimental studies that the multi-scale graph wavelet transform module improves the predictive performance and generalization ability of HyboWaveNet. This work links geometric deep learning and signal processing to advance PPI prediction, providing a principled approach for analyzing complex biological systems
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging
Authors: Junsouk Choi, Jian Kang, Veerabhadran Baladandayuthapani
Date: 2025-04-25 00:00:00
Tasks: Clustering, Gaussian Processes, Uncertainty Quantification

Motivation: Understanding the spatial architecture of tissues is essential for decoding the complex interactions within cellular ecosystems and their implications for disease pathology and clinical outcomes. Recent advances in multiplex imaging technologies have enabled high-resolution profiling of cellular phenotypes and their spatial distributions, revealing critical roles of tissue structures such as tertiary lymphoid structures (TLSs) in shaping immune responses and influencing disease progression. However, existing methods for analyzing spatial tissue structures often rely on hard clustering or adjacency-based spatial models, which are limited in capturing the nuanced and overlapping nature of cellular communities. To address these challenges, we develop a novel spatial topic modeling framework for the unsupervised discovery of spatial tissue structures in multiplex imaging data. Results: We propose TopSpace, a novel Bayesian spatial topic model that integrates Gaussian processes into latent Dirichlet allocation to flexibly model spatial dependencies in tissue microenvironments. By leveraging the Bayesian framework, TopSpace supports multicellular mixed-membership clustering and offers key inferential advantages, including robust uncertainty quantification and data-driven determination of the number of multicellular microenvironments. We demonstrate the utility of TopSpace through simulations and a case study on non-small cell lung cancer (NSCLC) data. Simulations show that TopSpace accurately recovers latent tissue microenvironments and spatial clustering patterns, outperforming existing methods in scenarios with varying spatial dependencies. Applied to NSCLC data, TopSpace successfully identifies TLS and captures their spatial probability distribution, which strongly correlates with patient survival outcomes.
3plex Web: An Interactive Platform for RNA:DNA Triplex Prediction and Analysis
Authors: Marco Masera, Chiara Cicconetti, Francesca Ferrero, Salvatore Oliviero, Ivan Molineris
Date: 2025-04-25 00:00:00

Summary: Long non-coding RNAs (lncRNAs) exert their functions by cooperating with other molecules including proteins and DNA. Triplexes, formed through the interaction between a single-stranded RNA (ssRNA) and a double-stranded DNA (dsDNA), have been consistently described as a mechanism that allows lncRNAs to target specific genomic sequences in vivo. Building on the computational tool 3plex, we developed 3plex Web, an accessible platform that enhances RNA:DNA triplex prediction by integrating interactive visualization, statistical evaluation, and user-friendly downstream analysis workflows. 3plex Web implements new features such as input randomization for statistical assessments, interactive profile plotting for triplex stability, and customizable DNA Binding Domain (DBD) selection. This platform enables rapid analysis through PATO, substantially reducing processing times compared to previous methods, while offering Snakemake workflows to integrate gene expression data and explore lncRNA regulatory mechanisms. Availability and implementation: 3plex Web is freely available at https://3plex.unito.it as an online web service. The source code for 3plex is available at https://github.com/molinerisLab/3plex, paired with a definition file to set up the application into a Singularity image. Contact: ivan.molineris@unito.it Keywords: DNA; RNA; RNA-DNA interaction; triplex; long non-coding RNA; lncRNA; gene regulation; web application
Probabilities of causation and post-infection outcomes
Authors: Bronner P. Gonçalves
Date: 2025-04-25 00:00:00

Probabilities of causation provide explanatory information on the observed occurrence (causal necessity) and non-occurrence (causal sufficiency) of events. Here, we adapt these probabilities (probability of necessity, probability of sufficiency, and probability of necessity and sufficiency) to an important class of epidemiologic outcomes, post-infection outcomes. A defining feature of studies on these outcomes is that they account for the post-treatment variable, infection acquisition, which means that, for individuals who remain uninfected, the outcome is not defined. Following previous work by Hudgens and Halloran, we describe analyses of post-infection outcomes using the principal stratification framework, and then derive expressions for the probabilities of causation in terms of principal strata-related parameters. Finally, we show that these expressions provide insights into the contributions of different processes (absence or occurrence of infection, and disease severity), implicitly encoded in the definition of the outcome, to causation.
Quantum machine learning framework for longitudinal biomedical studies
Authors: Maria Demidik, Filippo Utro, Alexey Galda, Karl Jansen, Daniel Blankenberg, Laxmi Parida
Date: 2025-04-24 00:00:00
Tasks: Quantum Machine Learning

Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research.
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Authors: Ivan Rossi, Flavio Sartori, Cesare Rollo, Giovanni Birolo, Piero Fariselli, Tiziana Sanavia
Date: 2025-04-24 00:00:00
Tasks: Survival Analysis, Survival Prediction

Survival analysis often relies on Cox models, assuming both linearity and proportional hazards (PH). This study evaluates machine and deep learning methods that relax these constraints, comparing their performance with penalized Cox models on a benchmark of three synthetic and three real datasets. In total, eight different models were tested, including six non-linear models of which four were also non-PH. Although Cox regression often yielded satisfactory performance, we showed the conditions under which machine and deep learning models can perform better. Indeed, the performance of these methods has often been underestimated due to the improper use of Harrell's concordance index (C-index) instead of more appropriate scores such as Antolini's concordance index, which generalizes C-index in cases where the PH assumption does not hold. In addition, since occasionally high C-index models happen to be badly calibrated, combining Antolini's C-index with Brier's score is useful to assess the overall performance of a survival method. Results on our benchmark data showed that survival prediction should be approached by testing different methods to select the most appropriate one according to sample size, non-linearity and non-PH conditions. To allow an easy reproducibility of these tests on our benchmark data, code and documentation are freely available at https://github.com/compbiomed-unito/survhive.
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
Authors: Tekin Gunasar, Virginia de Sa
Date: 2025-04-23 00:00:00
Tasks: Motor Imagery, Subject Transfer

We propose a method to improve subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold, followed by computing a new common spatial patterns (CSP) based spatial filter. We explore various ways to integrate information from multiple subjects and show improved performance compared to standard CSP. Across three datasets, our method shows marginal improvements over standard CSP; however, when training data are limited, the improvements become more significant.
Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
Authors: Masoud Tafavvoghi, Lars Ailo Bongo, André Berli Delgado, Nikita Shvetsov, Anders Sildnes, Line Moi, Lill-Tove Rasmussen Busund, Kajsa Møllersen
Date: 2025-04-23 00:00:00
Tasks: Cell Detection, whole slide images

In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath, demonstrating the potential of easily accessible tools to perform complex tasks in a fully automatic fashion. First, we trained a pixel classifier to segment tumor, tumor-associated stroma, and other tissue compartments in breast cancer H&E-stained whole-slide images (WSI) to isolate tumor-associated stroma for subsequent analysis. Next, we applied a pre-trained StarDist deep learning model in QuPath for cell detection and used the extracted cell features to train a binary classifier distinguishing TILs from other cells. To evaluate our TILs assessment pipeline, we calculated the TIL density in each WSI and categorized them as low, medium, or high TIL levels. Our pipeline was evaluated against pathologist-assigned TIL scores, achieving a Cohen's kappa of 0.71 on the external test set, corroborating previous research findings. These results confirm that existing software can offer a practical solution for the assessment of TILs in H&E-stained WSIs of breast cancer.
Moving mountains: grazing agents drive terracette formation on steep hillslopes
Authors: Benjamin Seleb, Atanu Chatterjee, Saad Bhamla
Date: 2025-04-23 00:00:00

Terracettes, striking, step-like landforms that stripe steep, vegetated hillslopes, have puzzled scientists for more than a century. Competing hypotheses invoke either slow mass-wasting or the relentless trampling of grazing animals, yet no mechanistic model has linked hoof-scale behavior to landscape-scale form. Here we bridge that gap with an active-walker model in which ungulates are represented as stochastic foragers moving on an erodible slope. Each agent weighs the energetic cost of climbing against the benefit of fresh forage; every hoof-fall compacts soil and lowers local biomass, subtly reshaping the energy landscape that guides subsequent steps. Over time, these stigmergic feedbacks concentrate traffic along cross-slope paths that coalesce into periodic tread-and-riser bands, morphologically analogous to natural terracettes. Our model illustrates how local foraging rules governing movement and substrate feedback can self-organize into large-scale topographic patterns, highlighting the wider role of decentralized biological processes in sculpting terrestrial landscapes.
Synergistic Benefits of Joint Molecule Generation and Property Prediction
Authors: Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek
Date: 2025-04-23 00:00:00
Tasks: Drug Design, molecular representation, Prediction, Property Prediction, Representation Learning

Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic benefits reaching beyond purely generative or predictive models. However, training joint models presents daunting architectural and optimization challenges. Here, we propose Hyformer, a transformer-based joint model that successfully blends the generative and predictive functionalities, using an alternating attention mechanism and a joint pre-training scheme. We show that Hyformer is simultaneously optimized for molecule generation and property prediction, while exhibiting synergistic benefits in conditional sampling, out-of-distribution property prediction and representation learning. Finally, we demonstrate the benefits of joint learning in a drug design use case of discovering novel antimicrobial~peptides.
Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations
Authors: Cong Qi, Hanzhang Fang, Siqi Jiang, Tianxing Hu, Wei Zhi
Date: 2025-04-22 00:00:00
Tasks: Benchmarking, Few-Shot Learning, Language Modeling, Language Modelling, Large Language Model, Specificity

Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We present LANTERN (Large lAnguage model-powered TCR-Enhanced Recognition Network), a deep learning framework that combines large-scale protein language models with chemical representations of peptides. By encoding TCR \b{eta}-chain sequences using ESM-1b and transforming peptide sequences into SMILES strings processed by MolFormer, LANTERN captures rich biological and chemical features critical for TCR-peptide recognition. Through extensive benchmarking against existing models such as ChemBERTa, TITAN, and NetTCR, LANTERN demonstrates superior performance, particularly in zero-shot and few-shot learning scenarios. Our model also benefits from a robust negative sampling strategy and shows significant clustering improvements via embedding analysis. These results highlight the potential of LANTERN to advance TCR-pMHC binding prediction and support the development of personalized immunotherapies.