pwc-archive/papers-with-abstracts huggingface dataset:
GENEOnet: Statistical analysis supporting explainability and trustworthiness
Authors: Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti, Alessandro Pedretti, Carmen Gratteri, Filippo Lunghini, Andrea Rosario Beccari, Carmine Talarico
Date: 2025-03-12 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings suggest that such models can be inserted within the domain of eXplainable Artificial Intelligence (XAI) due to their inherent interpretability. In this study, we aim to verify this claim with respect to GENEOnet, a GENEO network developed for an application in computational biochemistry by employing various statistical analyses and experiments. Such experiments first allow us to perform a sensitivity analysis on GENEOnet's parameters to test their significance. Subsequently, we show that GENEOnet exhibits a significantly higher proportion of equivariance compared to other methods. Lastly, we demonstrate that GENEOnet is on average robust to perturbations arising from molecular dynamics. These results collectively serve as proof of the explainability, trustworthiness, and robustness of GENEOnet and confirm the beneficial use of GENEOs in the context of Trustworthy Artificial Intelligence.
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models
Authors: Zicheng Ma, Chuanliu Fan, Zhicong Wang, Zhenyu Chen, Xiaohan Lin, Yanheng Li, Shihao Feng, Jun Zhang, Ziqiang Cao, Yi Qin Gao
Date: 2025-03-11 00:00:00
Tasks: multimodal generation, Protein Design, Protein Function Prediction

Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. However, many fundamental challenges in protein science are inherently structure-dependent. The absence of structure-aware tokens significantly limits the capabilities of LLMs for comprehensive biomolecular comprehension and multimodal generation. To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space. This innovative approach enables joint training of the LLM exclusively through the Next-Token Prediction paradigm, facilitating multimodal protein reasoning and generation. ProtTeX enables general LLMs to perceive and process protein structures through sequential text input, leverage structural information as intermediate reasoning components, and generate or manipulate structures via sequential text output. Experiments demonstrate that our model achieves significant improvements in protein function prediction, outperforming the state-of-the-art domain expert model with a twofold increase in accuracy. Our framework enables high-quality conformational generation and customizable protein design. For the first time, we demonstrate that by adopting the standard training and inference pipelines from the LLM domain, ProtTeX empowers decoder-only LLMs to effectively address diverse spectrum of protein-related tasks.
Differentiable Folding for Nearest Neighbor Model Optimization
Authors: Ryan K. Krueger, Sharon Aviran, David H. Mathews, Jeffrey Zuber, Max Ward
Date: 2025-03-12 00:00:00
Tasks: model, Model Optimization

The Nearest Neighbor model is the $\textit{de facto}$ thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains $\approx13,000$ underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in $\textit{differentiable folding}$, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems.
Towards Interpretable Protein Structure Prediction with Sparse Autoencoders
Authors: Nithin Parsan, David J. Yang, John J. Yang
Date: 2025-03-11 00:00:00
Tasks: Prediction, Protein Structure Prediction

Protein language models have revolutionized structure prediction, but their nonlinear nature obscures how sequence representations inform structure prediction. While sparse autoencoders (SAEs) offer a path to interpretability here by learning linear representations in high-dimensional space, their application has been limited to smaller protein language models unable to perform structure prediction. In this work, we make two key advances: (1) we scale SAEs to ESM2-3B, the base model for ESMFold, enabling mechanistic interpretability of protein structure prediction for the first time, and (2) we adapt Matryoshka SAEs for protein language models, which learn hierarchically organized features by forcing nested groups of latents to reconstruct inputs independently. We demonstrate that our Matryoshka SAEs achieve comparable or better performance than standard architectures. Through comprehensive evaluations, we show that SAEs trained on ESM2-3B significantly outperform those trained on smaller models for both biological concept discovery and contact map prediction. Finally, we present an initial case study demonstrating how our approach enables targeted steering of ESMFold predictions, increasing structure solvent accessibility while fixing the input sequence. To facilitate further investigation by the broader community, we open-source our code, dataset, pretrained models https://github.com/johnyang101/reticular-sae , and visualizer https://sae.reticular.ai .
The standard coil or globule phases cannot describe the denatured state of structured proteins and intrinsically disordered proteins
Authors: F. Righini, G. Potel, R. Capelli, G. Tiana
Date: 2025-03-11 00:00:00

The concepts of globule and random coil were developed to describe the phases of homopolymers and then used to characterize the denatured state of structured cytosolic proteins and intrinsically disordered proteins. Using multi-scale molecular dynamics simulations, we were able to explore the conformational space of the disordered conformations of both types of protein under biological conditions in an affordable amount of computational time. By studying the size of the protein and the density correlations in space, we conclude that the standard phases of homopolymers and the tools to detect them cannot be applied straightforwardly to proteins.
Terrier: A Deep Learning Repeat Classifier
Authors: Robert Turnbull, Neil D. Young, Edoardo Tescari, Lee F. Skerratt, Tiffany A. Kosch
Date: 2025-03-12 00:00:00
Tasks: Deep Learning

Repetitive DNA sequences underpin genome architecture and evolutionary processes, yet they remain challenging to classify accurately. Terrier is a deep learning model designed to overcome these challenges by classifying repetitive DNA sequences using a publicly available, curated repeat sequence library trained under the RepeatMasker schema. Existing tools often struggle to classify divergent taxa due to biases in reference libraries, limiting our understanding of repeat evolution and function. Terrier overcomes these challenges by leveraging deep learning for improved accuracy. Trained on RepBase, which includes over 100,000 repeat families -- four times more than Dfam -- Terrier maps 97.1% of RepBase sequences to RepeatMasker categories, offering the most comprehensive classification system available. When benchmarked against DeepTE, TERL, and TEclass2 in model organisms (rice and fruit flies), Terrier achieved superior accuracy while classifying a broader range of sequences. Further validation in non-model amphibian and flatworm genomes highlights its effectiveness in improving classification in non-model species, facilitating research on repeat-driven evolution, genomic instability, and phenotypic variation.
Regulatory DNA sequence Design with Reinforcement Learning
Authors: Zhao Yang, Bing Su, Chuan Cao, Ji-Rong Wen
Date: 2025-03-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Cis-regulatory elements (CREs), such as promoters and enhancers, are relatively short DNA sequences that directly regulate gene expression. The fitness of CREs, measured by their ability to modulate gene expression, highly depends on the nucleotide sequences, especially specific motifs known as transcription factor binding sites (TFBSs). Designing high-fitness CREs is crucial for therapeutic and bioengineering applications. Current CRE design methods are limited by two major drawbacks: (1) they typically rely on iterative optimization strategies that modify existing sequences and are prone to local optima, and (2) they lack the guidance of biological prior knowledge in sequence optimization. In this paper, we address these limitations by proposing a generative approach that leverages reinforcement learning (RL) to fine-tune a pre-trained autoregressive (AR) model. Our method incorporates data-driven biological priors by deriving computational inference-based rewards that simulate the addition of activator TFBSs and removal of repressor TFBSs, which are then integrated into the RL process. We evaluate our method on promoter design tasks in two yeast media conditions and enhancer design tasks for three human cell types, demonstrating its ability to generate high-fitness CREs while maintaining sequence diversity. The code is available at https://github.com/yangzhao1230/TACO.
Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review
Authors: Marcello Barylli, Joyaditya Saha, Tineke E. Buffart, Jan Koster, Kristiaan J. Lenos, Louis Vermeulen, Vivek M. Sheraton
Date: 2025-03-12 00:00:00

Recent advances in single cell sequencing and multi-omics techniques have significantly improved our understanding of biological phenomena and our capacity to model them. Despite combined capture of data modalities showing similar progress, notably single cell transcriptomics and proteomics, simultaneous multi-omics level probing still remains challenging. As an alternative to combined capture of biological data, in this review, we explore current and upcoming methods for post-hoc network inference and integration with an emphasis on single cell transcriptomics and proteomics. By examining various approaches, from probabilistic models to graph-based algorithms, we outline the challenges and potential strategies for effectively combining biological data types while simultaneously highlighting the importance of model validation. With this review, we aim to inform readers of the breadth of tools currently available for the purpose-specific generation of heterogeneous multi-layer networks.
A Grid Cell-Inspired Structured Vector Algebra for Cognitive Maps
Authors: Sven Krausse, Emre Neftci, Friedrich T. Sommer, Alpha Renner
Date: 2025-03-11 00:00:00

The entorhinal-hippocampal formation is the mammalian brain's navigation system, encoding both physical and abstract spaces via grid cells. This system is well-studied in neuroscience, and its efficiency and versatility make it attractive for applications in robotics and machine learning. While continuous attractor networks (CANs) successfully model entorhinal grid cells for encoding physical space, integrating both continuous spatial and abstract spatial computations into a unified framework remains challenging. Here, we attempt to bridge this gap by proposing a mechanistic model for versatile information processing in the entorhinal-hippocampal formation inspired by CANs and Vector Symbolic Architectures (VSAs), a neuro-symbolic computing framework. The novel grid-cell VSA (GC-VSA) model employs a spatially structured encoding scheme with 3D neuronal modules mimicking the discrete scales and orientations of grid cell modules, reproducing their characteristic hexagonal receptive fields. In experiments, the model demonstrates versatility in spatial and abstract tasks: (1) accurate path integration for tracking locations, (2) spatio-temporal representation for querying object locations and temporal relations, and (3) symbolic reasoning using family trees as a structured test case for hierarchical relationships.
Risk and Protective Factors in Parkinsons Disease
Authors: Iman Beheshti
Date: 2025-03-10 00:00:00
Tasks: Management

Understanding the risk and protective factors associated with Parkinsons disease (PD) is crucial for improving outcomes for patients, individuals at risk, healthcare providers, and healthcare systems. Studying these factors not only enhances our knowledge of the disease but also aids in developing effective prevention, management, and treatment strategies. This paper reviews the key risk and protective factors associated with PD, with a particular focus on the biological mechanisms underlying these factors. Risk factors include genetic mutations, racial predispositions, and environmental exposures, all of which contribute to an increased likelihood of developing PD or accelerating disease progression. Conversely, protective factors such as regular physical exercise, adherence to a Mediterranean diet, and higher urate levels have demonstrated potential to reduce inflammation and support mitochondrial function, thereby mitigating disease risk. However, identifying and validating these factors presents significant challenges. To overcome challenges, we propose several solutions and recommendations. Future research should prioritize the development of standardized biomarkers for early diagnosis, investigate gene-environment interactions in greater depth, and refine animal models to better mimic human PD pathology. Additionally, we offer actionable recommendations for PD prevention and management, tailored to healthy individuals, patients diagnosed with PD, and healthcare systems. These strategies aim to improve clinical outcomes, enhance quality of life, and optimize healthcare delivery for PD.
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
Authors: Jing Zhang, Xiaowei Yu, Tong Chen, Chao Cao, Mingheng Chen, Yan Zhuang, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu
Date: 2025-03-05 00:00:00
Tasks: Mixture-of-Experts

The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD and LBD is crucial because they require different treatment approaches, but this is challenging due to significant clinical overlap, heterogeneity, complex pathogenesis, and the rarity of LBD. While recent advances in artificial intelligence (AI) demonstrate powerful learning capabilities and offer new hope for accurate diagnosis, existing methods primarily focus on designing "neural-level networks". Our work represents a pioneering effort in modeling system-level artificial neural network called BrainNet-MoE for brain modeling and diagnosing. Inspired by the brain's hierarchical organization of bottom-up sensory integration and top-down control, we design a set of disease-specific expert groups to process brain sub-network under different condition, A disease gate mechanism guides the specializa-tion of expert groups, while a transformer layer enables communication be-tween all sub-networks, generating a comprehensive whole-brain represen-tation for downstream disease classification. Experimental results show superior classification accuracy with interpretable insights into how brain sub-networks contribute to different neurodegenerative conditions.
HIV/AIDS Suppression in North America: Intervention Plans and Cost-Effectiveness of UNAIDS 90-90-90 and 95-95-95 Targets
Authors: Nuzhat Nuari Khan Rivu, Md Kamrujjaman, Asif Iqbal
Date: 2025-02-19 00:00:00

This study utilizes mathematical models to assess progress toward achieving the UNAIDS 90-90-90 and 95-95-95 targets aimed at managing and eradicating HIV/AIDS. It contrasts stochastic and deterministic models, focusing on their utility in optimizing public health strategies. Stochastic models account for real-world unpredictability, offering more realistic insights compared to deterministic approaches. The 95-95-95 targets aim for 95\% of people living with HIV to know their status, 95\% of those diagnosed to receive antiretroviral therapy (ART), and 95\% of those on ART to achieve viral suppression. These benchmarks are critical for reducing transmission and improving health outcomes. This analysis establishes the basic reproduction number ($R_0$) to guide interventions and examines the stability of disease-free and endemic equilibria, providing a foundation for applying optimal control strategies to minimize HIV prevalence effectively and cost-efficiently. Moreover, the data for this study was sourced from the official UNAIDS website, focusing on North America. An innovative feature of this study is the application of the Stochastic method, which enhances model accuracy and operational efficiency in simulating HIV transmission under various interventions. This research offers actionable insights for policymakers and contributes to global efforts to achieve the 95-95-95 targets by 2030, advancing the fight against HIV/AIDS.
SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction
Authors: Yigang Chen, Xiang Ji, Ziyue Zhang, Yuming Zhou, Yang-Chi-Dung Lin, Hsi-Yuan Huang, Tao Zhang, Yi Lai, Ke Chen, Chang Su, Xingqiao Lin, Zihao Zhu, Yanggyi Zhang, Kangping Wei, Jiehui Fu, Yixian Huang, Shidong Cui, Shih-Chung Yen, Ariel Warshel, Hsien-Da Huang
Date: 2025-03-12 00:00:00
Tasks: Diversity, Drug Discovery

Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. Constructed from 13 public repositories, the SCOPE dataset expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. The SCOPE model integrates three-dimensional protein and compound representations, graph neural networks, and bilinear attention mechanisms to effectively capture cross domain interaction patterns, significantly outperforming state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further validate its effectiveness by experimentally identifying anticancer targets of Ginsenoside Rh1. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.
How To Make Your Cell Tracker Say "I dunno!"
Authors: Richard D. Paul, Johannes Seiffarth, David Rügamer, Hanno Scharr, Katharina Nöh
Date: 2025-03-12 00:00:00
Tasks: Bayesian Inference, Cell Tracking, Uncertainty Quantification

Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties.
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations
Authors: Jiancheng Zhang, Xiangting Li, Xiaolu Guo, Zhaoyi You, Lucas Böttcher, Alex Mogilner, Alexander Hoffman, Tom Chou, Mingtao Xia
Date: 2025-03-12 00:00:00
Tasks: Time Series Analysis

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions (``intrinsic noise'') and (ii) heterogeneity of cellular states across different cells that are influenced by external factors (``extrinsic noise''). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NF$\kappa$B signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical.
Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer's Disease
Authors: Kobra Rabiei, Jeffrey R. Petrella, Suzanne Lenhart, Chun Liu, P. Murali Doraiswamy, Wenrui Hao
Date: 2025-03-11 00:00:00
Tasks: parameter estimation

Alzheimer's disease (AD) is driven by the accumulation of amyloid-beta (Abeta) proteins in the brain, leading to memory loss and cognitive decline. While monoclonal antibodies targeting Abetahave been approved, optimizing their use to maximize benefits while minimizing side effects remains a challenge. This study develops a mathematical model to describe Abeta aggregation, capturing its progression from monomers to toxic oligomers, protofibrils, and fibrils using mass-action kinetics and coarse-grained modeling. The model is calibrated with experimental data, incorporating parameter estimation and sensitivity analysis to ensure accuracy. An optimal control framework is introduced to determine the best drug dosing strategy that reduces toxic Abeta aggregates while minimizing adverse effects, such as amyloid-related imaging abnormalities (ARIA). Results indicate that Donanemab achieves the greatest reduction in fibrils. This work provides a quantitative framework for optimizing AD treatment strategies, offering insights into balancing therapeutic efficacy and safety.