pwc-archive/papers-with-abstracts huggingface dataset:
Convergence-divergence models: Generalizations of phylogenetic trees modeling gene flow over time
Authors: Jonathan D. Mitchell, Barbara R. Holland
Date: 2025-04-10 00:00:00

Phylogenetic trees are simple models of evolutionary processes. They describe conditionally independent divergent evolution of taxa from common ancestors. Phylogenetic trees commonly do not have enough flexibility to adequately model all evolutionary processes. For example, introgressive hybridization, where genes can flow from one taxon to another. Phylogenetic networks model evolution not fully described by a phylogenetic tree. However, many phylogenetic network models assume ancestral taxa merge instantaneously to form ``hybrid'' descendant taxa. In contrast, our convergence-divergence models retain a single underlying ``principal'' tree, but permit gene flow over arbitrary time frames. Alternatively, convergence-divergence models can describe other biological processes leading to taxa becoming more similar over a time frame, such as replicated evolution. Here we present novel maximum likelihood-based algorithms to infer most aspects of $N$-taxon convergence-divergence models, many consistently, using a quartet-based approach. The algorithms can be applied to multiple sequence alignments restricted to genes or genomic windows or to gene presence/absence datasets.
Sparsified-Learning for Heavy-Tailed Locally Stationary Processes
Authors: Yingjie Wang, Mokhtar Z. Alaya, Salim Bouzebda, Xinsheng Liu
Date: 2025-04-08 00:00:00
Tasks: Sparse Learning

Sparsified Learning is ubiquitous in many machine learning tasks. It aims to regularize the objective function by adding a penalization term that considers the constraints made on the learned parameters. This paper considers the problem of learning heavy-tailed LSP. We develop a flexible and robust sparse learning framework capable of handling heavy-tailed data with locally stationary behavior and propose concentration inequalities. We further provide non-asymptotic oracle inequalities for different types of sparsity, including $\ell_1$-norm and total variation penalization for the least square loss.
Bounds in Wasserstein Distance for Locally Stationary Functional Time Series
Authors: Jan Nino G. Tinio, Mokhtar Z. Alaya, Salim Bouzebda
Date: 2025-04-08 00:00:00
Tasks: Time Series

Functional time series (FTS) extend traditional methodologies to accommodate data observed as functions/curves. A significant challenge in FTS consists of accurately capturing the time-dependence structure, especially with the presence of time-varying covariates. When analyzing time series with time-varying statistical properties, locally stationary time series (LSTS) provide a robust framework that allows smooth changes in mean and variance over time. This work investigates Nadaraya-Watson (NW) estimation procedure for the conditional distribution of locally stationary functional time series (LSFTS), where the covariates reside in a semi-metric space endowed with a semi-metric. Under small ball probability and mixing condition, we establish convergence rates of NW estimator for LSFTS with respect to Wasserstein distance. The finite-sample performances of the model and the estimation method are illustrated through extensive numerical experiments both on functional simulated and real data.
Deep spatio-temporal point processes: Advances and new directions
Authors: Xiuyuan Cheng, Zheng Dong, Yao Xie
Date: 2025-04-08 00:00:00
Tasks: Epidemiology, Point Processes

Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, nonstationary dynamics. Recent innovations integrate deep neural architectures -- either by modeling the conditional intensity function directly or by learning flexible, data-driven influence kernels, substantially broadening their expressive power. This article reviews the development of the deep influence kernel approach, which enjoys statistical explainability, since the influence kernel remains in the model to capture the spatiotemporal propagation of event influence and its impact on future events, while also possessing strong expressive power, thereby benefiting from both worlds. We explain the main components in developing deep kernel point processes, leveraging tools such as functional basis decomposition and graph neural networks to encode complex spatial or network structures, as well as estimation using both likelihood-based and likelihood-free methods, and address computational scalability for large-scale data. We also discuss the theoretical foundation of kernel identifiability. Simulated and real-data examples highlight applications to crime analysis, earthquake aftershock prediction, and sepsis prediction modeling, and we conclude by discussing promising directions for the field.
SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events
Authors: Yichen Kelly Chen, Sören Dittmer, Kinga Bernatowicz, Josep Arús-Pous, Kamen Bliznashki, John Aston, James H. F. Rudd, Carola-Bibiane Schönlieb, James Jones, Michael Roberts
Date: 2025-04-07 00:00:00

We propose a neural-network based survival model (SurvSurf) specifically designed for direct and simultaneous probabilistic prediction of the first hitting time of sequential events from baseline. Unlike existing models, SurvSurf is theoretically guaranteed to never violate the monotonic relationship between the cumulative incidence functions of sequential events, while allowing nonlinear influence from predictors. It also incorporates implicit truths for unobserved intermediate events in model fitting, and supports both discrete and continuous time and events. We also identified a variant of the Integrated Brier Score (IBS) that showed robust correlation with the mean squared error (MSE) between the true and predicted probabilities by accounting for implied truths about the missing intermediate events. We demonstrated the superiority of SurvSurf compared to modern and traditional predictive survival models in two simulated datasets and two real-world datasets, using MSE, the more robust IBS and by measuring the extent of monotonicity violation.
Balancing Complexity and Informativeness in LLM-Based Clustering: Finding the Goldilocks Zone
Authors: Justin Miller, Tristram Alexander
Date: 2025-04-06 00:00:00
Tasks: Clustering, Informativeness, Semantic Similarity, Semantic Textual Similarity

The challenge of clustering short text data lies in balancing informativeness with interpretability. Traditional evaluation metrics often overlook this trade-off. Inspired by linguistic principles of communicative efficiency, this paper investigates the optimal number of clusters by quantifying the trade-off between informativeness and cognitive simplicity. We use large language models (LLMs) to generate cluster names and evaluate their effectiveness through semantic density, information theory, and clustering accuracy. Our results show that Gaussian Mixture Model (GMM) clustering on embeddings generated by a LLM, increases semantic density compared to random assignment, effectively grouping similar bios. However, as clusters increase, interpretability declines, as measured by a generative LLM's ability to correctly assign bios based on cluster names. A logistic regression analysis confirms that classification accuracy depends on the semantic similarity between bios and their assigned cluster names, as well as their distinction from alternatives. These findings reveal a "Goldilocks zone" where clusters remain distinct yet interpretable. We identify an optimal range of 16-22 clusters, paralleling linguistic efficiency in lexical categorization. These insights inform both theoretical models and practical applications, guiding future research toward optimising cluster interpretability and usefulness.
PLM-eXplain: Divide and Conquer the Protein Embedding Space
Authors: Jan van Eck, Dea Gogishvili, Wilson Silva, Sanne Abeln
Date: 2025-04-09 00:00:00
Tasks: Prediction

Protein language models (PLMs) have revolutionised computational biology through their ability to generate powerful sequence representations for diverse prediction tasks. However, their black-box nature limits biological interpretation and translation to actionable insights. We present an explainable adapter layer - PLM-eXplain (PLM-X), that bridges this gap by factoring PLM embeddings into two components: an interpretable subspace based on established biochemical features, and a residual subspace that preserves the model's predictive power. Using embeddings from ESM2, our adapter incorporates well-established properties, including secondary structure and hydropathy while maintaining high performance. We demonstrate the effectiveness of our approach across three protein-level classification tasks: prediction of extracellular vesicle association, identification of transmembrane helices, and prediction of aggregation propensity. PLM-X enables biological interpretation of model decisions without sacrificing accuracy, offering a generalisable solution for enhancing PLM interpretability across various downstream applications. This work addresses a critical need in computational biology by providing a bridge between powerful deep learning models and actionable biological insights.
Comprehensive Insights into the Cholesterol-Mediated Modulation of Membrane Function through Molecular Dynamics Simulations
Authors: Ehsaneh Khodadadi, Ehsan Khodadadi, Parth Chaturvedi, Mahmoud Moradi
Date: 2025-04-07 00:00:00

Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. It is necessary for a variety of membranes, including planar bilayers, liposomes, curved bilayers, nanodiscs, and proteoliposomes. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein--membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, adding rigidity and fluidity. It modulates membrane curvature in curved bilayers and vesicles, and stabilises low-curvature regions, which are important for structural integrity. In liposomes, cholesterol facilitates drug encapsulation and release by controlling bilayer flexibility and stability. In nanodiscs, cholesterol enhances structural integrity and protein compatibility, which enables the investigation of protein--lipid interactions under physiological conditions. In proteoliposomes, cholesterol regulates the conformational stability of embedded proteins that have implications for protein--lipid interaction. Developments in molecular dynamics (MD) techniques, from coarse-grained to all-atom simulations, have shown how cholesterol modulates lipid tail ordering, membrane curvature, and flip-flop behaviour in response to concentration. Such simulations provide insights into the mechanisms underlying membrane-associated diseases, aiding in the design of efficient drug delivery systems. In this review, we combine results from MD simulations to provide a synoptic explanation of cholesterols complex function in regulating membrane behaviour.
Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation
Authors: Mohammad Amaan Sayeed, Engin Tekin, Maryam Nadeem, Nancy A. ElNaker, Aahan Singh, Natalia Vassilieva, Boulbaba Ben Amor
Date: 2025-04-06 00:00:00
Tasks: Decoder, Protein Design

Unlocking the next generation of biotechnology and therapeutic innovation demands overcoming the inherent complexity and resource-intensity of conventional protein engineering methods. Recent GenAI-powered computational techniques often rely on the availability of the target protein's 3D structures and specific binding sites to generate high-affinity binders, constraints exhibited by models such as AlphaProteo and RFdiffusion. In this work, we explore the use of Protein Language Models (pLMs) for high-affinity binder generation. We introduce Prot42, a novel family of Protein Language Models (pLMs) pretrained on vast amounts of unlabeled protein sequences. By capturing deep evolutionary, structural, and functional insights through an advanced auto-regressive, decoder-only architecture inspired by breakthroughs in natural language processing, Prot42 dramatically expands the capabilities of computational protein design based on language only. Remarkably, our models handle sequences up to 8,192 amino acids, significantly surpassing standard limitations and enabling precise modeling of large proteins and complex multi-domain sequences. Demonstrating powerful practical applications, Prot42 excels in generating high-affinity protein binders and sequence-specific DNA-binding proteins. Our innovative models are publicly available, offering the scientific community an efficient and precise computational toolkit for rapid protein engineering.
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
Authors: Xiaokun Liu, Sayedmohammadreza Rastegari, Yijun Huang, Sxe Chang Cheong, Weikang Liu, Wenjie Zhao, Qihao Tian, Hongming Wang, Yingjie Guo, Shuo Zhou, Sina Tabakhi, Xianyuan Liu, Zheqing Zhu, Wei Sang, Haiping Lu
Date: 2025-04-04 00:00:00
Tasks: Drug Design

In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein-metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. Key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the ''black box'' nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein-metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein-protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding.
Mathematical model of Nucleocytoplasmic Transport and Nuclear-to-Cell Ratio in a growing cell
Authors: Xuesong Bai, Thomas G. Fai
Date: 2025-04-08 00:00:00

It has been observed that the growth of the nucleus and the cytoplasm is coordinated during cell growth, resulting in a nearly constant nuclear-to-cell volume ratio (N/C) throughout the cell cycle. Previous studies have shown that the N/C ratio is determined by the ratio between the number of proteins in the nucleus and the total number of proteins in the cell. These observations suggest the importance of the nucleocytoplasmic transport process in nuclear size by regulating protein concentrations in the nucleus and cytoplasm. This paper combines a biophysical model of Ran-mediated nucleocytoplasmic transport and a simple cell growth model to provide insights into several key aspects of the N/C ratio homeostasis in growing cells. Our model shows that the permeability of the nuclear envelope needs to grow in line with the cell to maintain a nearly constant N/C ratio, that several parameters involved in the nucleocytoplasmic transport mechanism and gene translation significantly affect the N/C ratio, and that Ran may potentially compensate for the lack of NTF2 in the nucleocytoplasmic transport mechanism to maintain a viable N/C ratio. However, this compensation is possible only if RanGDP is allowed to translocate through the nuclear envelope independently of NTF2.
An LLM-Driven Multi-Agent Debate System for Mendelian Diseases
Authors: Xinyang Zhou, Yongyong Ren, Qianqian Zhao, Daoyi Huang, Xinbo Wang, Tingting Zhao, Zhixing Zhu, Wenyuan He, Shuyuan Li, Yan Xu, Yu Sun, Yongguo Yu, Shengnan Wu, Jian Wang, Guangjun Yu, Dake He, Bo Ban, Hui Lu
Date: 2025-04-10 00:00:00
Tasks: Diagnostic, Language Modeling, Language Modelling

Accurate diagnosis of Mendelian diseases is crucial for precision therapy and assistance in preimplantation genetic diagnosis. However, existing methods often fall short of clinical standards or depend on extensive datasets to build pretrained machine learning models. To address this, we introduce an innovative LLM-Driven multi-agent debate system (MD2GPS) with natural language explanations of the diagnostic results. It utilizes a language model to transform results from data-driven and knowledge-driven agents into natural language, then fostering a debate between these two specialized agents. This system has been tested on 1,185 samples across four independent datasets, enhancing the TOP1 accuracy from 42.9% to 66% on average. Additionally, in a challenging cohort of 72 cases, MD2GPS identified potential pathogenic genes in 12 patients, reducing the diagnostic time by 90%. The methods within each module of this multi-agent debate system are also replaceable, facilitating its adaptation for diagnosing and researching other complex diseases.
Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
Authors: Riselda Kodra, Hadjer Benmeziane, Irem Boybat, William Andrew Simon
Date: 2025-04-09 00:00:00

The ability to quickly and accurately identify microbial species in a sample, known as metagenomic profiling, is critical across various fields, from healthcare to environmental science. This paper introduces a novel method to profile signals coming from sequencing devices in parallel with determining their nucleotide sequences, a process known as basecalling, via a multi-objective deep neural network for simultaneous basecalling and multi-class genome classification. We introduce a new loss strategy where losses for basecalling and classification are back-propagated separately, with model weights combined for the shared layers, and a pre-configured ranking strategy allowing top-K species accuracy, giving users flexibility to choose between higher accuracy or higher speed at identifying the species. We achieve state-of-the-art basecalling accuracies, while classification accuracies meet and exceed the results of state-of-the-art binary classifiers, attaining an average of 92.5%/98.9% accuracy at identifying the top-1/3 species among a total of 17 genomes in the Wick bacterial dataset. The work presented here has implications for future studies in metagenomic profiling by accelerating the bottleneck step of matching the DNA sequence to the correct genome.
ViralQC: A Tool for Assessing Completeness and Contamination of Predicted Viral Contigs
Authors: Cheng Peng, Jiayu Shang, Jiaojiao Guan, Yanni Sun
Date: 2025-04-08 00:00:00

Motivation: Viruses represent the most abundant biological entities on the planet and play vital roles in diverse ecosystems. Cataloging viruses across various environments is essential for understanding their properties and functions. Metagenomic sequencing has emerged as the most comprehensive method for virus discovery, enabling the sequencing of all genetic materials, including viruses, from host or environmental samples. However, distinguishing viral sequences from the vast background of cellular organism-derived reads in metagenomic data remains a significant challenge. While several learning-based tools, such as VirSorter2 and geNomad, have shown promise in identifying viral contigs, they often experience varying degrees of false positive rates due to noise in sequencing and assembly, shared genes between viruses and their hosts, and the formation of proviruses within host genomes. This highlights the urgent need for an accurate and efficient method to evaluate the quality of viral contigs. Results: To address these challenges, we introduce ViralQC, a tool designed to assess the quality of reported viral contigs or bins. ViralQC identifies contamination regions within putative viral sequences using foundation models trained on viral and cellular genomes and estimates viral completeness through protein organization alignment. We evaluate ViralQC on multiple datasets and compare its performance against CheckV, the state-of-the-art in virus quality assessment. Notably, ViralQC correctly identifies 38% more contamination than CheckV, while maintaining a median absolute error of only 3%. In addition, ViralQC delivers more accurate results for medium- to high-quality (>50% completeness) contigs, demonstrating its superior performance in completeness estimation.
Leveraging State Space Models in Long Range Genomics
Authors: Matvei Popov, Aymen Kallala, Anirudha Ramesh, Narimane Hennouni, Shivesh Khaitan, Rick Gentry, Alain-Sam Cohen
Date: 2025-04-07 00:00:00
Tasks: Benchmarking, GPU, State Space Models

Long-range dependencies are critical for understanding genomic structure and function, yet most conventional methods struggle with them. Widely adopted transformer-based models, while excelling at short-context tasks, are limited by the attention module's quadratic computational complexity and inability to extrapolate to sequences longer than those seen in training. In this work, we explore State Space Models (SSMs) as a promising alternative by benchmarking two SSM-inspired architectures, Caduceus and Hawk, on long-range genomics modeling tasks under conditions parallel to a 50M parameter transformer baseline. We discover that SSMs match transformer performance and exhibit impressive zero-shot extrapolation across multiple tasks, handling contexts 10 to 100 times longer than those seen during training, indicating more generalizable representations better suited for modeling the long and complex human genome. Moreover, we demonstrate that these models can efficiently process sequences of 1M tokens on a single GPU, allowing for modeling entire genomic regions at once, even in labs with limited compute. Our findings establish SSMs as efficient and scalable for long-context genomic analysis.