pwc-archive/papers-with-abstracts huggingface dataset:
A new and flexible class of sharp asymptotic time-uniform confidence sequences
Authors: Felix Gnettner, Claudia Kirch
Date: 2025-02-14 00:00:00
Tasks: valid

Confidence sequences are anytime-valid analogues of classical confidence intervals that do not suffer from multiplicity issues under optional continuation of the data collection. As in classical statistics, asymptotic confidence sequences are a nonparametric tool showing under which high-level assumptions asymptotic coverage is achieved so that they also give a certain robustness guarantee against distributional deviations. In this paper, we propose a new flexible class of confidence sequences yielding sharp asymptotic time-uniform confidence sequences under mild assumptions. Furthermore, we highlight the connection to corresponding sequential testing problems and detail the underlying limit theorem.
Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
Authors: Syamantak Kumar, Dheeraj Nagaraj, Purnamrita Sarkar
Date: 2025-02-14 00:00:00

Diffusion models generate samples by estimating the score function of the target distribution at various noise levels. The model is trained using samples drawn from the target distribution, progressively adding noise. In this work, we establish the first (nearly) dimension-free sample complexity bounds for learning these score functions, achieving a double exponential improvement in dimension over prior results. A key aspect of our analysis is the use of a single function approximator to jointly estimate scores across noise levels, a critical feature of diffusion models in practice which enables generalization across timesteps. Our analysis introduces a novel martingale-based error decomposition and sharp variance bounds, enabling efficient learning from dependent data generated by Markov processes, which may be of independent interest. Building on these insights, we propose Bootstrapped Score Matching (BSM), a variance reduction technique that utilizes previously learned scores to improve accuracy at higher noise levels. These results provide crucial insights into the efficiency and effectiveness of diffusion models for generative modeling.
Forecasting time series with constraints
Authors: Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer, Yannig Goude
Date: 2025-02-14 00:00:00
Tasks: Additive models, Benchmarking, Demand Forecasting, Time Series, Time Series Forecasting

Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for integrating and combining linear constraints in time series forecasting. Within this framework, we show that the exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra alone. This approach allows for highly scalable implementations optimized for GPUs. We validate the proposed methodology through extensive benchmarking on real-world tasks, including electricity demand forecasting and tourism forecasting, achieving state-of-the-art performance.
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
Authors: Jiayang Zhang, Xianyuan Liu, Wei Wu, Sina Tabakhi, Wenrui Fan, Shuo Zhou, Kang Lan Tee, Tuck Seng Wong, Haiping Lu
Date: 2025-02-17 00:00:00
Tasks: Interpretable Machine Learning

Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to form a VLP, is critical for vaccine optimisation. However, current experimental methods to determine stoichiometry are time-consuming and require highly purified proteins. To efficiently classify stoichiometry classes in proteins, we curate a new dataset and propose an interpretable, data-driven pipeline leveraging linear machine learning models. We also explore the impact of feature encoding on model performance and interpretability, as well as methods to identify key protein sequence features influencing classification. The evaluation of our pipeline demonstrates that it can classify stoichiometry while revealing protein features that possibly influence VLP assembly. The data and code used in this work are publicly available at https://github.com/Shef-AIRE/StoicIML.
Deep Learning of Proteins with Local and Global Regions of Disorder
Authors: Oufan Zhang, Zi Hao Liu, Julie D Forman-Kay, Teresa Head-Gordon
Date: 2025-02-17 00:00:00
Tasks: Protein Structure Prediction

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder.
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening
Authors: Gen Zhou, Sugitha Janarthanan, Yutong Lu, Pingzhao Hu
Date: 2025-02-16 00:00:00
Tasks: Contrastive Learning, Molecular Property Prediction, Property Prediction, Representation Learning

Due to the rise in antimicrobial resistance, identifying novel compounds with antibiotic potential is crucial for combatting this global health issue. However, traditional drug development methods are costly and inefficient. Recognizing the pressing need for more effective solutions, researchers have turned to machine learning techniques to streamline the prediction and development of novel antibiotic compounds. While foundation models have shown promise in antibiotic discovery, current mainstream efforts still fall short of fully leveraging the potential of multimodal molecular data. Recent studies suggest that contrastive learning frameworks utilizing multimodal data exhibit excellent performance in representation learning across various domains. Building upon this, we introduce CL-MFAP, an unsupervised contrastive learning (CL)-based multimodal foundation (MF) model specifically tailored for discovering small molecules with potential antibiotic properties (AP) using three types of molecular data. This model employs 1.6 million bioactive molecules with drug-like properties from the ChEMBL dataset to jointly pretrain three encoders: (1) a transformer-based encoder with rotary position embedding for processing SMILES strings; (2) another transformer-based encoder, incorporating a novel bi-level routing attention mechanism to handle molecular graph representations; and (3) a Morgan fingerprint encoder using a multilayer perceptron, to achieve the contrastive learning purpose. The CL-MFAP outperforms baseline models in antibiotic property prediction by effectively utilizing different molecular modalities and demonstrates superior domain-specific performance when fine-tuned for antibiotic-related property prediction tasks.
ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule Optimization
Authors: Xuefeng Liu, Songhao Jiang, Bo Li, Rick Stevens
Date: 2025-02-15 00:00:00

Large Language Models (LLMs) employ three popular training approaches: Masked Language Models (MLM), Causal Language Models (CLM), and Sequence-to-Sequence Models (seq2seq). However, each approach has its strengths and limitations, and faces challenges in addressing specific tasks that require controllable and bidirectional generation, such as drug optimization. To address this challenge, inspired by the biological processes of growth and evolution, which involve the expansion, shrinking, and mutation of sequences, we introduce ControllableGPT. This initiative represents the first effort to combine the advantages of MLM, CLM, and seq2seq into a single unified, controllable GPT framework. It enables the precise management of specific locations and ranges within a sequence, allowing for expansion, reduction, or mutation over chosen or random lengths, while maintaining the integrity of any specified positions or subsequences. In this work, we designed ControllableGPT for drug optimization from the ground up, which included proposing the Causally Masked Seq2seq (CMS) objective, developing the training corpus, introducing a novel pre-training approach, and devising a unique generation process. We demonstrate the effectiveness and controllability of ControllableGPT by conducting experiments on drug optimization tasks for both viral and cancer benchmarks, surpassing competing baselines.
Fluorescence Phasor Analysis: Basic Principles and Biophysical Applications
Authors: Alvaro A. Recoulat Angelini, Leonel Malacrida, F. Luis Gonzalez Flecha
Date: 2025-02-14 00:00:00
Tasks: Protein Folding

Fluorescence is one of the most widely used techniques in biological sciences. Its exceptional sensitivity and versatility make it a tool of first choice for quantitative studies in biophysics. The concept of phasors, originally introduced by Charles Steinmetz in the late 19th century for analyzing alternating current circuits, has since found applications across diverse disciplines, including fluorescence spectroscopy. The main idea behind fluorescence phasors was posited by Gregorio Weber in 1981. By analyzing the complementary nature of pulse and phase fluorometry data, he shows that two magnitudes -denoted as G and S- derived from the frequency-domain fluorescence measurements correspond to the real and imaginary part of the Fourier transform of the fluorescence intensity in the time domain. This review provides a historical perspective on how the concept of phasors originates and how it integrates into fluorescence spectroscopy. We discuss their fundamental algebraic properties, which enable intuitive model-free analysis of fluorescence data despite the complexity of the underlying phenomena. Some applications in biophysics illustrate the power of this approach in studying diverse phenomena, including protein folding, protein interactions, phase transitions in lipid mixtures and formation of high-order structures in nucleic acids.
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Authors: Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy
Date: 2025-02-13 00:00:00

Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects -- a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlux enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research. Project page: https://yuhui-zh15.github.io/CellFlux/.
Stochastic Mutation Theory of SARS-CoV-2 Variants
Authors: Liaofu Luo, Jun Lv
Date: 2025-02-13 00:00:00

Predicting the future evolutionary trajectory of SARS-CoV-2 remains a critical challenge, particularly due to the pivotal role of spike protein mutations. Developing an evolutionary model capable of continuously integrating new experimental data is an urgent priority. By employing well-founded assumptions for mutant representation (four-letter and two-letter formats) and the n-mer distance algorithm, we constructed an evolutionary tree of SARS-CoV-2 mutations that accurately reflects observed viral strain evolution. We introduce a stochastic method for generating new strains on this tree based on spike protein mutations. For a given set A of existing mutation sites, we define a set X of x randomly generated sites on the spike protein. Our analysis reveals that the position of a generated strain on the tree is determined by x. Through large-scale stochastic sampling, we predict the emergence of new macro-lineages. As x increases, the proportions of macro-lineages shift: lineage O surpasses lineage N, lineage P overtakes O, and ultimately, new lineage Q surpasses P. We identified threshold values of x that distinguish between macro-lineages. Furthermore, we demonstrate that the linear regression of the number of mutated sites (x) against sample collection dates (t) provides a robust approximation, enabling the prediction of new lineage emergence based on the x-t relationship. To conclude, we demonstrated that the SARS-CoV-2 evolution adheres to statistical principles: the emergence of new strains on the evolutionary tree can be driven by randomly generated spike protein sites; and the large-scale stochastic sampling uncovers evolutionary patterns governing the emergence of diverse macro-lineages.
DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations
Authors: Anis Bourou, Saranga Kingkor Mahanta, Thomas Boyer, Valérie Mezger, Auguste Genovesio
Date: 2025-02-12 00:00:00
Tasks: Drug Discovery

In recent years, deep learning models have been extensively applied to biological data across various modalities. Discriminative deep learning models have excelled at classifying images into categories (e.g., healthy versus diseased, treated versus untreated). However, these models are often perceived as black boxes due to their complexity and lack of interpretability, limiting their application in real-world biological contexts. In biological research, explainability is essential: understanding classifier decisions and identifying subtle differences between conditions are critical for elucidating the effects of treatments, disease progression, and biological processes. To address this challenge, we propose DiffEx, a method for generating visually interpretable attributes to explain classifiers and identify microscopic cellular variations between different conditions. We demonstrate the effectiveness of DiffEx in explaining classifiers trained on natural and biological images. Furthermore, we use DiffEx to uncover phenotypic differences within microscopy datasets. By offering insights into cellular variations through classifier explanations, DiffEx has the potential to advance the understanding of diseases and aid drug discovery by identifying novel biomarkers.
Single-Cell Proteomics Using Mass Spectrometry
Authors: Amanda Momenzadeh, Jesse G. Meyer
Date: 2025-02-17 00:00:00
Tasks: Imputation, Missing Values

Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
Authors: Mingqian Ma, Guoqing Liu, Chuan Cao, Pan Deng, Tri Dao, Albert Gu, Peiran Jin, Zhao Yang, Yingce Xia, Renqian Luo, Pipi Hu, Zun Wang, Yuan-Jyue Chen, Haiguang Liu, Tao Qin
Date: 2025-02-15 00:00:00
Tasks: Language Modeling, Language Modelling, State Space Models

Advances in natural language processing and large language models have sparked growing interest in modeling DNA, often referred to as the "language of life". However, DNA modeling poses unique challenges. First, it requires the ability to process ultra-long DNA sequences while preserving single-nucleotide resolution, as individual nucleotides play a critical role in DNA function. Second, success in this domain requires excelling at both generative and understanding tasks: generative tasks hold potential for therapeutic and industrial applications, while understanding tasks provide crucial insights into biological mechanisms and diseases. To address these challenges, we propose HybriDNA, a decoder-only DNA language model that incorporates a hybrid Transformer-Mamba2 architecture, seamlessly integrating the strengths of attention mechanisms with selective state-space models. This hybrid design enables HybriDNA to efficiently process DNA sequences up to 131kb in length with single-nucleotide resolution. HybriDNA achieves state-of-the-art performance across 33 DNA understanding datasets curated from the BEND, GUE, and LRB benchmarks, and demonstrates exceptional capability in generating synthetic cis-regulatory elements (CREs) with desired properties. Furthermore, we show that HybriDNA adheres to expected scaling laws, with performance improving consistently as the model scales from 300M to 3B and 7B parameters. These findings underscore HybriDNA's versatility and its potential to advance DNA research and applications, paving the way for innovations in understanding and engineering the "language of life".
Alias4SBML: A Python Package for Generating Alias Nodes in SBML Models
Authors: Adel Heydarabadipour, Herbert M Sauro
Date: 2025-02-16 00:00:00

Interpreting biological networks becomes challenging when molecular components, such as genes or proteins, participate in numerous interactions, resulting in densely connected regions and overlapping interactions that obscure functional relationships and biological insights. To address this, we introduce Alias4SBML, a Python package that enhances SBML model visualizations by generating alias nodes-duplicate representations of highly connected molecular components-to redistribute interactions and reduce visual congestion. Applying Alias4SBML to the SBML models, including one with 59 species and 41 reactions and another with 701 species and 505 reactions, demonstrated significant improvements in readability, with edge length reductions of up to 50.88 %. Our approach preserves the structural integrity of the network while facilitating clearer interpretation of complex biological systems, offering a flexible and scalable solution for visualizing biological models more efficiently.
Personal Danger Signals Reprocessing: New Online Group Intervention for Chronic Pain
Authors: Carmit Himmelblau Gat, Natalia Polyviannaya, Pavel Goldstein
Date: 2025-02-17 00:00:00

Chronic pain is a significant global health issue, with many patients experiencing persistent pain despite no identifiable organic cause, classified as nociplastic pain. Increasing evidence highlights the role of danger signal processing in the maintenance of chronic pain. In response, we developed Personal Danger Signals Reprocessing (PDSR), an online, group-based intervention designed to modify these mechanisms using coaching techniques to enhance accessibility and affordability. This study evaluated the efficacy of PDSR in reducing pain and mental health comorbidities. A cohort of women (N=19, mean age 43) participated in an 8-week online program, receiving weekly sessions on chronic pain mechanisms within a systemic framework. Outcomes were assessed at three time points: pre-intervention, mid-intervention, and post-intervention. A waiting list group (N=20, mean age 43.5) completed assessments at the same intervals. Participants in the PDSR group showed significant pain reduction (p < .001), with moderate to large effects observed at mid-intervention (Cohen's D = 0.7) and post-intervention (Cohen's D = 1.5) compared to controls. Pain interference significantly decreased (p < .01), with large reductions in the PDSR group (Cohen's D = -1.7, p < .0001). Well-being also improved substantially (p < .001, Cohen's D = 1.7-1.8). Secondary outcomes, including pain catastrophizing, sleep interference, anxiety, and depressive symptoms, consistently improved (all p-values < .01). Findings suggest PDSR is an effective, scalable intervention for reducing pain, improving function, and enhancing well-being in individuals with chronic pain.
Using economic value signals from primate prefrontal cortex in neuro-engineering applications
Authors: Tevin C. Rouse, Shira M. Lupkin, Vincent B. McGinty
Date: 2025-02-17 00:00:00
Tasks: Decision Making

Neural signals related to movement can be measured from intracranial recordings and used in brain-machine interface devices (BMI) to restore physical function in impaired patients. In this study, we explore the use of more abstract neural signals related to economic value in a BMI context. Using data collected from the orbitofrontal cortex in non-human primates, we develop deep learning-based neural decoders that can predict the monkey's choice in a value-based decision-making task. Out-of-sample performance was improved by augmenting the training set with synthesized data, showing the feasibility of using limited training data. We further demonstrate that we can predict the monkey's choice sooner using a neural forecasting module that is equipped with task-related information. These findings support the feasibility of user preference-informed neuroengineering devices that leverage abstract cognitive signals.