pwc-archive/papers-with-abstracts huggingface dataset:
A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
Authors: Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, Alex Mogilner
Date: 2025-03-07 00:00:00

In this work, we propose and analyze a new local time-decoupled squared Wasserstein-2 method for reconstructing the distribution of unknown parameters in dynamical systems. Specifically, we show that a stochastic neural network model, which can be effectively trained by minimizing our proposed local time-decoupled squared Wasserstein-2 loss function, is an effective model for approximating the distribution of uncertain model parameters in dynamical systems. Through several numerical examples, we showcase the effectiveness of our proposed method in reconstructing the distribution of parameters in different dynamical systems.
A characterization of sample adaptivity in UCB data
Authors: Yilun Chen, Jiaqi Lu
Date: 2025-03-06 00:00:00
Tasks: Form

We characterize a joint CLT of the number of pulls and the sample mean reward of the arms in a stochastic two-armed bandit environment under UCB algorithms. Several implications of this result are in place: (1) a nonstandard CLT of the number of pulls hence pseudo-regret that smoothly interpolates between a standard form in the large arm gap regime and a slow-concentration form in the small arm gap regime, and (2) a heuristic derivation of the sample bias up to its leading order from the correlation between the number of pulls and sample means. Our analysis framework is based on a novel perturbation analysis, which is of broader interest on its own.
Kernel-based estimators for functional causal effects
Authors: Yordan P. Raykov, Hengrui Luo, Justin D. Strait, Wasiur R. KhudaBukhsh
Date: 2025-03-06 00:00:00
Tasks: Causal Inference

We propose causal effect estimators based on empirical Fr\'{e}chet means and operator-valued kernels, tailored to functional data spaces. These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification. Using structural assumptions, we obtain compact representations of potential outcomes, enabling scalable estimation of causal effects over time and across covariates. We provide both theoretical, regarding the consistency of functional causal effects, as well as empirical comparison of a range of proposed causal effect estimators. Applications to binary treatment settings with functional outcomes illustrate the framework's utility in biomedical monitoring, where outcomes exhibit complex temporal dynamics. Our estimators accommodate scenarios with registered covariates and outcomes, aligning them to the Fr\'{e}chet means, as well as cases requiring higher-order representations to capture intricate covariate-outcome interactions. These advancements extend causal inference to dynamic and non-linear domains, offering new tools for understanding complex treatment effects in functional data settings.
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration
Authors: Dylan J. Foster, Zakaria Mhammedi, Dhruv Rohatgi
Date: 2025-03-10 00:00:00
Tasks: Efficient Exploration

Language model alignment (or, reinforcement learning) techniques that leverage active exploration -- deliberately encouraging the model to produce diverse, informative responses -- offer the promise of super-human capabilities. However, current understanding of algorithm design primitives for computationally efficient exploration with language models is limited. To better understand how to leverage access to powerful pre-trained generative models to improve the efficiency of exploration, we introduce a new computational framework for RL with language models, in which the learner interacts with the model through a sampling oracle. Focusing on the linear softmax model parameterization, we provide new results that reveal the computational-statistical tradeoffs of efficient exploration: 1. Necessity of coverage: Coverage refers to the extent to which the pre-trained model covers near-optimal responses -- a form of hidden knowledge. We show that coverage, while not necessary for data efficiency, lower bounds the runtime of any algorithm in our framework. 2. Inference-time exploration: We introduce a new algorithm, SpannerSampling, which obtains optimal data efficiency and is computationally efficient whenever the pre-trained model enjoys sufficient coverage, matching our lower bound. SpannerSampling leverages inference-time computation with the pre-trained model to reduce the effective search space for exploration. 3. Insufficiency of training-time interventions: We contrast the result above by showing that training-time interventions that produce proper policies cannot achieve similar guarantees in polynomial time. 4. Computational benefits of multi-turn exploration: Finally, we show that under additional representational assumptions, one can achieve improved runtime (replacing sequence-level coverage with token-level coverage) through multi-turn exploration.
Comparing regularisation paths of (conjugate) gradient estimators in ridge regression
Authors: Laura Hucker, Markus Reiß, Thomas Stark
Date: 2025-03-07 00:00:00
Tasks: regression

We consider standard gradient descent, gradient flow and conjugate gradients as iterative algorithms for minimizing a penalized ridge criterion in linear regression. While it is well known that conjugate gradients exhibit fast numerical convergence, the statistical properties of their iterates are more difficult to assess due to inherent nonlinearities and dependencies. On the other hand, standard gradient flow is a linear method with well known regularizing properties when stopped early. By an explicit non-standard error decomposition we are able to bound the prediction error for conjugate gradient iterates by a corresponding prediction error of gradient flow at transformed iteration indices. This way, the risk along the entire regularisation path of conjugate gradient iterations can be compared to that for regularisation paths of standard linear methods like gradient flow and ridge regression. In particular, the oracle conjugate gradient iterate shares the optimality properties of the gradient flow and ridge regression oracles up to a constant factor. Numerical examples show the similarity of the regularisation paths in practice.
Learning Causal Response Representations through Direct Effect Analysis
Authors: Homer Durand, Gherardo Varando, Gustau Camps-Valls
Date: 2025-03-06 00:00:00
Tasks: Representation Learning

We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a treatment variable. By bridging conditional independence testing with causal representation learning, we formulate an optimisation problem that maximises the evidence against conditional independence between the treatment and outcome, given a conditioning set. This formulation employs flexible regression models tailored to specific applications, creating a versatile framework. The problem is addressed through a generalised eigenvalue decomposition. We show that, under mild assumptions, the distribution of the largest eigenvalue can be bounded by a known $F$-distribution, enabling testable conditional independence. We also provide theoretical guarantees for the optimality of the learned representation in terms of signal-to-noise ratio and Fisher information maximisation. Finally, we demonstrate the empirical effectiveness of our approach in simulation and real-world experiments. Our results underscore the utility of this framework in uncovering direct causal effects within complex, multivariate settings.
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
Authors: Shuang Liu, Yihan Wang, Yifan Zhu, Yibo Miao, Xiao-Shan Gao
Date: 2025-03-06 00:00:00

Wasserstein distributionally robust optimization (WDRO) optimizes against worst-case distributional shifts within a specified uncertainty set, leading to enhanced generalization on unseen adversarial examples, compared to standard adversarial training which focuses on pointwise adversarial perturbations. However, WDRO still suffers fundamentally from the robust overfitting problem, as it does not consider statistical error. We address this gap by proposing a novel robust optimization framework under a new uncertainty set for adversarial noise via Wasserstein distance and statistical error via Kullback-Leibler divergence, called the Statistically Robust WDRO. We establish a robust generalization bound for the new optimization framework, implying that out-of-distribution adversarial performance is at least as good as the statistically robust training loss with high probability. Furthermore, we derive conditions under which Stackelberg and Nash equilibria exist between the learner and the adversary, giving an optimal robust model in certain sense. Finally, through extensive experiments, we demonstrate that our method significantly mitigates robust overfitting and enhances robustness within the framework of WDRO.
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
Authors: Lijia Yu, Yibo Miao, Yifan Zhu, Xiao-Shan Gao, Lijun Zhang
Date: 2025-03-06 00:00:00
Tasks: Attribute, Generalization Bounds

The primary objective of learning methods is generalization. Classic uniform generalization bounds, which rely on VC-dimension or Rademacher complexity, fail to explain the significant attribute that over-parameterized models in deep learning exhibit nice generalizability. On the other hand, algorithm-dependent generalization bounds, like stability bounds, often rely on strict assumptions. To establish generalizability under less stringent assumptions, this paper investigates the generalizability of neural networks that minimize or approximately minimize empirical risk. We establish a lower bound for population accuracy based on the expressiveness of these networks, which indicates that with an adequate large number of training samples and network sizes, these networks, including over-parameterized ones, can generalize effectively. Additionally, we provide a necessary condition for generalization, demonstrating that, for certain data distributions, the quantity of training data required to ensure generalization exceeds the network size needed to represent the corresponding data distribution. Finally, we provide theoretical insights into several phenomena in deep learning, including robust generalization, importance of over-parameterization, and effect of loss function on generalization.
Mechanism of Electricacupuncture Treating Detrusor Bladder Neck Dyscoordination After Suprasacral Spinal Cord Injury by Proteomics
Authors: Liya Tang, Qirui Qu, Yiying Long, Xia Wu, Jincan Liu, Ming Xu, Hong Zhang, Lu Zhou, Qiong Liu, Kun Ai
Date: 2025-03-08 00:00:00
Tasks: TAG

Objectives This study aimed to elucidate the potential mechanisms of electroacupuncture (EA) in restoring detrusor-bladder neck dyssynergesia (DBND) following suprasacral spinal cord injury. Methods A total of 52 adult female Sprague-Dawley rats were randomly assigned to either a sham group (n=12) or a spinal cord injury model group (n=40). In the model group, DBND was induced in 40 rats through Hassan Shaker spinal cord transection, with 24 rats surviving spinal shock and subsequently randomized into two groups: a model-only group (DBND, n=12) and an EA intervention group (DBND+EA, n=12). DBND+EA was administered at Ciliao (BL32), Zhongji (RN3), and Sanyinjiao (SP6) acupoints, for 20 minutes per session, once daily for 10 consecutive days. On day 29 post-injury, all rats underwent urodynamic assessments, followed by hematoxylin and eosin (HE) staining, tandem mass tag (TMT) proteomics, and Western blot (WB) analysis of the detrusor and bladder neck tissues. Results Urodynamic evaluation demonstrated that EA intervention enhanced bladder function in DBND rats. HE staining indicated reduced fibroplasia in the detrusor muscle and alleviated inflammation in the bladder neck following EA. TMT proteomic analysis revealed 30 differentially expressed proteins (DEPs) in the detrusor and 59 DEPs in the bladder neck post-EA treatment. WB results corroborated these TMT findings. Conclusion EA effectively promotes synergy between the detrusor muscle and bladder neck in DBND, likely by enhancing detrusor contractility and facilitating bladder neck relaxation during urination. This study provides mechanistic insights into the therapeutic role of EA in managing DBND.
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
Authors: Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, Karsten Kreis
Date: 2025-03-06 00:00:00
Tasks: Diversity

We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference time, we condition on ellipsoids that are hand-constructed, extracted from existing proteins, or from a statistical model, with each option unlocking new capabilities. Hand-specifying ellipsoids enables users to control the location, size, orientation, secondary structure, and approximate shape of protein substructures. Conditioning on ellipsoids of existing proteins enables redesigning their substructure's connectivity or editing substructure properties. By conditioning on novel and diverse ellipsoid layouts from a simple statistical model, we improve protein generation with expanded Pareto frontiers between designability, novelty, and diversity. Further, this enables sampling designable proteins with a helix-fraction that matches PDB proteins, unlike existing generative models that commonly oversample conceptually simple helix bundles. Code is available at https://github.com/NVlabs/protcomposer.
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery
Authors: Yiheng Zhu, Mingyang Li, Junlong Liu, Kun fu, JianSheng Wu, Qiuyi Li, Mingze Yin, Jieping Ye, Jian Wu, Zheng Wang
Date: 2025-03-06 00:00:00
Tasks: Denoising, Drug Discovery, Mixture-of-Experts, Molecular Property Prediction, Property Prediction

Structure-based drug discovery (SBDD) is a systematic scientific process that develops new drugs by leveraging the detailed physical structure of the target protein. Recent advancements in pre-trained models for biomolecules have demonstrated remarkable success across various biochemical applications, including drug discovery and protein engineering. However, in most approaches, the pre-trained models primarily focus on the characteristics of either small molecules or proteins, without delving into their binding interactions which are essential cross-domain relationships pivotal to SBDD. To fill this gap, we propose a general-purpose foundation model named BIT (an abbreviation for Biomolecular Interaction Transformer), which is capable of encoding a range of biochemical entities, including small molecules, proteins, and protein-ligand complexes, as well as various data formats, encompassing both 2D and 3D structures. Specifically, we introduce Mixture-of-Domain-Experts (MoDE) to handle the biomolecules from diverse biochemical domains and Mixture-of-Structure-Experts (MoSE) to capture positional dependencies in the molecular structures. The proposed mixture-of-experts approach enables BIT to achieve both deep fusion and domain-specific encoding, effectively capturing fine-grained molecular interactions within protein-ligand complexes. Then, we perform cross-domain pre-training on the shared Transformer backbone via several unified self-supervised denoising tasks. Experimental results on various benchmarks demonstrate that BIT achieves exceptional performance in downstream tasks, including binding affinity prediction, structure-based virtual screening, and molecular property prediction.
DeepSilencer: A Novel Deep Learning Model for Predicting siRNA Knockdown Efficiency
Authors: Wangdan Liao, Weidong Wang
Date: 2025-03-06 00:00:00
Tasks: Deep Learning

Background: Small interfering RNA (siRNA) is a promising therapeutic agent due to its ability to silence disease-related genes via RNA interference. While traditional machine learning and early deep learning methods have made progress in predicting siRNA efficacy, there remains significant room for improvement. Advanced deep learning techniques can enhance prediction accuracy, reducing the reliance on extensive wet-lab experiments and accelerating the identification of effective siRNA sequences. This approach also provides deeper insights into the mechanisms of siRNA efficacy, facilitating more targeted and efficient therapeutic strategies. Methods: We introduce DeepSilencer, an innovative deep learning model designed to predict siRNA knockdown efficiency. DeepSilencer utilizes advanced neural network architectures to capture the complex features of siRNA sequences. Our key contributions include a specially designed deep learning model, an innovative online data sampling method, and an improved loss function tailored for siRNA prediction. These enhancements collectively boost the model's prediction accuracy and robustness. Results: Extensive evaluations on multiple test sets demonstrate that DeepSilencer achieves state-of-the-art performance using only siRNA sequences and basic physicochemical properties. Our model surpasses several other methods and shows superior predictive performance, particularly when incorporating thermodynamic parameters. Conclusion: The advancements in data sampling, model design, and loss function significantly enhance the predictive capabilities of DeepSilencer. These improvements underscore its potential to advance RNAi therapeutic design and development, offering a powerful tool for researchers and clinicians.
Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
Authors: Xiangxin Zhou, Yi Xiao, Haowei Lin, Xinheng He, Jiaqi Guan, Yang Wang, Qiang Liu, Feng Zhou, Liang Wang, Jianzhu Ma
Date: 2025-03-06 00:00:00
Tasks: Drug Design, Drug Discovery

The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically relevant conformations, the transition rate is dictated by the intrinsic energy barrier between them, making the sampling process computationally expensive. To overcome the aforementioned challenges, we propose to use generative modeling for SBDD considering conformational changes of protein pockets. We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules. Our method uncovers promising ligand molecules and corresponding holo conformations of pockets. Additionally, the resultant holo-like states provide superior inputs for traditional SBDD approaches, playing a significant role in practical drug discovery.
Bizard: A Community-Driven Platform for Accelerating and Enhancing Biomedical Data Visualization
Authors: Kexin Li, Hong Yang, Ying Shi, Yujie Peng, Yinying Chai, Kexin Huang, Chunyang Wang, Anqi Lin, Jianfeng Li, Jianming Zeng, Peng Luo, Shixiang Wang
Date: 2025-03-10 00:00:00
Tasks: Data Visualization

Bizard is a novel visualization code repository designed to simplify data analysis in biomedical research. It integrates diverse visualization codes, facilitating the selection and customization of optimal visualization methods for specific research needs. The platform offers a user-friendly interface with advanced browsing and filtering mechanisms, comprehensive tutorials, and interactive forums to enhance knowledge exchange and innovation. Bizard's collaborative model encourages continuous refinement and expansion of its functionalities, making it an indispensable tool for advancing biomedical data visualization and analytical methodologies. By leveraging Bizard's resources, researchers can enhance data visualization skills, drive methodological advancements, and improve data interpretation standards, ultimately fostering the development of precision medicine and personalized therapeutic interventions.Bizard can be accessed from http://genaimed.org/Bizard/.
Large Language Models in Bioinformatics: A Survey
Authors: Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen, Xiangyu Shi, Yu Li
Date: 2025-03-06 00:00:00
Tasks: Survey

Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.
Large Language Models for Zero-shot Inference of Causal Structures in Biology
Authors: Izzy Newsham, Luka Kovačević, Richard Moulange, Nan Rosemary Ke, Sach Mukherjee
Date: 2025-03-06 00:00:00
Tasks: Articles, scientific discovery

Genes, proteins and other biological entities influence one another via causal molecular networks. Causal relationships in such networks are mediated by complex and diverse mechanisms, through latent variables, and are often specific to cellular context. It remains challenging to characterise such networks in practice. Here, we present a novel framework to evaluate large language models (LLMs) for zero-shot inference of causal relationships in biology. In particular, we systematically evaluate causal claims obtained from an LLM using real-world interventional data. This is done over one hundred variables and thousands of causal hypotheses. Furthermore, we consider several prompting and retrieval-augmentation strategies, including large, and potentially conflicting, collections of scientific articles. Our results show that with tailored augmentation and prompting, even relatively small LLMs can capture meaningful aspects of causal structure in biological systems. This supports the notion that LLMs could act as orchestration tools in biological discovery, by helping to distil current knowledge in ways amenable to downstream analysis. Our approach to assessing LLMs with respect to experimental data is relevant for a broad range of problems at the intersection of causal learning, LLMs and scientific discovery.
A Phylogenetic Approach to Genomic Language Modeling
Authors: Carlos Albors, Jianan Canal Li, Gonzalo Benegas, Chengzhong Ye, Yun S. Song
Date: 2025-03-04 00:00:00
Tasks: Language Modeling, Language Modelling, Transfer Learning

Genomic language models (gLMs) have shown mostly modest success in identifying evolutionarily constrained elements in mammalian genomes. To address this issue, we introduce a novel framework for training gLMs that explicitly models nucleotide evolution on phylogenetic trees using multispecies whole-genome alignments. Our approach integrates an alignment into the loss function during training but does not require it for making predictions, thereby enhancing the model's applicability. We applied this framework to train PhyloGPN, a model that excels at predicting functionally disruptive variants from a single sequence alone and demonstrates strong transfer learning capabilities.