pwc-archive/papers-with-abstracts huggingface dataset:
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences
Authors: Krithik Ramesh, Sameed M. Siddiqui, Albert Gu, Michael D. Mitzenmacher, Pardis C. Sabeti
Date: 2025-03-20 00:00:00
Tasks: Prediction, Property Prediction, State Space Models

Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent advances driven by scaling up foundation and task-specific models. The computational resources and large datasets required, however, limit their applicability in biological contexts. We introduce Lyra, a subquadratic architecture for sequence modeling, grounded in the biological framework of epistasis for understanding sequence-to-function relationships. Mathematically, we demonstrate that state space models efficiently capture global epistatic interactions and combine them with projected gated convolutions for modeling local relationships. We demonstrate that Lyra is performant across over 100 wide-ranging biological tasks, achieving state-of-the-art (SOTA) performance in many key areas, including protein fitness landscape prediction, biophysical property prediction (e.g. disordered protein region functions) peptide engineering applications (e.g. antibody binding, cell-penetrating peptide prediction), RNA structure analysis, RNA function prediction, and CRISPR guide design. It achieves this with orders-of-magnitude improvements in inference speed and reduction in parameters (up to 120,000-fold in our tests) compared to recent biology foundation models. Using Lyra, we were able to train and run every task in this study on two or fewer GPUs in under two hours, democratizing access to biological sequence modeling at SOTA performance, with potential applications to many fields.
Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy
Authors: Qiang Li, Ting Xu, Vince D. Calhoun
Date: 2025-03-19 00:00:00

The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets. Quantifying functional correspondences in the visual cortex across species can help us understand how information is processed in the primate visual cortex, while also providing deeper insights into human visual cortex functions through the study of marmosets. In this study, we measured pairwise and beyond pairwise correlation, redundancy, and synergy in movie-driven fMRI data across species. Our first key finding was that humans and marmosets exhibited significant overlaps in functional synergy. Second, we observed that the strongest functional correspondences between the human peri-entorhinal and entorhinal cortex (PeEc) and the occipitotemporal higher-level visual regions in the marmoset during movie watching reflected a functional synergistic relationship. These regions are known to correspond to face-selective areas in both species. Third, redundancy measures maintained stable high-order hubs, indicating a steady core of shared information processing, while synergy measures revealed a dynamic shift from low- to high-level visual regions as interaction increased, reflecting adaptive integration. This highlights distinct patterns of information processing across the visual hierarchy. Ultimately, our results reveal the marmoset as a compelling model for investigating visual perception, distinguished by its remarkable functional parallels to the human visual cortex.
Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation
Authors: Aung Htet, Alejandro Rodriguez Jimenez, Sarah Hamburg, Alessandro Di Nuovo
Date: 2025-03-20 00:00:00
Tasks: Specificity

We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations. Allostasis is a fundamental regulatory mechanism observed in animal physiology that orchestrates responses to maintain a dynamic equilibrium in bodily needs and internal states. In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation. While existing neural networks can maintain persistent states, to date, there is no unified framework for dynamically controlling spatial changes in neuronal activity in response to environmental changes. To address this, we couple a well known allostatic microcircuit, the Hammel model, with a ring attractor, resulting in a Spiking Neural Network architecture that can modulate the location of the bump as a function of some reference input. This localized activity in turn is used as a perceptual belief in a simulated subitization task a quick enumeration process without counting. We provide a general procedure to fine-tune the model and demonstrate the successful control of the bump location. We also study the response time in the model with respect to changes in parameters and compare it with biological data. Finally, we analyze the dynamics of the network to understand the selectivity and specificity of different neurons to distinct categories present in the input. The results of this paper, particularly the mechanism for moving persistent states, are not limited to numerical cognition but can be applied to a wide range of tasks involving similar representations.
Representational Similarity via Interpretable Visual Concepts
Authors: Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
Date: 2025-03-19 00:00:00

How do two deep neural networks differ in how they arrive at a decision? Measuring the similarity of deep networks has been a long-standing open question. Most existing methods provide a single number to measure the similarity of two networks at a given layer, but give no insight into what makes them similar or dissimilar. We introduce an interpretable representational similarity method (RSVC) to compare two networks. We use RSVC to discover shared and unique visual concepts between two models. We show that some aspects of model differences can be attributed to unique concepts discovered by one model that are not well represented in the other. Finally, we conduct extensive evaluation across different vision model architectures and training protocols to demonstrate its effectiveness.
A Foundational Theory for Decentralized Sensory Learning
Authors: Linus Mårtensson, Jonas M. D. Enander, Udaya B. Rongala, Henrik Jörntell
Date: 2025-03-19 00:00:00

In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to allow the brain to be interpreted as a negative feedback control system, and show how this could lead to local learning algorithms without the need for global error correction metrics. Thereby, a sufficiently good minima in sensory activity can be the complete reward signal of the network, as well as being both necessary and sufficient for biological learning to arise. We show that this method of learning was likely already present in the earliest unicellular life forms on earth. We show evidence that the same principle holds and scales to multicellular organisms where it in addition can lead to division of labour between cells. Available evidence shows that the evolution of the nervous system likely was an adaptation to more effectively communicate intercellular signals to support such division of labour. We therefore propose that the same learning principle that evolved already in the earliest unicellular life forms, i.e. negative feedback control of externally and internally generated sensor signals, has simply been scaled up to become a fundament of the learning we see in biological brains today. We illustrate diverse biological settings, from the earliest unicellular organisms to humans, where this operational principle appears to be a plausible interpretation of the meaning of sensor signals in biology, and how this relates to current neuroscientific theories and findings.
From Density to Void: Why Brain Networks Fail to Reveal Complex Higher-Order Structures
Authors: Moo K. Chung, Anass B. El-Yaagoubi, Anqi Qiu, Hernando Ombao
Date: 2025-03-18 00:00:00

In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Despite the promise of these advanced topological tools, robust and consistently observed higher-order interactions over time remain elusive. In this study, we investigate why conventional analyses often fail to reveal complex higher-order structures - such as interactions involving four or more nodes - and explore whether such interactions truly exist in functional brain networks. We utilize a simplicial complex framework often used in persistent homology to address this question.
There must be encapsulated nonconceptual content in vision
Authors: Vincent C. Müller
Date: 2025-03-06 00:00:00

In this paper I want to propose an argument to support Jerry Fodor's thesis (Fodor 1983) that input systems are modular and thus informationally encapsulated. The argument starts with the suggestion that there is a "grounding problem" in perception, i. e. that there is a problem in explaining how perception that can yield a visual experience is possible, how sensation can become meaningful perception of something for the subject. Given that visual experience is actually possible, this invites a transcendental argument that explains the conditions of its possibility. I propose that one of these conditions is the existence of a visual module in Fodor's sense that allows the step from sensation to object-identifying perception, thus enabling visual experience. It seems to follow that there is informationally encapsulated nonconceptual content in visual perception.
Contribution to the study of the flora in the central-west of Tunisia landscape dynamics and evaluation of plant biodiversity of mountain Bouchebka
Authors: Saadaoui Islem, Ilahi Hayet, Robin Bryant Christopher, Rejeb Hichem
Date: 2025-03-19 00:00:00

The study was conducted during 2013 in Bouchebka, located in the central west of Tunisia.Such territory has a typical landscape of the transfrontier region. The series of the forest in Bouchebka is a part of the great mass of Aleppo pine. It is distinguished by the importance of the forest area which covers 92 % of the surface area (19,700 ha). The study attempts to inventory the natural vegetation and characterize ecological terms while highlighting the importance of environmental conditions. The method is based on a phytoecological analysis to quantify the floristic richness and diversity of the ecosystem in the forest of mountains in Bouchebka on the basis of floristic surveys and transects distributed in a stratified, systematic sampling in different vegetation formations that were previously distinguished. Statistical analyzes were performed using the Factorial Correspondence Analysis (FCA). The results show that the forest is composed primarily of the Aleppo pine trees, these forests are characterized by the abundance of young feet (10-25 cm diameter class). The ecosystem includes 12 families and 17 genera, 26 species. Thus the study has identified that the biological spectrum of the study area is characterized by a clear dominance of shrubs (41%) and chamaephytes (32 %). The distribution of plant species is influenced by ecological features of the region: the results show that 82% of species are drought tolerant which shows the arid environment. The region is also characterized by its windy side: 32% of species spread via anemochory. Factor analysis showed a pastoral aspect in the study area, with a presence of cultured human action exerted on the forest land species. Phytological spectrum indicates a predominance of woody species reflecting a territory dominated by open grassy areas, predominantly reflecting an arid climate.
Targeting Neurodegeneration: Three Machine Learning Methods for G9a Inhibitors Discovery Using PubChem and Scikit-learn
Authors: Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic
Date: 2025-03-20 00:00:00

In light of the increasing interest in G9a's role in neuroscience, three machine learning (ML) models, that are time efficient and cost effective, were developed to support researchers in this area. The models are based on data provided by PubChem and performed by algorithms interpreted by the scikit-learn Python-based ML library. The first ML model aimed to predict the efficacy magnitude of active G9a inhibitors. The ML models were trained with 3,112 and tested with 778 samples. The Gradient Boosting Regressor perform the best, achieving 17.81% means relative error (MRE), 21.48% mean absolute error (MAE), 27.39% root mean squared error (RMSE) and 0.02 coefficient of determination (R2) error. The goal of the second ML model called a CID_SID ML model, utilised PubChem identifiers to predict the G9a inhibition probability of a small biomolecule that has been primarily designed for different purposes. The ML models were trained with 58,552 samples and tested with 14,000. The most suitable classifier for this case study was the Extreme Gradient Boosting Classifier, which obtained 78.1% accuracy, 84.3% precision,69.1% recall, 75.9% F1-score and 8.1% Receiver-operating characteristic (ROC). The third ML model based on the Random Forest Classifier algorithm led to the generation of a list of descending-ordered functional groups based on their importance to the G9a inhibition. The model was trained with 19,455 samples and tested with 14,100. The probability of this rank was 70% accuracy.
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
Authors: Yang Tan, Chen Liu, Jingyuan Gao, Banghao Wu, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
Date: 2025-03-19 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Retrieval

Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task benchmarking, and application. This work presents VenusFactory, a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. VenusFactory supports both computer science and biology communities with choices of both a command-line execution and a Gradio-based no-code interface, integrating $40+$ protein-related datasets and $40+$ popular PLMs. All implementations are open-sourced on https://github.com/tyang816/VenusFactory.
The three-dimensional impulse-response model: Modeling the training process in accordance with energy system-specific adaptation
Authors: Hilkka Kontro, Armando Mastracci, Stephen S. Cheung, Martin J. MacInnis
Date: 2025-03-19 00:00:00

Athletic training is characterized by physiological systems responding to repeated exercise-induced stress, resulting in gradual alterations in the functional properties of these systems. The adaptive response leading to improved performance follows a remarkably predictable pattern that may be described by a systems model provided that training load can be accurately quantified and that the constants defining the training-performance relationship are known. While various impulse-response models have been proposed, they are inherently limited in reducing training stress (the impulse) into a single metric, assuming that the adaptive responses are independent of the type of training performed. This is despite ample evidence of markedly diverse acute and chronic responses to exercise of different intensities and durations. Herein, we propose an alternative, three-dimensional impulse-response model that uses three training load metrics as inputs and three performance metrics as outputs. These metrics, represented by a three-parameter critical power model, reflect the stress imposed on each of the three energy systems: the alactic (phosphocreatine/immediate) system; the lactic (glycolytic) system; and the aerobic (oxidative) system. The purpose of this article is to outline the scientific rationale and the practical implementation of the three-dimensional impulse-response model.
Sequence Analysis Using the Bezier Curve
Authors: Taslim Murad, Sarwan Ali, Murray Patterson
Date: 2025-03-18 00:00:00
Tasks: Drug Discovery

The analysis of sequences (e.g., protein, DNA, and SMILES string) is essential for disease diagnosis, biomaterial engineering, genetic engineering, and drug discovery domains. Conventional analytical methods focus on transforming sequences into numerical representations for applying machine learning/deep learning-based sequence characterization. However, their efficacy is constrained by the intrinsic nature of deep learning (DL) models, which tend to exhibit suboptimal performance when applied to tabular data. An alternative group of methodologies endeavors to convert biological sequences into image forms by applying the concept of Chaos Game Representation (CGR). However, a noteworthy drawback of these methods lies in their tendency to map individual elements of the sequence onto a relatively small subset of designated pixels within the generated image. The resulting sparse image representation may not adequately encapsulate the comprehensive sequence information, potentially resulting in suboptimal predictions. In this study, we introduce a novel approach to transform sequences into images using the B\'ezier curve concept for element mapping. Mapping the elements onto a curve enhances the sequence information representation in the respective images, hence yielding better DL-based classification performance. We employed different sequence datasets to validate our system by using different classification tasks, and the results illustrate that our B\'ezier curve method is able to achieve good performance for all the tasks.
Efficient Data Selection for Training Genomic Perturbation Models
Authors: George Panagopoulos, Johannes F. Lutzeyer, Sofiane Ennadir, Michalis Vazirgiannis, Jun Pang
Date: 2025-03-18 00:00:00
Tasks: Active Learning, Graph Neural Network

Genomic studies, including CRISPR-based PerturbSeq analyses, face a vast hypothesis space, while gene perturbations remain costly and time-consuming. Gene expression models based on graph neural networks are trained to predict the outcomes of gene perturbations to facilitate such experiments. Active learning methods are often employed to train these models due to the cost of the genomic experiments required to build the training set. However, poor model initialization in active learning can result in suboptimal early selections, wasting time and valuable resources. While typical active learning mitigates this issue over many iterations, the limited number of experimental cycles in genomic studies exacerbates the risk. To this end, we propose graph-based one-shot data selection methods for training gene expression models. Unlike active learning, one-shot data selection predefines the gene perturbations before training, hence removing the initialization bias. The data selection is motivated by theoretical studies of graph neural network generalization. The criteria are defined over the input graph and are optimized with submodular maximization. We compare them empirically to baselines and active learning methods that are state-of-the-art on this problem. The results demonstrate that graph-based one-shot data selection achieves comparable accuracy while alleviating the aforementioned risks.
Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
Authors: Andrea Zerio, Maya Bechler-Speicher, Tine Jess, Aleksejs Sazonovs
Date: 2025-03-18 00:00:00
Tasks: Imputation

Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular sampling and informative observation. Traditional approaches rely on imputation, which can distort learning signals and bias predictions while lacking biological interpretability. We propose a novel methodology using Graph Neural Additive Networks (GNAN) to model biomarker trajectories as time-weighted directed graphs, where nodes represent sampling events and edges encode the time delta between events. GNAN's additive structure enables the explicit decomposition of feature and temporal contributions, allowing the detection of critical disease-associated time points. Unlike conventional imputation-based approaches, our method preserves the temporal structure of sparse data without introducing artificial biases and provides inherently interpretable predictions by decomposing contributions from each biomarker and time interval. This makes our model clinically applicable, as well as allowing it to discover biologically meaningful disease signatures.
Longitudinal Impact of Tobacco Use and Social Determinants on Respiratory Health Disparities Among Louisiana Medicaid Enrollees
Authors: Yead Rahman, Prerna Dua
Date: 2025-03-15 00:00:00
Tasks: Descriptive

Tobacco use remains a leading preventable contributor to serious health conditions in the United States, notably chronic obstructive pulmonary disease (COPD) and severe COVID-19 complications. Within Louisiana's Medicaid population, tobacco use prevalence is particularly high compared to privately insured groups, yet its full impact on long-term outcomes is not fully understood. This study aimed to investigate how tobacco use, in conjunction with demographic and clinical risk factors, influences the incidence of COPD and COVID-19 among Medicaid enrollees over time. We analyzed Louisiana Department of Health data from January 2020 to February 2023. Chi-square tests were conducted to provide descriptive statistics, and multivariate logistic regression models were applied across three discrete waves to assess both cross-sectional and longitudinal associations between risk factors and disease outcomes. Enrollees without baseline diagnoses of COPD or COVID-19 were followed to determine new-onset cases in subsequent waves. Adjusted odds ratios (AOR) were calculated after controlling for socio-demographic variables, comorbidities, and healthcare utilization patterns. Tobacco use emerged as a significant independent predictor of both COPD (Adjusted Odd Ratio= 1.12) and COVID-19 (Adjusted Odd Ratio = 1.66). Additional risk factors -- such as older age, gender, region, and pre-existing health conditions -- also showed significant associations with higher incidence rates of COPD and COVID-19. By linking tobacco use, demographic disparities, and comorbidities to an increased risk of COPD and COVID-19, this study underscores the urgent need for targeted tobacco cessation efforts and prevention strategies within this underserved population.