Genomics · Cross-listed from cs.LG
Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening
Ziyuan Gao
High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A difficult case arises when both genes in a test pair were seen individually during training, but their combination was not. In this setting, standard OOD methods often fail because neither gene appears unfamiliar on its own. We therefore compare each model prediction with a simple additive prediction obtained by summing the two observed single-gene effects. The difference between them, which we call additive disagreement (AD), measures how much extra interaction effect the model introduces for an unseen combination. Across eight prediction methods and three Perturb-seq
datasets, specialised neural predictors do not consistently outperform this simple additive baseline. More importantly, predictions with larger deviations from the additive baseline tend to have larger errors. Conventional output-based and
embedding-based confidence signals are inconsistent across
datasets and models, whereas AD remains informative across all evaluated compositional settings. We further study how this signal can guide decisions in an agentic perturbation-screening workflow. The code is available at https://github.com/zygao930/OOD.
Read the full abstract ▾
Quantitative Methods · Cross-listed from cs.LG
Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders
Ziyuan Gao
Protein function annotation operates over an expanding label space, making class-incremental learning (CIL) a natural framework for incorporating newly discovered functions without full retraining. Yet existing CIL methods have been developed largely in
vision and have not been systematically evaluated over frozen
protein representations. We conduct a controlled
benchmark of nine representative CIL methods across five sequence-, structure-, and sequence–structure-based
protein encoders and two enzyme function
datasets at different scales, while keeping the encoders frozen and evaluation settings consistent across representations. We find that no method transfers reliably across encoders, and that these failures persist across alternative phase constructions. Diagnostic analyses reveal that this instability arises from weak gradient-based stability signals, substantial heterogeneity in
embedding geometry, and covariance assumptions whose appropriate capacity changes with both
dataset scale and encoder. These findings suggest that the representation itself should be treated as a central component of continual-learning design rather than a passive feature extractor. More generally, reliable
protein CIL will require encoder-aware methods that adapt their statistical assumptions and capacity to the geometry of the representation on which they operate. Code is available at https://github.com/zygao930/ProteinCIL.
Read the full abstract ▾
Tissues and Organs · Cross-listed from eess.IV
Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
Vasisht Ishwar, Hari Trivedi, Young Seok Jeon et al. (+5 more)
Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained from scratch for breast implant classification in 2D mammography, with emphasis on suitability for near real-time clinical deployment. Methods: We evaluated four models: two FMs (RAD-DINO and MammoCLIP) and two CNNs trained from scratch for implant prediction (ResNet18 and our lightweight ResNetLite). Using the Emory Breast Imaging
Dataset, 5,000 unilateral screening mammograms were used for training/validation and 1,000 manually reviewed unilateral images were held out for testing. For the FMs, global image
embeddings from the pretrained encoder were classified using a support vector machine (SVM). The CNNs were trained end-to-end on 2D mammograms, with ResNetLite optimized via grid search over depth and width to balance accuracy and efficiency. Performance was evaluated using AUROC, sensitivity, specificity, accuracy,
embedding visualization, and inference-latency. Results: All models demonstrated strong performance on held-out test data (n = 1,000). MammoCLIP achieved the highest AUROC (0.999) with the quickest training time of 493 seconds. RAD-DINO achieved the highest sensitivity (0.980; accuracy 0.989) but had the slowest inference and training times. ResNet18 and MammoCLIP achieved comparable accuracy (0.985). ResNetLite showed no statistically significant difference from ResNet18 (AUROC 0.993; accuracy 0.976) despite using only 1.4% of ResNet18's parameters, and had the fastest inference time. Conclusion: FMs and task-specific CNN models reliably detect breast implants on 2D mammography. Model selection is best guided by deployment context: MammoCLIP for GPU-equipped hospital settings requiring scalable integration, and lightweight CNNs such as ResNetLite for resource-constrained or edge deployments.
Read the full abstract ▾
All authors Vasisht Ishwar, Hari Trivedi, Young Seok Jeon, Beatrice Brown-Mulry, Frank Li, Rohan Satya Isaac, Mohammadreza Chavoshi, Judy Wawira Gichoya
Genomics
GCTAg: scalable mixed-model analysis for biobank-scale agricultural cohorts
Alexander S. Leonard, Qiongyu He, Natasha Watson et al. (+2 more)
Genome-wide association studies identify genomic variants associated with traits. Mixed linear model association (MLMA) methods using a whole-genome relationship
matrix, such as those implemented in GCTA, are powerful but computationally expensive. Here, we remove key
memory and CPU bottlenecks in GCTA, reducing REML
memory usage by nearly 75% and substantially accelerating MLMA by orders of magnitude in biobank-scale cohorts while preserving exactness. We further exploit relatedness in the mapping cohort through a reduced-rank Woodbury
matrix approach, delivering further orders of magnitude performance gains with controlled genomic inflation. Native on-the-fly dominance recoding also eliminates slow I/O-operations on intermediate files, enabling efficient additive and dominance MLMA analyses in large agricultural cohorts.
Read the full abstract ▾
All authors Alexander S. Leonard, Qiongyu He, Natasha Watson, Naveen Kumar Kadri, Hubert Pausch
Populations and Evolution · Cross-listed from cond-mat.soft
Traveling waves, phase separation and pattern formation in the active stepping stone model
Rashmiranjan Bhutia, Prasad Perlekar and Kabir Ramola
Selective advantage leads to the proliferation of a species into an ecosystem. On the other hand, self-propulsion can lead to dynamic clustering of individuals. To analyze the intricate effects produced by such competing processes, we introduce a minimal model for a proliferating active population, where run-and-tumble migration is combined with a
stochastic birth-death process. Beginning with the microscopic dynamics of the particles, we derive the corresponding hydrodynamic equations and validate them through numerical simulations. We demonstrate that the inclusion of activity leads to the emergence of novel morphological patterns, which are sensitive to the specific rules governing migration. Furthermore, we show that activity significantly alters the characteristics of invasion waves, increasing their propagation speed. However, beyond a threshold, excessive activity disrupts the wave-like nature of the invasion process. We also extend our study to two dimensions and analyze the instabilities that lead to pattern formation in such systems.
Read the full abstract ▾
Molecular Networks · Cross-listed from cs.LG
Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
Jianru Shen
Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction structure carries that information. Our candidate signal is effective resistance, used previously to relieve over-squashing by rewiring. Across 24 tissue-specific interactomes it is dominated by inverse degree, and the degeneration deepens as the co-expression filtered network grows, with a Spearman correlation of -0.955. The residual departure from that limit exceeds degree-preserving null graphs in all 24 networks. Controlling for predictive
entropy, degree, annotation cardinality, local structure and feature-only difficulty, the residual explains additional per-node loss in 19 of 24 held-out networks once a permutation floor is subtracted, at every depth, and the effect strengthens monotonically with depth. The increment reaches 0.37% of the variance the controls leave unexplained, 5.6 times a permutation floor, against 1.5 times when the model is retrained in a degree-preserving null world. Selective prediction improves negligibly. The signal is reproducible; degree degeneration bounds it.
Read the full abstract ▾
Neurons and Cognition · Cross-listed from cs.LG
A foundation for systematic analysis of transformers and RNNs for tractography
Emmanuelle Renauld, Philippe Poulin, Hugo Larochelle et al. (+2 more)
Machine learning (ML) has emerged as a promising approach for improving
diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic tension between local
diffusion information and global anatomical plausibility. In this work, we systematically evaluate recurrent neural networks (RNNs) and
Transformer models for iterative tractography, with particular
attention to training strategies, input representations (including convolutional neural network (CNN)-based
embeddings and end-of-sequence (EOS)
tokens), and hyperparameter selection. We introduce a generation-validation phase enabling supervision at the streamline level during training, allowing supervision despite the mismatch between local loss functions and global streamline quality. Using the ISMRM2015 tractography challenge
dataset, our models achieve the highest reported performance to date. Through controlled experiments, we quantify the impact of missing bundles, noisy or imperfect training streamlines, and invalid fibers in the training set. Finally, we demonstrate the applicability of our best-performing models for in vivo data from the Tractoinferno database. Overall, our results highlight both the potential and the limits of sequence-based deep learning models such as
Transformers and RNNs for tractography, and emphasize the need for improved phantoms and evaluation methods for in vivo validation. We provide takeaways and recommendations for future researchers training and validating sequence-based supervised methods for tractography.
Read the full abstract ▾
All authors Emmanuelle Renauld, Philippe Poulin, Hugo Larochelle, Antoine Théberge, Maxime Descoteaux
Biomolecules · Cross-listed from cs.CE
RipplePLM: Structural and Property Decoupling for Protein Mutation Effect Generation
Liuzhenghao Lv, Yuyang Liu, Yuyang Gao et al. (+2 more)
Protein mutation effect generation asks a model to describe the functional consequence of a point mutation in natural
language. Existing
protein-to-text systems typically encode mutation information into undifferentiated representations, overlooking the organization of mutation-induced evidence across structural and biochemical factors. We propose RipplePLM, a mutation-aware generation framework centered on Direct-Distal Cross-
Attention (DDCA). By constructing a residue-level Mutation Perturbation Field from pre-trained
protein language models, DDCA leverages predicted contact maps to organize mutation representations into two pathways: the mutation site's immediate contact neighborhood and its multi-hop distal context. To complement this structural decomposition, we further introduce the Property Latent Chain (PLChain), which injects expert-guided supervision of biochemical property changes (e.g., thermostability and optimal pH) into the LLM hidden-state pathway through latent property
tokens. On MutaDescribe, RipplePLM improves over mutation-specific baselines on temporal and structural splits; under a matched-backbone comparison, average structural-split ROUGE-L increases from 22.23 to 35.65. Expert evaluation further shows a higher proportion of biologically accurate or relevant descriptions than the mutation-specific baseline. Additional ablations, representation diagnostics, and low-$N$ fitness regression experiments further support the effectiveness of the learned mutation-aware representations. Code: https://github.com/Lyu6PosHao/RipplePLM.
Read the full abstract ▾
All authors Liuzhenghao Lv, Yuyang Liu, Yuyang Gao, Li Yuan, Yonghong Tian
Neurons and Cognition · Cross-listed from cs.LG
Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems
Lulu Gong, Yongxu Zhang and Shreya Saxena
Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural recordings is important for better understanding neural computation and function. However, traditional approaches based on autocorrelation fitting are difficult to scale to high-dimensional population recordings and can become unreliable when neural dynamics change with behavior. State-space models have been a powerful framework for modeling high-dimensional neural population activity through latent dynamical systems, but standard formulations and inference methods do not explicitly account for multiple timescales and therefore do not guarantee accurate recovery of the underlying temporal structure. Motivated by these questions, we introduce the Multi-Timescale Switching Linear Dynamical System (MTS-SLDS), a framework for identifying regime-specific latent timescales from continuous or spiking neural observations. MTS-SLDS combines a multi-lag moment initialization, which captures temporal structure across multiple observation lags, with regime-conditioned Laplace-EM inference, which reduces mixing of dynamical statistics across uncertain regimes. Characteristic timescales can then be extracted directly from the
eigenvalues of the learned latent transition
matrices. In synthetic and neural experiments with Gaussian and Poisson spike observations, MTS-SLDS accurately recovers timescales and switching structure over multiple
datasets.
Read the full abstract ▾
Biomolecules · Cross-listed from cs.LG
pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows
Tong Chen, Maximilian Holsman, Lin Zhao et al. (+1 more)
Biomolecular therapeutics often start from known sequences and require targeted editing to improve multiple properties while satisfying hard biochemical and manufacturability constraints. However, existing generative methods do not jointly support multi-objective optimization, hard feasibility, and sequence editing in discrete, variable-length biological spaces. In this work, we introduce Pareto-Constrained Molecule Editing (pCoMole), a framework built on discrete flow matching that steers a pre-trained Edit Flow toward user-specified preferences while enforcing terminal feasibility. pCoMole defines a feasibility-gated terminal distribution using an augmented Tchebycheff utility and realizes the resulting preference tilt through a Doob-h transform of the underlying edit process. To make this construction practical, we approximate the required harmonic function using short Monte Carlo rollouts over candidate edits, yielding an efficient guided editor with provable preference consistency. We validate pCoMole by shrinking GFP while retaining fluorescence-related properties, shortening diverse Cas9 orthologs while preserving PAM specificity, and compressing peptide binders into short peptidomimetics that optimize seven drug-related properties under hard constraints. In wet lab testing, two 229-residue pCoMole-designed eGFP variants retained clear green fluorescence in BL21
cells after 10 deletions, with either one or two substitutions. Together, pCoMole enables constraint-aware, Pareto-aligned editing of biomolecular sequences in discrete, variable-length spaces.
Read the full abstract ▾
All authors Tong Chen, Maximilian Holsman, Lin Zhao, Pranam Chatterjee
Molecular Networks · Cross-listed from math.DS
Autocatalysis and Boundary Stability in Chemical Reaction Systems
Matthew D. Johnston and Pol Jarne Cupons
The next-generation
matrix method is a powerful tool for computing the basic reproduction number in compartmental mathematical models of infectious diseases. The method has been recently extended to mathematical biochemistry, where it can be used to establish parameter regions of stability and instability for boundary steady states. Several significant challenges in its application remain, however, particularly around establishing conditions under which the method is mathematically valid, computationally tractable, and biologically meaningful in the biochemical setting. In this paper, we address these challenges by shifting the interpretation from new infections in the epidemiological setting to autocatalysis in the biochemical setting. We introduce a graph, called the ALT-graph (autocatalysis-leak-transition graph), which decomposes the contribution of each reaction to the Jacobian as autocatalytic, leak, or transition edges. We then present a systematic
algorithm for splitting the ALT-graph, which is guaranteed to produce a valid reproduction number, $\rho(FV^{-1})$, while also decreasing computational complexity by lowering the rank of $FV^{-1}$. We apply the method to models of both biochemical reaction networks and infectious disease spread.
Read the full abstract ▾
Quantitative Methods · Cross-listed from cs.CL
Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
Laura van Weesep, Riccardo Tedoldi, Jens Sjölund et al. (+6 more)
The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repositories and industrial screening databases suffer from missing, inconsistent, or conflated assay annotations. In this work, we quantify the extent of missing annotations in PubChem for the BioAssay Ontology (BAO) assay format and physical detection method fields and investigate whether open-source and proprietary large
language models (LLMs) can reliably predict and audit metadata annotations directly from the assay text. In our assessment, we found that the annotation coverage across PubChem's $\sim$2 million bioassays is critically sparse, 36% lacking an assay format, 89% a BioAssay type, and >99.9% any BAO-mapped assay format or detection technology term. This motivates the need for automated test-metadata curation. Using evaluation sets derived from PubChem and ChEMBL, we assess the agreement of seven open-source and proprietary LLMs with existing silver labels. Recall is at least 0.96 for biochemical and
cell-based assay formats, with a similar pattern for detection technology, although disagreements increase on under-represented classes. Manual inspection shows that many of these disagreements trace back to inconsistencies between silver sources rather than to LLM error. Moreover, in a qualitative study with a senior industrial curator, LLM-generated evidence prompted the expert to revise some of their own labels, showing LLMs can flag potentially mislabeled assays. Across the study, performance differences between proprietary and open-source models were small. Together, these results suggest LLMs can support the large-scale annotation and auditing of assay metadata, though per-class reliability estimates and targeted human review remain necessary before such labels enter downstream ML pipelines.
Read the full abstract ▾
All authors Laura van Weesep, Riccardo Tedoldi, Jens Sjölund, Hossein Azizpour, Susanne Winiwarter, Ola Engkvist, Jon Paul Janet, Samuel Genheden, Juan Viguera Diez
Biomolecules
Fold'EM: Direct atomic structure inference from Cryo-EM particles
Advaith Maddipatla, Märt-Erik Mäeots, Marco Pegoraro et al. (+5 more)
Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination. The conventional cryo-EM computational pipeline first combines many particle images to reconstruct an electrostatic potential (ESP) map and then fits an atomic model to the recovered map. Density reconstruction has high sample complexity, requiring large numbers of particle images and making structure determination high-cost and low-throughput, particularly for heterogeneous samples. Downstream atomic model building, in turn, becomes increasingly difficult as the resolution of the reconstructed map deteriorates.
Protein structure prediction models provide strong sequence-derived priors on atomic structure, and experiment-guided approaches can use these priors to recover structures consistent with experimental measurements. Yet, in cryo-EM, such priors are typically integrated only after density reconstruction during atomic model fitting. We introduce Fold'EM, an inference-time framework that combines priors from
protein generative models directly with cryo-EM particle images to determine atomic models from a small number of single particle images, bypassing both intermediate density reconstruction and downstream model building against the reconstructed map. Across synthetic and experimental cryo-EM
datasets, Fold'EM recovers accurate atomic structures both with known particle orientations and in an ab-initio setting where orientations are inferred jointly with structure. In heterogeneous
datasets, Fold'EM further resolves distinct conformational states from mixed particle populations without separately reconstructing a density map and building an atomic model for each state. We believe these results open new avenues for structure determination in the low-sample regime and for characterizing low-population conformational states directly from cryo-EM particles.
Read the full abstract ▾
All authors Advaith Maddipatla, Märt-Erik Mäeots, Marco Pegoraro, Nikolaus Dräger, Roberto Covino, Sanketh Vedula, Martin Pacesa, Alex M. Bronstein
Neurons and Cognition
A High-Density EEG Dataset for Stimulus-Driven Auditory Attention
Ruofan Yan, Na Lu, Shu Peng et al. (+6 more)
Stimulus-driven auditory
attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the Stimulus-driven Auditory
Attention (SAAD) paradigm and develops a neurophysiologically informed framework that integrates stimulus-derived sound priority with trial-specific
EEG evidence. Behavioral analysis using a Bradley–Terry model showed that sound priority estimated from previous competitions generalized to unseen sound pairings, improving held-out prediction from an AUC of 0.577 to 0.718.
EEG analysis further revealed mid-to-late centro-temporal lateralization associated with the reported selection side, with neural information remaining predictive beyond acoustic asymmetry. Guided by these findings, the proposed model first estimates a latent priority for each competing sound and forms relative stimulus evidence from their difference. A multi-scale
EEG pathway with complementary signed and power-based readouts then extracts trial-specific neural evidence, which is incorporated through gated decision-level integration. The framework is evaluated using mirror-constrained and pairing-held-out protocols, together with representative acoustic,
EEG,
multimodal baselines, and systematic ablations. The results support a computational account in which spontaneous auditory selection reflects the interaction between generalizable stimulus priority and trial-specific neural variability.
Read the full abstract ▾
All authors Ruofan Yan, Na Lu, Shu Peng, Wenlong You, Zhige Chen, Yuxuan Yan, Yan Liu, Kay Chen Tan, Jibin Wu
Molecular Networks
Protocol for Discovering Convergent Molecular Networks across Multi-Omics Datasets Using Network Propagation
Charles O. Olwal, Declan M. Winters, Immy A. Ashley et al. (+9 more)
Phenotypic outcomes arise from coordinated changes across molecular layers. Although multi-omics data capture complementary molecular signals, integrating them into coherent biological mechanisms remains challenging due to limited overlap at the gene level. Network propagation maps significant gene hits from each
dataset onto a prior knowledge molecular interaction network and diffuses their signals to neighboring genes. Mathematical integration of propagated networks, followed by clustering and pathway enrichment, reveals shared molecular networks, pinpointing the core biology captured across orthogonal molecular layers.
Read the full abstract ▾
All authors Charles O. Olwal, Declan M. Winters, Immy A. Ashley, Valerie Vandersall, Amberin Rahman, Alexandra Lindsey Djomkam Zune, Kyla F. Dangerfield, Yennifer Delgado, Sara K. Makanani, Sophie F. Blanc, Shipra Sharma, Mehdi Bouhaddou
Populations and Evolution · Cross-listed from math.DS
Compartmental epidemiological models with infection-driven immune escape
Daniela R. Ramírez-Gutiérrez, Lajos Lóczi, Carlos Muñoz-Moncayo et al. (+1 more)
We propose and study a compartmental epidemiological model that incorporates both waning immunity and mutation-driven immune escape into a single-strain framework. The model, termed SIRCm, extends the SIRC (Susceptible-Infected-Recovered-Cross-immune) model, in which recovered individuals pass through an intermediate cross-immune class before returning to full susceptibility. In SIRCm, both the rate of immune waning and the rate of immune escape are amplified by a feedback function tied to the current level of infection. We study two formulations of this feedback, a transmission-driven model, in which mutation opportunities arise at the point of new infection, and a prevalence-driven model, in which they arise in proportion to the infected population alone. For both models and any feedback strength, we prove existence of an endemic
equilibrium using persistence theory, and characterize its behavior analytically in the weak and strong-feedback limits, recovering the SIRC and SIS models, respectively, as limiting cases. Unlike the SIRC model, we show that the SIRCm endemic
equilibrium is unstable in certain parameter regimes. This leads to novel behavior, even in the absence of seasonal forcing.
Read the full abstract ▾
All authors Daniela R. Ramírez-Gutiérrez, Lajos Lóczi, Carlos Muñoz-Moncayo, David I. Ketcheson
Biomolecules
Multi-Scale Temporal Flows for Peptide Trajectory Generation
Xichen Sun, Wentao Wei, Xiaoxi Zhang et al. (+2 more)
Peptides occupy a particularly challenging regime of biomolecular dynamics. Short peptides lack a stable folded core and populate broad conformational ensembles, while cyclization adds ring closure, non-local residue coupling, and stereochemical diversity. Deep generative models have made remarkable progress in emulating molecular-dynamics trajectories directly, yet each is trained on windows cut at a fixed interval and therefore observes the process at a fixed temporal resolution. More fundamentally, none takes that interval as an input, so the physical time a window spans is never represented, leaving the sparse frames on which a peptide crosses between metastable states difficult to learn. Here, we introduce PepTIDE, a multi-scale temporal framework for peptide trajectory generation. The stride of each training window is drawn from a continuous range, so one model observes the same dynamics at many temporal resolutions, and an Adaptive Physical-Time
Embedding encodes the relative interval of a window together with the absolute time of each frame, making the shared velocity field interval-aware. All frames are generated jointly through a
stochastic-interpolant flow, and pair-aware invariant point
attention captures the geometry and
topology of both linear and cyclic peptides. On PepMD and a 50-system cyclic-peptide
benchmark, PepTIDE reaches state-of-the-art distribution agreement and structural validity. Read in temporal order, its trajectories recover these sparse transition frames and reproduce inter-state fluxes more faithfully, confirming that modeling multiple temporal scales is what makes these rare events accessible.
Read the full abstract ▾
All authors Xichen Sun, Wentao Wei, Xiaoxi Zhang, Yuedong Yang, Jiahua Rao
Biomolecules
Molecular architecture of TE-seeded chromosome-naive initiation networks of protein-protein interactions delineates the Human Protein Map of the assembly of multiprotein complexes
Gennadi Glinsky
Upon binding to transposable elements seeded DNA, initiator transcription factors are poised to activate the hybrid assembly pathways (HAP) of endogenous multiprotein complexes (EMC) by iteratively engaging
protein-
protein interaction (PPI) cascade and creating
cell type-specific pools of
protein dimers. Here, molecular features and inferred biological functions of PPI cascade constituents were elucidated within the context of
cell type-specific intracellular
protein monomer pools documented in the Human
Protein Atlas (HPA) of 154 human
cell types. The following key features of the molecular architecture and functional performance of the 4 families of PPI cascade constituents were documented within the HPA contexts of intracellular proteomes: a) the ubiquitous expression and balanced representation; c) the universal availability and engagements; d) constitutive {invariant) and
cell type-specific assembly profiles of EMCs. The biological relevance of the PPI cascades is demonstrated by
cell-type-resolved multiprotein complex-engagement analysis across the HPA of human body, which faithfully recovers without supervision the intrinsic lineage structures of EMCs and lineage-specific master-regulator multiprotein assemblies. PPI cascade constituents have the capacity to generate 51,830
protein dimers within the proteome availability contexts of 154 types of human
cells. Present analyses revealed critical impacts of the persistence of proteinopathy
proteins that may cause the damage to the PPI cascade performance by sequestering 44 synaptic
proteins. The sequestration of synaptic
proteins withdraws structural-functional components of synapses suggesting a mechanism contributing to the pathogenesis of neurodegeneration. Conversely, reconstitution of availability of defined synaptic structural
proteins appears to restore stoichiometry and recovers the lost assembly of synaptic
protein dimers.
Read the full abstract ▾
Neurons and Cognition · Cross-listed from physics.bio-ph
Selection rules for the harmonic spectroscopy of animal decisions
Mohammad Salahshour
Spectroscopy reads structure by sending a structured probe into a system and measuring what comes back. Here we apply this principle to animal decision-making. Harmonic Theory casts choice as motion on an angular landscape over heading, whose
Fourier components form an animal's decision spectrum. We show that the arrangement of cues enters that landscape as a structure factor, so it factors like a diffraction amplitude, and
symmetry imposes selection rules: a p-fold cue array annihilates every harmonic that is not a multiple of p, and sweeping two cues apart draws a notch through each harmonic. Cue geometry becomes a tunable probe: the experimenter sets which harmonics an experiment can see. We read these spectra from public recordings of flies, schooling fish, larval zebrafish, mice and a swimming alga, and from the fly's neural compass, where symmetric scenes suppress the forbidden harmonics and breaking that
symmetry restores them. Measuring the compass form factor favours the theory's smooth bump over its square one, and raising cue contrast narrows that bump in every fly. A width fitted to one harmonic then predicts the next. Cue geometry is a spectrometer for decisions, and
symmetry sets what it can see.
Read the full abstract ▾
Biomolecules · Cross-listed from cs.LG
Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures
Shan Yu and Xuening Wu
Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry. A complementary challenge is to make the structural measurements and learned evidence behind each prediction directly inspectable. We introduce a supervised algebraic counting field (ACF) for predicting cryptic-pocket residues from apo structures. ACF compiles explicit
geometric, physicochemical, and topological features into compact, integer-weighted lookup tables. Each prediction score can be reconstructed from feature values, training counts, table weights, and spatial aggregation, without sequence search, structural-template transfer, or a
protein language model at inference. We evaluate ACF on CryptoBench and two locked external collections, separating ranking performance from the effects of residue-calling budgets. On an external set of 57 post-CryptoBench apo-holo units, ACF exceeded P2Rank by +0.044 in mean paired ROC-AUC (multiplicity-adjusted 95% CI [+0.010, +0.079]). The advantage was
dataset-dependent: official-fold ROC-AUC and matched-budget F1 differences against P2Rank remained unresolved, and a second external evaluation did not confirm gains from added structural features. ACF thus provides a compact predictor with externally validated signal and an inspectable path from structural measurements and training counts to residue scores.
Read the full abstract ▾
Quantitative Methods · Cross-listed from stat.AP
Efficient Convex Optimization Methods for State-Space Heartbeat Dynamics Models with Gamma Generalized Linear Models
Sabrina Liu, Andrew S. Perley and Todd P. Coleman
Objective: This work proposes a dynamic state-space statistical model of heartbeat dynamics whose optimal state estimate can be solved efficiently with convex optimization and used to assess autonomic and cardiovascular health. Methods: This state-space model combines a Gauss-Markov process prior with gamma emissions using a log-link function. Parameters are estimated using a static version of the model, and alternating direction method of multipliers is used to efficiently find the optimal maximum a priori latent state estimate. Model selection is performed by finding the tradeoff between model size and model fit using the partial autocorrelation function. Results: We demonstrate in synthetically generated heartbeats that this model is able to closely recover the underlying latent weights. Then, we show using a tilt table
dataset that this statistical model is able to better capture sharp transients in heart rate patterns than traditional local averaging methods. In addition, combining heart beat timings with information from other organs such as the respiratory phase, we show that our statistical modeling framework enables the extraction of information-theoretic measures to quantify inter-organ coupling such as respiratory sinus arrythmia. Conclusion: This statistical framework for modeling heartbeat dynamics provides an efficient framework that can provide physiologically meaningful insights. Significance: This work broadens potential tools that can be used to assess heartbeat dynamics.
Read the full abstract ▾
Neurons and Cognition
One Inference, Four Failure Modes: Formal Models of Why Pain Location Fails
Adam Y. Shavit
Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others. A companion paper argues this is not one gradient of diagnostic utility but three distinct failures of localization. This paper gives those failures their mathematics and shows they are one object: a single
Bayesian generative model failing at different nodes - the likelihood, the model class, and group- or context-dependence in that same likelihood. The count is not in dispute: the first three are failures of the inference that produces a felt location, and the fourth, added here, is a failure of reporting it. Anatomical multiplexing is a non-identifiable inverse problem: a rank-deficient referral
matrix sends distinct causes to one report. Delocalized amplification is a change of generative model whose dynamics are a neural-field bifurcation, with spatial extent as the order parameter. Referred and atypical displacement is group- or context-dependence in the presentation likelihood - a probability, not a loss - whose consequence sits one layer downstream, in a decision threshold varying with group prevalence and cost. Repeated observation cannot reduce recoverable information about a fixed inferential target, an exact chain-rule identity, while practical value can fall. The fourth node is the report itself. A spatial
Bayesian model factorizes mislocalization into referral blur, an anatomical offset and a precision-weighted cognitive override, and reproduces phantom-limb, mirror-box and central-post-stroke reports as regimes of one equation. It is estimable under conditions the paper states as necessary or sufficient rather than assuming them; Sec. 8 gives the design that meets them. One control is reported against the paper's own interest: the transport statistic first used to measure migration scores a stationary, spreading profile as though it had travelled.
Read the full abstract ▾
Populations and Evolution
A multi-scale immuno-epidemiological behavioral model for influenza-like illness: connecting scales through symptom scores
Binod Pant, Summer Atkins, Necibe Tuncer et al. (+1 more)
For many infectious diseases, behavior change is not a population-level reaction to rising case counts, but a personal one, triggered by how sick an individual feels. Yet most models fail to capture how illness severity drives this response. Multi-scale models that link the immunological scale to the epidemiological scale seem well-suited for capturing this behavior. Most multi-scale models link within-host viral load to population-level transmission rates through linking functions that are a function of viral load. However, viral load is often misaligned with symptom severity and thus possibly ill-suited for the purpose of informing behavior at a population level. We present a novel multi-scale model in which symptomatic individuals dynamically modify their transmission based on systemic symptom scores (a composite measure of muscle ache, fatigue, headache, and feverishness informed by within-host immune dynamics) while asymptomatic individuals do not change behavior. We find that stronger illness-driven behavior change delays and lowers the epidemic peak. Moreover, the resulting epidemic trajectory remains qualitatively similar to that of a standard SEIR model without behavior change, across several distinct choices of linking functions connecting the two scales. This stands in contrast to models where behavior change is driven by population-level feedback (e.g., reported cases or deaths), which can produce qualitatively distinct signatures such as plateaus, shoulders, or an elongated epidemic decline. In the case when an exponential function of symptom score is used as a linking function, we show that the standard SEIR model can closely reproduce the total number of infectious individuals at a given point in time generated through the multi-scale level.
Read the full abstract ▾
All authors Binod Pant, Summer Atkins, Necibe Tuncer, Hana M. Dobrovolny