Copertina

Inferring stochastic dynamics with growth from cross-sectional data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Stephen, Maddu, Suryanarayana, Qiu, Xiaojie, Chardès, Victor
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Biological Physics Quantitative Methods
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Inferring biological processes with intrinsic noise from cross-sectional data
di: Maddu, Suryanarayana, et al.
Pubblicazione: (2024)

Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data
di: Chardès, Victor
Pubblicazione: (2025)

Learning biophysical models of gene regulation with probability flow matching
di: Maddu, Suryanarayana, et al.
Pubblicazione: (2026)

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
di: Zhang, Zhenyi, et al.
Pubblicazione: (2025)

Machine learning approaches for interpretable antibody property prediction using structural data
di: Michalewicz, Kevin, et al.
Pubblicazione: (2025)

adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
di: Rosset, Lorenzo, et al.
Pubblicazione: (2025)

Protein generation with embedding learning for motif diversification
di: Michalewicz, Kevin, et al.
Pubblicazione: (2025)

The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map
di: Khodaee, Farhan, et al.
Pubblicazione: (2025)

Enhancing Swarms Durability to Threats via Graph Signal Processing and GNN-based Generative Modeling
di: Karin, Jonathan, et al.
Pubblicazione: (2025)

Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
di: Rossi, Ivan, et al.
Pubblicazione: (2025)

Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops
di: Borisiak, Kevin, et al.
Pubblicazione: (2024)

Inferring the dynamics of underdamped stochastic systems
di: Brückner, David B., et al.
Pubblicazione: (2020)

Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks
di: Mircea, Maria, et al.
Pubblicazione: (2024)

Explainable Deep Learning Framework for SERS Bio-quantification
di: Zaki, Jihan K., et al.
Pubblicazione: (2024)

Learning noisy tissue dynamics across time scales
di: Han, Ming, et al.
Pubblicazione: (2025)

Nonlinear memory in cell division dynamics across species
di: Zhang, Shijie, et al.
Pubblicazione: (2024)

Adaptive cross-country optimisation strategies in thermal soaring birds
di: Keskin, Göksel, et al.
Pubblicazione: (2024)

Predicting Soil Macronutrient Levels: A Machine Learning Approach Models Trained on pH, Conductivity, and Average Power of Acid-Base Solutions
di: Kumar, Mridul, et al.
Pubblicazione: (2025)

AI-powered simulation-based inference of a genuinely spatial-stochastic model of early mouse embryogenesis
di: Ramirez-Sierra, Michael A., et al.
Pubblicazione: (2024)

Inferring processes within dynamic forest models using hybrid modeling
di: Pichler, Maximilian, et al.
Pubblicazione: (2025)

Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders
di: Calderon, Josuan, et al.
Pubblicazione: (2024)

Mapping gene expression dynamics to developmental phenotypes with information entropy analysis
di: Ansbacher, Ben, et al.
Pubblicazione: (2025)

A control theoretical approach to gene regulation raises quantitative constraints for dynamic homeostasis in stochastic gene expression
di: Giovanini, Guilherme, et al.
Pubblicazione: (2025)

bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction
di: Zhang, Alex Chen Yi, et al.
Pubblicazione: (2024)

ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression
di: Shen, Yuning, et al.
Pubblicazione: (2025)

Global graph features unveiled by unsupervised geometric deep learning
di: Granfors, Mirja, et al.
Pubblicazione: (2025)

Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics
di: Shoghi, Nima, et al.
Pubblicazione: (2026)

Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
di: Zhan, Xianghao, et al.
Pubblicazione: (2023)

Thermal Stress Disrupts Symbiotic Fluid Dynamics in Bobtail Squid
di: Williams, Stephen, et al.
Pubblicazione: (2025)

Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality
di: Auslender, Ilya, et al.
Pubblicazione: (2023)

Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport
di: Zhang, Zhenyi, et al.
Pubblicazione: (2024)

Correlation between Gyral Size, Brain Size, and Head Impact Risk across Mammalian Species
di: Zhang, Nianqin, et al.
Pubblicazione: (2022)

Learning minimal representations of stochastic processes with variational autoencoders
di: Fernández-Fernández, Gabriel, et al.
Pubblicazione: (2023)

Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)
di: Manzo, Carlo
Pubblicazione: (2021)

Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networks
di: Wen, Haizhou, et al.
Pubblicazione: (2025)

Double projection for reconstructing dynamical systems: between stochastic and deterministic regimes
di: Sip, Viktor, et al.
Pubblicazione: (2025)

Impact of phylogeny on structural contact inference from protein sequence data
di: Dietler, Nicola, et al.
Pubblicazione: (2022)

Simulation of snakes using vertical body bending to traverse terrain with large height variation
di: Zhang, Yifeng, et al.
Pubblicazione: (2022)

Discovering stochastic dynamical equations from biological time series data
di: Nabeel, Arshed, et al.
Pubblicazione: (2022)

Enhanced Spatial Clustering of Single-Molecule Localizations with Graph Neural Networks
di: Pineda, Jesús, et al.
Pubblicazione: (2024)