Romeo et al., 2021 - Google Patents
Learning developmental mode dynamics from single-cell trajectoriesRomeo et al., 2021
View PDF- Document ID
- 6328942966238455866
- Author
- Romeo N
- Hastewell A
- Mietke A
- Dunkel J
- Publication year
- Publication venue
- Elife
External Links
Snippet
Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at …
- 238000000034 method 0 abstract description 13
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Romeo et al. | Learning developmental mode dynamics from single-cell trajectories | |
| Wang et al. | An expert's guide to training physics-informed neural networks | |
| Krakauer et al. | The challenges and scope of theoretical biology | |
| Sbalzarini | Modeling and simulation of biological systems from image data | |
| Friston et al. | A variational synthesis of evolutionary and developmental dynamics | |
| Cofré et al. | Information entropy production of maximum entropy Markov chains from spike trains | |
| Cislo et al. | Active cell divisions generate fourfold orientationally ordered phase in living tissue | |
| Pastor-Escuredo et al. | How computation is helping unravel the dynamics of morphogenesis | |
| de Dios Rojas Olvera et al. | Observational cosmology with artificial neural networks | |
| Thomas et al. | Topological data analysis of c. elegans locomotion and behavior | |
| Tegnér et al. | A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference | |
| Karaca et al. | Computational fractional-order calculus and classical calculus AI for comparative differentiability prediction analyses of complex-systems-grounded paradigm | |
| Albalawi et al. | Well-posedness and Ulam-Hyers stability results of solutions to pantograph fractional stochastic differential equations in the sense of conformable derivatives | |
| Treml et al. | Modeling and analysis of cardiac hybrid cellular automata via GPU-accelerated Monte Carlo simulation | |
| Zhang et al. | Online machine learning for accelerating molecular dynamics modeling of cells | |
| Bardella et al. | Neural activity in quarks language: Lattice Field Theory for a network of real neurons | |
| Burark et al. | CoDBench: a critical evaluation of data-driven models for continuous dynamical systems | |
| Hafez et al. | Review on recent advances in fractional differentiation and its applications | |
| Ghamari et al. | Sampling a rare protein transition using quantum annealing | |
| Vinuesa et al. | Opportunities for machine learning in scientific discovery | |
| Boltz et al. | Kinetic theory of self-propelled particles with nematic alignment | |
| Tøndel et al. | Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling | |
| Legrand et al. | pyhgf: A neural network library for predictive coding | |
| Yatskou et al. | Simulation modelling and machine learning platform for processing fluorescence spectroscopy data | |
| Diez et al. | An optimal transport model for dynamical shapes, collective motion and cellular aggregates |