Katti et al., 2023 - Google Patents
Artificial intelligence in multiscale scaffolds for cancer organoids testbedKatti et al., 2023
- Document ID
- 7466463311317787908
- Author
- Katti D
- Katti K
- Gaikwad H
- Jaswandkar S
- Publication year
- Publication venue
- Artificial Intelligence in Tissue and Organ Regeneration
External Links
Snippet
The development of in vitro models that accurately recapitulate the complex biology of tumors is essential for advancing cancer research and drug discovery. Recent advances in tissue engineering and artificial intelligence (AI) have opened up new opportunities to create …
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
- G06F19/30—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
- 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
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sun et al. | Computer‐aided tissue engineering: overview, scope and challenges | |
| Cao et al. | Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation | |
| Zhang et al. | Multiscale agent-based cancer modeling | |
| Mackay et al. | The future of bone regeneration: integrating AI into tissue engineering | |
| Han et al. | R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data | |
| Kim et al. | Engineering tissue fabrication with machine intelligence: generating a blueprint for regeneration | |
| Mohammadrezaei et al. | Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models | |
| Hammel et al. | A new flow-regulating cell type in the demosponge Tethya wilhelma–functional cellular anatomy of a leuconoid canal system | |
| Robles-Bykbaev et al. | An artificial-vision-and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems | |
| Norfleet et al. | Computational modeling of organoid development | |
| Carotenuto et al. | Towards a material-by-design approach to electrospun scaffolds for tissue engineering based on statistical design of experiments (DOE) | |
| Valvez et al. | Computational flow dynamic analysis in left atrial appendage thrombus formation risk: A review | |
| d'Alessandro et al. | Unraveling liver complexity from molecular to organ level: Challenges and perspectives | |
| Zhou et al. | AI for biofabrication | |
| da Silva et al. | Machine learning approaches to 3D models for drug screening | |
| Krasnyakov et al. | Cell-based modeling of tissue developing in the scaffold pores of varying cross-sections | |
| Sheikh et al. | Deep learning for predicting spheroid viability: novel convolutional neural network model for automating quality control for three-dimensional bioprinting | |
| Oncu et al. | Comparative analysis of deep learning models for predicting biocompatibility in tissue scaffold images | |
| Zadpoor | Biomaterials and tissue biomechanics: a match made in heaven? | |
| Rodriguez-Salvador et al. | Discovering the latest scientific pathways on tissue spheroids: opportunities to innovate | |
| Katti et al. | Artificial intelligence in multiscale scaffolds for cancer organoids testbed | |
| Pemmada et al. | ML and AI approaches for design of tissue scaffolds | |
| CN120530419A (en) | Monitoring of biological manufacturing processes | |
| Wu et al. | Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level | |
| Mukherjee et al. | Optimization of bio-ink using machine learning |