Yuan, 2024 - Google Patents
MENDER: fast and scalable tissue structure identification in spatial omics dataYuan, 2024
View HTML- Document ID
- 4037082383678104965
- Author
- Yuan Z
- Publication year
- Publication venue
- Nature Communications
External Links
Snippet
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved …
- 238000004458 analytical method 0 abstract description 89
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
- G06F19/28—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
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- 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
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
-
- 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
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wei et al. | Spatial charting of single-cell transcriptomes in tissues | |
Bergenstråhle et al. | Super-resolved spatial transcriptomics by deep data fusion | |
Palla et al. | Squidpy: a scalable framework for spatial omics analysis | |
Arshadi et al. | SNT: a unifying toolbox for quantification of neuronal anatomy | |
Ma et al. | Spatially informed cell-type deconvolution for spatial transcriptomics | |
Bao et al. | Integrative spatial analysis of cell morphologies and transcriptional states with MUSE | |
Jin et al. | CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics | |
Dong et al. | Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder | |
Cable et al. | Robust decomposition of cell type mixtures in spatial transcriptomics | |
Walker et al. | Deciphering tissue structure and function using spatial transcriptomics | |
Ding et al. | Interpretable dimensionality reduction of single cell transcriptome data with deep generative models | |
Sun et al. | Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies | |
Phillip et al. | A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei | |
Stepniewska-Dziubinska et al. | Improving detection of protein-ligand binding sites with 3D segmentation | |
Yuan et al. | SODB facilitates comprehensive exploration of spatial omics data | |
Armingol et al. | Context-aware deconvolution of cell–cell communication with Tensor-cell2cell | |
Yuan et al. | Benchmarking spatial clustering methods with spatially resolved transcriptomics data | |
Yuan | MENDER: fast and scalable tissue structure identification in spatial omics data | |
Caldera et al. | Mapping the perturbome network of cellular perturbations | |
Bilgin et al. | BioSig3D: high content screening of three-dimensional cell culture models | |
Shen et al. | Comparative assessment and novel strategy on methods for imputing proteomics data | |
Yang et al. | Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network | |
Bergenstråhle et al. | SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation | |
Liang et al. | Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity | |
Si et al. | FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics |