Nothing Special   »   [go: up one dir, main page]

Walter et al., 2010 - Google Patents

Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging

Walter et al., 2010

View PDF
Document ID
15291438212624920998
Author
Walter T
Held M
Neumann B
Hériché J
Conrad C
Pepperkok R
Ellenberg J
Publication year
Publication venue
Journal of structural biology

External Links

Snippet

High-throughput time-lapse microscopy is an excellent way of studying gene function by collecting time-resolved image data of the cellular responses to gene perturbations. With the increase in both data amount and complexity, computational methods capable of dealing …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • 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
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
    • 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
    • G06F19/10—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
    • 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
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
Walter et al. Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging
Sommer et al. A deep learning and novelty detection framework for rapid phenotyping in high-content screening
Caicedo et al. Data-analysis strategies for image-based cell profiling
Wang et al. Deep learning approach to peripheral leukocyte recognition
Sommer et al. Machine learning in cell biology–teaching computers to recognize phenotypes
Kraus et al. Automated analysis of high‐content microscopy data with deep learning
Angermueller et al. Deep learning for computational biology
Wang et al. Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy
Harder et al. Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time
Dzyubachyk et al. Automated analysis of time-lapse fluorescence microscopy images: from live cell images to intracellular foci
Bakker et al. Morphologically constrained and data informed cell segmentation of budding yeast
Chessel An overview of data science uses in bioimage informatics
Handfield et al. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins
Johnson et al. Joint modeling of cell and nuclear shape variation
Müssel et al. BiTrinA—multiscale binarization and trinarization with quality analysis
Qu et al. Simultaneous recognition and segmentation of cells: application in C. elegans
Laksameethanasan et al. cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes
Janssens et al. Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
Boyd et al. Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen
Budak et al. Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method
Kim et al. Self-supervision advances morphological profiling by unlocking powerful image representations
Harder et al. Large‐scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high‐throughput screens of neuroblastoma cells
US20240046671A1 (en) High dimensional spatial analysis
Pastore et al. Efficient unsupervised learning of biological images with compressed deep features
Nibid et al. Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer