Processing

Please wait...

Settings

Settings

Goto Application

1. WO2020072383 - DEEP LEARNING PARTICLE CLASSIFICATION PLATFORM

Publication Number WO/2020/072383
Publication Date 09.04.2020
International Application No. PCT/US2019/053880
International Filing Date 30.09.2019
IPC
G06K 9/00 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06K 9/62 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
CPC
G01N 15/1459
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
10Investigating individual particles
14Electro-optical investigation, e.g. flow cytometers
1456without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
1459the analysis being performed on a sample stream
G01N 2015/1006
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
10Investigating individual particles
1006for cytology
G05B 13/027
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
0265the criterion being a learning criterion
027using neural networks only
G06K 9/6227
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6227Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
G06K 9/6273
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
6268relating to the classification paradigm, e.g. parametric or non-parametric approaches
627based on distances between the pattern to be recognised and training or reference patterns
6271based on distances to prototypes
6272based on distances to cluster centroïds
6273Smoothing the distance, e.g. Radial Basis Function Networks
G16B 20/40
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
40Population genetics; Linkage disequilibrium
Applicants
  • FLOWJO, LLC [US]/[US]
  • BECTON, DICKINSON AND COMPANY [US]/[US]
Inventors
  • LAI, Janice H.
  • VELAZQUEZ-PALAFOX, Miguel
  • TAYLOR, Ian
Agents
  • SIERA, Scott, G.
Priority Data
62/739,79601.10.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) DEEP LEARNING PARTICLE CLASSIFICATION PLATFORM
(FR) PLATE-FORME DE CLASSIFICATION DE PARTICULES À APPRENTISSAGE PROFOND
Abstract
(EN)
Methods and systems for a deep-learning platform for sorting cell populations. An example method includes executing a software-platform associated with analyzing received flow cytometry data obtained via an acquisition device in communication with the computing system, and the software-platform sorting cell populations indicated in the flow cytometry data. User input is received indicating selection of a deep-learning module, the deep-learning module being obtained via a network to supplement the software-platform. The flow cytometry data is analyzed and a machine learning model is selected which was trained based on similar phenotype information as indicated in the flow cytometry data. The machine learning model is applied based on the flow cytometry data, the information being normalized based on the UMI counts associated with the flow cytometry data. A graphical representation of cell populations indicated in the flow cytometry data is presented, the graphical representation sorting the cell populations according to phenotype information.
(FR)
L'invention concerne des procédés et des systèmes pour une plate-forme d'apprentissage profond destinée à trier des populations de cellules. L'invention concerne un procédé donné à titre d'exemple, consistant à exécuter une plate-forme logicielle associée à une analyse de données de cytométrie en flux reçues, obtenues par l'intermédiaire d'un dispositif d'acquisition en communication avec le système informatique, et à trier, au moyen de la plate-forme logicielle, des populations de cellules indiquées dans les données de cytométrie en flux. Une entrée utilisateur est reçue, ladite entrée indiquant la sélection d'un module d'apprentissage profond, ledit module étant obtenu par l'intermédiaire d'un réseau pour compléter la plate-forme logicielle. Les données de cytométrie en flux sont analysées et un modèle d'apprentissage automatique est sélectionné, ledit modèle ayant été entraîné en fonction d'informations de phénotypes similaires, telles qu'indiquées dans les données de cytométrie en flux. Le modèle d'apprentissage machine est appliqué en fonction des données de cytométrie en flux, les informations étant normalisées en fonction du nombre total d'identifiants moléculaires uniques (UMI) associé aux données de cytométrie en flux. Une représentation graphique de populations de cellules indiquées dans les données de cytométrie en flux est présentée, ladite représentation triant les populations de cellules en fonction d'informations de phénotypes.
Latest bibliographic data on file with the International Bureau