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1. WO2018080522 - TARGET CLASS FEATURE MODEL

Publication Number WO/2018/080522
Publication Date 03.05.2018
International Application No. PCT/US2016/059556
International Filing Date 28.10.2016
IPC
G06F 19/00 2011.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
G01N 33/00 2006.01
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
CPC
G06F 16/285
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
28Databases characterised by their database models, e.g. relational or object models
284Relational databases
285Clustering or classification
G06F 16/288
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
28Databases characterised by their database models, e.g. relational or object models
284Relational databases
288Entity relationship models
G16B 20/00
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
G16B 40/20
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
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
G16C 20/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
70Machine learning, data mining or chemometrics
G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
Applicants
  • HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P. [US]/[US]
Inventors
  • LIU, Lei
  • ROGACS, Anita
Agents
  • BURROWS, Sarah E.
  • RATHE, Todd A.
Priority Data
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) TARGET CLASS FEATURE MODEL
(FR) MODÈLE DE CARACTÉRISTIQUES D'UNE CATÉGORIE CIBLE
Abstract
(EN)
A method may include sensing first data samples from a first set of different subjects having a membership in a target class and sensing second data samples from a second set of different subjects not having a membership in the target class, wherein each of the first data samples and the second data samples includes a composite of individual data features. The individual data features from each composite of the first data samples and the second data samples are extracted and quantified. Sets of features and associated weightings of a target class model are identified based upon quantified values of the individual features from each composite of the first samples and the second samples to create a model representing a fingerprint of the target class to determine membership status of a sample having an unknown membership status with respect to the target class.
(FR)
Un procédé peut comprendre les étapes consistant à : détecter de premiers échantillons de données provenant d'un premier ensemble de sujets différents présentant une appartenance à une catégorie cible ; détecter de seconds échantillons de données provenant d'un second ensemble de sujets différents ne présentant pas d'appartenance à la catégorie cible, chacun des premiers et seconds échantillons de données comprenant une combinaison de caractéristiques de données individuelles ; extraire et quantifier les caractéristiques de données individuelles issues de chaque combinaison des premiers et seconds échantillons de données ; et identifier des ensembles de caractéristiques et de pondérations associées d'un modèle de la catégorie cible sur la base des valeurs quantifiées des caractéristiques individuelles issues de chaque combinaison des premiers et seconds échantillons de façon à créer un modèle représentant une empreinte de la catégorie cible afin de déterminer un état d'appartenance d'un échantillon présentant un état d'appartenance inconnu à la catégorie cible.
Also published as
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