Processing

Please wait...

Settings

Settings

Goto Application

1. WO2021058710 - MODELLING METHOD USING A CONDITIONAL VARIATIONAL AUTOENCODER

Publication Number WO/2021/058710
Publication Date 01.04.2021
International Application No. PCT/EP2020/076832
International Filing Date 25.09.2020
IPC
G16B 40/30 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY 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
30Unsupervised data analysis
G06N 3/04 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
G06N 20/00 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G16B 25/10 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
25ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
10Gene or protein expression profiling; Expression-ratio estimation or normalisation
CPC
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/0472
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0472using probabilistic elements, e.g. p-rams, stochastic processors
G06N 3/088
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
088Non-supervised learning, e.g. competitive learning
G16B 25/10
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
25ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
10Gene or protein expression profiling; Expression-ratio estimation or normalisation
G16B 40/30
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
30Unsupervised data analysis
Applicants
  • HELMHOLTZ ZENTRUM MÜNCHEN - DEUTSCHES FORSCHUNGSZENTRUM FÜR GESUNDHEIT UND UMWELT (GMBH) [DE]/[DE]
Inventors
  • THEIS, Fabian
  • LOTFOLLAHI, Mohammad
  • WOLF, Fabian Alexander
Agents
  • WEINZIERL, Gerhard
  • SCHIWECK, Wolfram Dr.
  • KOCH, Andreas Dr.
  • HAGEMANN, Borys
Priority Data
LU10141425.09.2019LU
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) MODELLING METHOD USING A CONDITIONAL VARIATIONAL AUTOENCODER
(FR) PROCÉDÉ DE MODÉLISATION À L'AIDE D'UN AUTO-ENCODEUR VARIATIONNEL CONDITIONNEL
Abstract
(EN)
The present invention relates to a computer-implemented method for modelling genomic data represented in an unsupervised neural network, trVAE, comprising a conditional variational autoencoder, CVAE, with an encoder (f) and a decoder (g).
(FR)
La présente invention concerne un procédé mis en oeuvre par ordinateur pour modéliser des données génomiques représentées dans un réseau neuronal non supervisé, trVAE, comprenant un auto-encodeur variationnel conditionnel, CVAE, avec un encodeur (f) et un décodeur (g).
Latest bibliographic data on file with the International Bureau