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1. WO2020198068 - SYSTEMS AND METHODS FOR DERIVING AND OPTIMIZING CLASSIFIERS FROM MULTIPLE DATASETS

Publication Number WO/2020/198068
Publication Date 01.10.2020
International Application No. PCT/US2020/024036
International Filing Date 20.03.2020
IPC
G16B 20/00 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
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 45/00 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
45ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
G06N 20/00 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
CPC
G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines [SVM]
G06N 20/20
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
20Ensemble learning
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 7/005
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computer systems based on specific mathematical models
005Probabilistic networks
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 40/00
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
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Applicants
  • INFLAMMATIX, INC. [US]/[US]
Inventors
  • MAYHEW, Michael B.
  • BUTUROVIC, Ljubomir
  • SWEENEY, Timothy E.
  • LUETHY, Roland
  • KHATRI, Purvesh
Agents
  • ANTCZAK, Andrew J.
  • BALL, Conor
  • GE, Xinquan
  • ESKER, Todd, W.
  • LOVEJOY, Brett, A.
Priority Data
62/822,73022.03.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEMS AND METHODS FOR DERIVING AND OPTIMIZING CLASSIFIERS FROM MULTIPLE DATASETS
(FR) SYSTÈMES ET PROCÉDÉS DE DÉDUCTION ET D’OPTIMISATION DE CLASSIFICATEURS À PARTIR D’ENSEMBLES DE DONNÉES MULTIPLES
Abstract
(EN)
Systems and methods for subject clinical condition evaluation using a plurality of modules are provided. Modules comprise features whose corresponding feature values associate with an absence, presence or stage of phenotypes associated with the clinical condition. A first dataset is obtained having feature values, acquired through a first technical background from respective subjects in transcriptomic, proteomic, or metabolomic form, for at least a first of the plurality of modules. A second training dataset is obtained having feature values, acquired through a technical background other than the first technical background, from training subjects of the second dataset, in the same form as for the first dataset, of at least the first module. Inter-dataset batch effects are removed by co- normalizing feature values across the training datasets, thereby calculating co-normalized feature values used to train a classifier for clinical condition evaluation of the test subject.
(FR)
La présente invention concerne des systèmes et des procédés d’évaluation de l’état clinique d’un sujet au moyen d’une pluralité de modules. Les modules comprennent des caractéristiques dont les valeurs caractéristiques correspondantes sont associées à une absence, une présence ou un stade de phénotypes associés à l’état clinique. Un premier ensemble de données est obtenu et comporte des valeurs caractéristiques, acquises par l’intermédiaire d’un premier arrière-plan technique à partir de sujets respectifs sous forme transcriptomique, protéomique ou métabolomique, pour au moins un premier module parmi la pluralité de modules. Un deuxième ensemble de données d’apprentissage est obtenu et comporte des valeurs caractéristiques, acquises par l’intermédiaire d’un arrière-plan technique autre que le premier arrière-plan technique, à partir de sujets d’apprentissage du deuxième ensemble de données, sous le même formulaire que pour le premier ensemble de données, d’au moins le premier module. Des effets de lot inter-ensembles de données sont éliminés par conormalisation des valeurs caractéristiques pour tous les ensembles de données d’apprentissage, de façon à calculer des valeurs caractéristiques conormalisées utilisées pour entraîner un classificateur pour l’évaluation de l’état clinique du sujet d’essai.
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