METHOD AND SYSTEM FOR GENERATING A VISUAL REPRESENTATION
Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 03. Mai Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
ABERDEEN ALAN [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Sonstige Themen: |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2023-05-03, Last update posted on www.tib.eu: 2023-06-15, Last updated: 2023-06-23 |
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| Patentnummer: |
EP4172852 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA017183448 |
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| 245 | 1 | 0 | |a METHOD AND SYSTEM FOR GENERATING A VISUAL REPRESENTATION |
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| 520 | |a Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space. | ||
| 650 | 4 | |a G16B: Bioinformatics, i.e. information and communication technology [ict] specially adapted for genetic or protein-related data processing in computational molecular biology | |
| 650 | 4 | |a rest | |
| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
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