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Method for simultaneous multivariate feature selection, feature generation, and sample clustering

A multivariate analysis and sample technology, applied in the fields of biostatistics, bioinformatics, instruments, etc., can solve the problem that test developers are rarely guided, and achieve the effect of efficient detection

Pending Publication Date: 2020-06-19
KONINKLJIJKE PHILIPS NV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the structure of an autoencoder needs to be defined in advance, and optimization results as well as data compression strongly depend on this predefined structure; however, there is little guidance available to the test developer on how to best pick such a structure

Method used

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  • Method for simultaneous multivariate feature selection, feature generation, and sample clustering
  • Method for simultaneous multivariate feature selection, feature generation, and sample clustering
  • Method for simultaneous multivariate feature selection, feature generation, and sample clustering

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Embodiment Construction

[0022] Some methods disclosed herein for genomic / proteomic test integration proceed in two stages. First, univariate feature pre-selection is performed, since there is a possibility that even a single feature provides an important representation of the dataset. Next, the process iterates over the features ranked by the analysis results of the first step and detects associated sample clusters, while performing forward selection and nonlinear transformation of features. Cluster properties such as connectivity, homogeneity, and / or etc. may be evaluated to include or exclude certain features from further iterations. One or more sets of discriminative features are obtained, along with associated sample clusters that characterize the data set based on the selected criteria. For clinical applications, discriminative features are linked to sample groups defined by clinical variables to provide analytical solutions for predictive diagnostics and biomarker detection.

[0023] The disc...

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Abstract

A genomic / proteomic test synthesis method includes receiving a genomic / proteomic data set (12) comprising samples corresponding to persons with each sample including values of features of a set of features derived from genomic / proteomic data for the corresponding person. For each feature, univariate analysis (30) is performed to generate a sample density versus feature value data set for the feature, for example represented as a kernel density estimate (KDE) (52). Multivariate analysis (32, 34) is performed on the features using the KDEs to generate a set of discriminative features (36, 38). In one example, the multivariate analysis (32) uses energy spectral density (ESD) characteristics of the KDEs. In another example, the multivariate analysis (34) uses peak location characteristics of the KDEs.

Description

technical field [0001] The following generally relates to the field of clinical testing, the field of genomic testing, the field of proteomic testing, and related fields. Background technique [0002] Genomic and proteomic tests are increasingly used as tools for diagnosing and typing cancer, identifying causative strains, and other clinical tasks. These techniques are capable of generating large amounts of data. [0003] Genomic testing can use next-generation sequencing (NGS) to acquire whole genome sequences (WGS), whole exome sequences (WES, including only protein-coding exons), RNA sequences, etc. In a typical NGS workflow, tissue samples from cancerous tumors or other tissues of interest are obtained via biopsy or other interventional procedures. Wet lab processing is used to extract, clean up, or otherwise prepare deoxyribonucleic acid (DNA) from samples, followed by target enrichment (eg, for WES), polymerase chain reaction (PCR) amplification, and / or other sample ...

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Application Information

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IPC IPC(8): G16B40/30G16B40/00G16B40/20G16B50/00
CPCG16B40/00G16B50/00G16B40/30
Inventor K·沃良斯基N·迪米特罗娃
Owner KONINKLJIJKE PHILIPS NV
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