Provided are a method of detecting chromosomal aneuploidy of a targeted fetal chromosome, and a computer-readable medium having recorded thereon a program to be applied to performing the method. According to the present disclosure, fetal chromosomal aneuploidy may be non-invasively and prenatally diagnosed with excellent sensitivity and specificity.

Provided are a method of detecting chromosomal aneuploidy of a targeted fetal chromosome, and a computer-readable medium having recorded thereon a program to be applied to performing the method. According to the present disclosure, fetal chromosomal aneuploidy may be non-invasively and prenatally diagnosed with excellent sensitivity and specificity.

This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide-base calls (e.g., variant calls) of a call-generation model. For instance, the disclosed systems can train and utilize a call-recalibration-machine-learning model to generate a set of predicted variant-call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant-call classifications, the disclosed systems can further update or modify nucleotide-base calls (e.g., variant calls) corresponding to genomic coordinates. Indeed, the disclosed systems can generate an initial nucleotide-base call based on sequencing metrics for nucleotide reads of a sample sequence utilizing a call-generation model and further utilize a call-recalibration-machine-learning model to generate classification predictions for updating or recalibrating the initial nucleotide-base call from a subset of the same sequencing metrics or other sequencing metrics.

This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide-base calls (e.g., variant calls) of a call-generation model. For instance, the disclosed systems can train and utilize a call-recalibration-machine-learning model to generate a set of predicted variant-call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant-call classifications, the disclosed systems can further update or modify nucleotide-base calls (e.g., variant calls) corresponding to genomic coordinates. Indeed, the disclosed systems can generate an initial nucleotide-base call based on sequencing metrics for nucleotide reads of a sample sequence utilizing a call-generation model and further utilize a call-recalibration-machine-learning model to generate classification predictions for updating or recalibrating the initial nucleotide-base call from a subset of the same sequencing metrics or other sequencing metrics.

The present invention provides a novel method for the classification of adjuvants. In one embodiment, the present invention provides a method for generating organ transcriptome profiles for adjuvants, said method comprising: (A) a step for obtaining expression data by performing transcriptome analysis for at least one organ of a target organism by using at least two adjuvants; (B) a step for clustering the adjuvants with respect to the expression data; and (C) a step for generating the organ transcriptome profile for the adjuvants on the basis of the clustering.

The present invention provides a novel method for the classification of adjuvants. In one embodiment, the present invention provides a method for generating organ transcriptome profiles for adjuvants, said method comprising: (A) a step for obtaining expression data by performing transcriptome analysis for at least one organ of a target organism by using at least two adjuvants; (B) a step for clustering the adjuvants with respect to the expression data; and (C) a step for generating the organ transcriptome profile for the adjuvants on the basis of the clustering.

The disclosure provides gene fusions, gene variants, and novel associations with disease states, as well as kits, probes, and methods of using the same.

An image generation device includes an imaging unit configured to convert data representing an expression level for each microRNA type into image-rendition data serving as data representing a matrix of two dimensions or more, a classification unit configured to perform classification of the image-rendition data, and a contribution-presentation-image generation unit configured to generate a contribution-presentation image representing a contribution of a specific part of the image-rendition data to the classification.

An image generation device includes an imaging unit configured to convert data representing an expression level for each microRNA type into image-rendition data serving as data representing a matrix of two dimensions or more, a classification unit configured to perform classification of the image-rendition data, and a contribution-presentation-image generation unit configured to generate a contribution-presentation image representing a contribution of a specific part of the image-rendition data to the classification.

A method for identifying by mass spectrometry an unknown microorganism subgroup among a set of reference subgroups, including a step of constructing one knowledgebase and one classifying model per associated subgroup on the basis of the acquisition of at least one set of learning spectra of microorganisms identified as belonging to the subgroups of a group and including: constructing an adjusting model allowing mass-to-charge offsets of the acquired spectra to be corrected on the basis of reference masses-to-charges that are common to the various subgroups; adjusting the masses-to-charges of all of the lists of peaks of the learning spectra and constructing one classifying model per subgroup and the associated knowledgebase on the basis of the adjusted learning spectra.