The present disclosure relates to an authentication device using base sequence information of DNAs included in a medium, the authentication device including an authentication means provided in a medium, in which the plurality of DNA base sequence information are included in authentication information derived by reading out the authentication means composed of a plurality of DNAs.

The present disclosure relates to an authentication device using base sequence information of DNAs included in a medium, the authentication device including an authentication means provided in a medium, in which the plurality of DNA base sequence information are included in authentication information derived by reading out the authentication means composed of a plurality of DNAs.

Disclosed herein include systems, devices, and methods for determining a variable number tandem repeat (VNTR) status. Haplotypes of a VNTR can be determined using long sequence reads of reference samples aligned to the VNTR in a reference. Short reads of a test sample of a test subject can be aligned to the haplotypes determined using the long sequence reads to determine a VNTR status (e.g., one or more haplotypes or a genotype of the test subject) of the test subject based on the probability indications of the haplotypes.

Disclosed herein include systems, devices, and methods for determining a variable number tandem repeat (VNTR) status. Haplotypes of a VNTR can be determined using long sequence reads of reference samples aligned to the VNTR in a reference. Short reads of a test sample of a test subject can be aligned to the haplotypes determined using the long sequence reads to determine a VNTR status (e.g., one or more haplotypes or a genotype of the test subject) of the test subject based on the probability indications of the haplotypes.

We disclose a system. The system comprises a memory and a runtime logic. The memory stores a plurality of specialist signal profilers. Each specialist signal profiler in the plurality of specialist signal profilers is trained to maximize signal-to-noise ratio of sequenced signals in a particular signal profile detected for analytes in a particular analyte class and characterized in a particular training data set. The runtime logic, having access to the memory, is configured to execute a base calling operation by applying respective specialist signal profilers in the plurality of specialist signal profilers to sequenced signals in respective signal profiles detected for analytes in respective analyte classes during the base calling operation.

We disclose a system. The system comprises a memory and a runtime logic. The memory stores a plurality of specialist signal profilers. Each specialist signal profiler in the plurality of specialist signal profilers is trained to maximize signal-to-noise ratio of sequenced signals in a particular signal profile detected for analytes in a particular analyte class and characterized in a particular training data set. The runtime logic, having access to the memory, is configured to execute a base calling operation by applying respective specialist signal profilers in the plurality of specialist signal profilers to sequenced signals in respective signal profiles detected for analytes in respective analyte classes during the base calling operation.

The present disclosure proposes an apparatus for determining an intestinal microbiome index. The apparatus according to the present disclosure may: obtain test information about a biological sample of a subject from a test apparatus; determine, based on the test information, first information about the similarity of intestinal microflora, second information about a proportion of harmful intestinal microflora, third information about a proportion of beneficial intestinal microflora, and/or fourth information about the diversity of intestinal microflora; and determine an intestinal microbiome index indicating a state of intestinal microbiome of the subject based on the determined information.

The present disclosure proposes an apparatus for determining an intestinal microbiome index. The apparatus according to the present disclosure may: obtain test information about a biological sample of a subject from a test apparatus; determine, based on the test information, first information about the similarity of intestinal microflora, second information about a proportion of harmful intestinal microflora, third information about a proportion of beneficial intestinal microflora, and/or fourth information about the diversity of intestinal microflora; and determine an intestinal microbiome index indicating a state of intestinal microbiome of the subject based on the determined information.

A predicting method of cell deconvolution based on a convolutional neural network is provided. The convolutional neural network technology is used to speculate the cell type composition proportion of a tissue from single-cell RNA sequencing data. Compared with a traditional cell deconvolution algorithm, the predicting method of cell deconvolution based on a convolutional neural network overcomes the defects that the traditional cell deconvolution algorithm needs to carry out complex data preprocessing and needs to design a mathematical algorithm to standardize the single-cell sequencing data. According to the convolutional neural network designed by the present disclosure, hidden features can be extracted from the single-cell RNA sequencing data, network nodes have very high robustness to noise and errors of the data, and internal relations among various genes are fully mined, so that the cell deconvolution performance is improved. Meanwhile, the model of the present disclosure is established based on the neural network.

A predicting method of cell deconvolution based on a convolutional neural network is provided. The convolutional neural network technology is used to speculate the cell type composition proportion of a tissue from single-cell RNA sequencing data. Compared with a traditional cell deconvolution algorithm, the predicting method of cell deconvolution based on a convolutional neural network overcomes the defects that the traditional cell deconvolution algorithm needs to carry out complex data preprocessing and needs to design a mathematical algorithm to standardize the single-cell sequencing data. According to the convolutional neural network designed by the present disclosure, hidden features can be extracted from the single-cell RNA sequencing data, network nodes have very high robustness to noise and errors of the data, and internal relations among various genes are fully mined, so that the cell deconvolution performance is improved. Meanwhile, the model of the present disclosure is established based on the neural network.