The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

The subject matter presently claimed relates to a data quality management system and method whereby a first data point comprising a first obtained data and a first assigned value from is received from a first data repository, a first quality score as well as a first storable data of the first data point is determined and/or stored. A second data point comprising a second obtained data, which is similar to the first obtained data according to a predefined similarity measure, and a second assigned value is received from the second data repository, a second quality score as well as a second storable data is determined from the second data point and/or stored and a second transmittable data, determined from the second data point and/or the second quality score is transmitted to the first data repository, causing the first data repository to re-evaluate the first assigned value.

The subject matter presently claimed relates to a data quality management system and method whereby a first data point comprising a first obtained data and a first assigned value from is received from a first data repository, a first quality score as well as a first storable data of the first data point is determined and/or stored. A second data point comprising a second obtained data, which is similar to the first obtained data according to a predefined similarity measure, and a second assigned value is received from the second data repository, a second quality score as well as a second storable data is determined from the second data point and/or stored and a second transmittable data, determined from the second data point and/or the second quality score is transmitted to the first data repository, causing the first data repository to re-evaluate the first assigned value.

The disclosure relates to analysing genetic data. In one arrangement, a method operates on input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. A set of one or more fine-mapped variants is identified for each association. A fine-mapping predictive model is calculated on the basis of the input data and the set of fine-mapped variants. The effect on the target phenotype of the set of fine-mapped variants is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

The disclosure relates to analysing genetic data. In one arrangement, a method operates on input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. A set of one or more fine-mapped variants is identified for each association. A fine-mapping predictive model is calculated on the basis of the input data and the set of fine-mapped variants. The effect on the target phenotype of the set of fine-mapped variants is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.

Method to identify microorganisms in a sample, by evaluating the vibrational profile.

Method to identify microorganisms in a sample, by evaluating the vibrational profile.

A method and system to decode information stored on a polymer sequence, such as a DNA strand, is described herein. The method and system use molecular probes to label sections of the polymer sequence. Each molecular probe includes a fluorophore and a quencher. The fluorophore produces light with a color and wavelength corresponding to the information stored on the section of the polymer sequence the molecular probe labels. The quencher inhibits the production of light by an adjacent fluorophore. When adjacent sections of the polymer sequence are labeled with molecular probes, the fluorophore of the leading molecular probe produces light while the trailing molecular probe's light is quenched. The method and system then sequentially unbind the molecular probes from the sections of the polymer sequence within a waveguide, producing a sequence of observable fluorescence signals. The sequence can be used to determine the information stored on a polymer sequence.

A method and system to decode information stored on a polymer sequence, such as a DNA strand, is described herein. The method and system use molecular probes to label sections of the polymer sequence. Each molecular probe includes a fluorophore and a quencher. The fluorophore produces light with a color and wavelength corresponding to the information stored on the section of the polymer sequence the molecular probe labels. The quencher inhibits the production of light by an adjacent fluorophore. When adjacent sections of the polymer sequence are labeled with molecular probes, the fluorophore of the leading molecular probe produces light while the trailing molecular probe's light is quenched. The method and system then sequentially unbind the molecular probes from the sections of the polymer sequence within a waveguide, producing a sequence of observable fluorescence signals. The sequence can be used to determine the information stored on a polymer sequence.

Disclosed herein are methods and systems of utilizing sequencing reads for detecting and quantifying the presence of a tissue type or a disease type in cell-free DNA prepared from blood samples.