Patents by Inventor Kishore JAGANATHAN
Kishore JAGANATHAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20210265009Abstract: The technology disclosed relates to artificial intelligence-based base calling of index sequences. The technology disclosed accesses index images generated for the index sequences during index sequencing cycles of a sequencing run. The index images depict intensity emissions generated as a result of nucleotide incorporation in the index sequences during the sequencing run. The technology disclosed normalizes an index image from a current index sequencing cycle based on (i) intensity values of index images from one or more preceding index sequencing cycles, (ii) intensity values of index images from one or more succeeding index sequencing cycles, and (iii) intensity values of index images from the current index sequencing cycle. The technology disclosed processes normalized versions of the index images through a neural network-based base caller and generates a base call for each of the index sequencing cycles, thereby producing index reads for the index sequences.Type: ApplicationFiled: February 12, 2021Publication date: August 26, 2021Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Amirali KIA
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Publication number: 20210265016Abstract: The technology disclosed relates to an artificial intelligence-based method of base calling. In particular, it relates to processing, through a spatial network of a neural network-based base caller, a first window of per-cycle analyte channel sets in for a first window of sequencing cycles of a sequencing run, and generating respective sequences of spatial output sets for respective sequencing cycles in the first window of sequencing cycles, processing, through a compression network of the neural network-based base caller, respective final spatial output sets in the respective sequences of spatial output sets, and generating respective compressed spatial output sets for the respective sequencing cycles in the first window of sequencing cycles, and generating, based on the respective compressed spatial output sets, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.Type: ApplicationFiled: February 18, 2021Publication date: August 26, 2021Applicant: Illumina, Inc.Inventors: Gery VESSERE, Gavin Derek PARNABY, Anindita DUTTA, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
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Publication number: 20210264266Abstract: The technology disclosed relates to a system that comprises a spatial convolution network and a bus network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets on a cycle-by-cycle basis by separately processing respective per-cycle sequencing image sets through respective spatial processing pipelines to generate respective per-cycle spatial feature map sets for respective sequencing cycles. The bus network is configured to form buses between spatial convolution layers within the respective spatial processing pipelines. The buses are configured to cause respective per-cycle spatial feature map sets generated by two or more spatial convolution layers in a particular sequence of spatial convolution layer for a particular sequencing cycle to combine into a combined per-cycle spatial feature map set, and provide the combined per-cycle spatial feature map set as input to another spatial convolution layer in the particular sequence of spatial convolution layer.Type: ApplicationFiled: February 19, 2021Publication date: August 26, 2021Applicant: Illumina, Inc.Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Gavin Derek PARNABY, Kishore JAGANATHAN, Amirali KIA
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Publication number: 20210265017Abstract: The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.Type: ApplicationFiled: February 19, 2021Publication date: August 26, 2021Applicant: Illumina, Inc.Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
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Publication number: 20210265018Abstract: The technology disclosed compresses a larger, teacher base caller into a smaller, student base caller. The student base caller has fewer processing modules and parameters than the teacher base caller. The teacher base caller is trained using hard labels (e.g., one-hot encodings). The trained teacher base caller is used to generate soft labels as output probabilities during the inference phase. The soft labels are used to train the student base caller.Type: ApplicationFiled: February 15, 2021Publication date: August 26, 2021Applicant: Illumina, Inc.Inventors: Anindita DUTTA, Gery VESSERE, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN, Amirali KIA
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Publication number: 20200327377Abstract: The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.Type: ApplicationFiled: March 20, 2020Publication date: October 15, 2020Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, John Randall GOBBEL, Amirali KIA
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Publication number: 20200302224Abstract: The technology disclosed processes a first input through a first neural network and produces a first output. The first input comprises first image data derived from images of analytes and their surrounding background captured by a sequencing system for a sequencing run. The technology disclosed processes the first output through a post-processor and produces metadata about the analytes and their surrounding background. The technology disclosed processes a second input through a second neural network and produces a second output. The second input comprises third image data derived by modifying second image data based on the metadata. The second image data is derived from the images of the analytes and their surrounding background. The second output identifies base calls for one or more of the analytes at one or more sequencing cycles of the sequencing run.Type: ApplicationFiled: March 21, 2020Publication date: September 24, 2020Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Anindita DUTTA, Dorna KASHEFHAGHIGHI, John Randall GOBBEL, Amirali KIA
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Publication number: 20200302297Abstract: The technology disclosed processes input data through a neural network and produces an alternative representation of the input data. The input data includes per-cycle image data for each of one or more sequencing cycles of a sequencing run. The per-cycle image data depicts intensity emissions of one or more analytes and their surrounding background captured at a respective sequencing cycle. The technology disclosed processes the alternative representation through an output layer and producing an output and base calls one or more of the analytes at one or more of the sequencing cycles based on the output.Type: ApplicationFiled: March 20, 2020Publication date: September 24, 2020Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, John Randall GOBBEL, Amirali KIA
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Publication number: 20190197401Abstract: 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.Type: ApplicationFiled: October 15, 2018Publication date: June 27, 2019Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
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Publication number: 20190114391Abstract: 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.Type: ApplicationFiled: October 15, 2018Publication date: April 18, 2019Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
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Publication number: 20190114547Abstract: 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.Type: ApplicationFiled: October 15, 2018Publication date: April 18, 2019Applicant: Illumina, Inc.Inventors: Kishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE