A device to support work of searching document data for interpreting an information analysis result of analysis data obtained by analyzing a sample containing an analyte, includes: an acquisition unit to acquire first information for identifying the analyte from the analysis data; a reception unit to receive input of second information for searching data of a document for interpreting the information analysis result of the analysis data; an extraction unit to extract, based on the first and second information, terms relevant to the information analysis result, from among terms in data of documents in a database; a calculation unit to calculate, for each relevant term, relevance scores indicating a relevance degree between the relevant term and the first information, and a relevance degree between the relevant term and the second information; and a processing unit to obtain an index value of statistical likelihood from the relevance scores.

Disclosed herein relates to processes that identify segments of a target dataset that match segments of other datasets in a database. A computing server may encode the target dataset to generate a pair of encoded target bitmap sequences based on an encoding scheme. The encoding scheme defines encoding values based on homogeneity between the pair of data value sequences. The computing server may compare the pair of encoded target bitmap sequences with other pairs of encoded bitmap sequences to identify homogeneous mismatched locations. A homogeneous mismatched location may be a location where the target dataset and the other dataset in comparison are both homogeneous but have different types of homogeneity at the location. The computing server may identify a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified. The matched segment is contained within two homogeneous mismatched locations.

Disclosed herein relates to processes that identify segments of a target dataset that match segments of other datasets in a database. A computing server may encode the target dataset to generate a pair of encoded target bitmap sequences based on an encoding scheme. The encoding scheme defines encoding values based on homogeneity between the pair of data value sequences. The computing server may compare the pair of encoded target bitmap sequences with other pairs of encoded bitmap sequences to identify homogeneous mismatched locations. A homogeneous mismatched location may be a location where the target dataset and the other dataset in comparison are both homogeneous but have different types of homogeneity at the location. The computing server may identify a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified. The matched segment is contained within two homogeneous mismatched locations.

A method of forming a neural network includes specifying layers of neural network neurons. A parameter genome is defined with numerical parameters characterizing connections between neural network neurons in the layers of neural network neurons, where the connections are defined from a neuron in a current layer to neurons in a set of adjacent layers, and where the parameter genome has a unique representation characterized by kilobytes of numerical parameters. Parameter genomes are combined into a connectome characterizing all connections between all neural network neurons in the connectome, where the connectome has in excess of millions of neural network neurons and billions of connections between the neural network neurons.

one embodiment of a memory stores information, including address bits, on DNA strands and provides access using a pipeline of tubes, where each tube selectively transfers half of the strands to the next tube based on probing of associated address bits. Transfers are controlled by logic relating to the state of the tubes: The pipeline may be initialized to start at a high-order target address, providing random access without enzymes, synthesizing probe molecules or PCR at access time. Thereafter, a processing unit gets fast access to sequentially addressed strands each cycle, for applications like executing machine language instructions or reading blocks of data from a file. Another embodiment with a compare unit allows low-order random access. Provided that addresses are encoded using single-stranded regions of DNA where probe molecules may hybridize, other information may use any DNA encoding. Electronic/electrochemical (electrowetting, nanopore, etc.) embodiments as well as biochemical embodiments are possible.

A system and method for region-based calling utilizes a probability distribution of a phi-transformed logarithmic ratio to determine a set of possible transition paths through markers and marker states, constructs a local evidence matrix for each of the markers and generates a total per-marker value for each segment in a discrete region.

A system and method for region-based calling utilizes a probability distribution of a phi-transformed logarithmic ratio to determine a set of possible transition paths through markers and marker states, constructs a local evidence matrix for each of the markers and generates a total per-marker value for each segment in a discrete region.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

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.