# US 2019/0244680 A1 · Systems and methods for generative machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0244680 A1 - Document ID: US20190244680A1 - Status: Published application - Kind: A1 - Application number: 16/270,273 - Filed: 2019-02-07 - Published: 2019-08-08 - Earliest claimed: 2018-02-07 - Assignees: D-Wave Systems Inc. - Applicants: D-Wave Systems Inc. - Inventors: Jason Tyler Rolfe; Karsten J. Kreis - Web page: https://patentgrep.com/patent/US20190244680A1 ## CPC - G16B 40/20: Supervised data analysis - G16B 40/30: Unsupervised data analysis - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Machine-learning (ML) techniques and systems are advantageously employed in areas such as psychiatric genetics, and analysis of gene expression. Generative ML may be performed over large datasets to infer new pleiotropic effects of genetic variants in multiple neuropsychiatric diseases. Deep ML may infer information about psychiatric genetics. ML may realize an efficient estimation of multi-disease genetic and environmental correlation matrices. ML may obtaining protein expression data and generating a mapping between the protein expression data and at least one disease. ML may be performed on a data that that jointly models multiple diseases. Quantum processing can be advantageously employed in ML scenarios. ## Claims ### Claim 1 A method for machine learning over an input space comprising a plurality of input variables relating to a plurality of organisms, and at least a subset of a training dataset of samples of the respective variables, to attempt to identify the value of at least one parameter that increases the log-likelihood of the at least a subset of a training dataset with respect to a model, the model expressible as a function of the at least one parameter, the method executed by circuitry including at least one processor and comprising; forming a latent space comprising a genetic latent subspace and an environmental subspace, each subspace comprising one or more continuous random latent variables; forming an approximating posterior distribution over the latent space, conditioned on the input space; forming a prior distribution over the latent space; forming a decoding distribution over the input space, conditioned on the latent space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms; and training the model based on the encoding, prior, and decoding distributions. ### Claim 2 (depends on 1) The method of claim 1 wherein forming a decoding distribution over the input space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms comprises conditioning a latent variable corresponding to an organism on a first parental latent variable corresponding to a first parent of the organism and a second parental latent variable corresponding to a second parent of the organism. ### Claim 3 (depends on 2) The method of claim 2 wherein forming a decoding distribution over the input space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms further comprises conditioning the latent variable corresponding to the organism on a noise latent variable, the noise latent variable independent of the first and second parental latent variables. ### Claim 4 (depends on 1) The method of claim 1 wherein forming a decoding distribution over the input space, conditioned on the latent space comprises conditioning the decoding distribution on the random latent variables of the environmental latent subspace based on shared environmental characteristics of a subset of the plurality of organisms. ### Claim 5 (depends on 4) The method of claim 4 wherein conditioning the decoding distribution on the random latent variables of the environmental latent subspace based on shared environmental characteristics of a subset of the plurality of organisms comprises, for a first subset of family latent variables in the environmental latent subspace and a second subset of sibling latent variables, conditioning the decoding distribution on the family latent variables and sibling latent variables when generating a representation of sibling organisms and conditioning on the family latent variables exclusive of the sibling latent variables when generating a representation of a parent organism of the sibling organisms. ### Claim 6 (depends on 1) The method of claim 1 wherein training the model comprises training the model based on genetic sequence data for one or more of the plurality of organisms. ### Claim 7 (depends on 1) The method of claim 1 wherein the input space comprises diagnoses for a plurality of diseases for the plurality of organisms and training the model comprises generating one or more generated organisms each having a prediction for each of the plurality of diseases. ### Claim 8 (depends on 1) The method of claim 1 comprises predicting diagnoses for one or more of the plurality of diseases for a given organism by determining a latent representation of the given organism based on the approximating posterior distribution, sampling one or more times from the prior distribution based on the latent representation to obtain one or more samples, generating the one or more generated organisms based on the one or more samples, and determining a prediction for each disease based on the one or more generated organisms and the decoding distribution. ### Claim 9 (depends on 8) The method of claim 8 wherein forming a decoding distribution over the input space comprises conditioning the decoding distribution based on age information of the plurality of organisms and determining a prediction for each disease based on the decoding distribution comprises conditioning the decoding distribution based on an age of the given organism. ### Claim 10 A machine-learning system, comprising: at least one processor; at least one nontransitory processor-readable medium communicatively coupled to the at least one processor, the at least one nontransitory processor-readable medium which stores at least one of processor-executable instructions or data which, when executed by the at least one processor, cause the at least one processor to attempt to identify the value of at least one parameter that increases the log-likelihood of the at least a subset of a training dataset with respect to a model, and particularly cause the processor to: form a latent space comprising a genetic latent subspace and an environmental subspace, each subspace comprising one or more continuous random latent variables; form an approximating posterior distribution over the latent space, conditioned on the input space; form a prior distribution over the latent space; form a decoding distribution over the input space, conditioned on the latent space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms; and train the model based on the encoding, prior, and decoding distributions. ### Claim 11 (depends on 10) The system of claim 10 wherein the at least one of processor-executable instructions or data which cause the at least one processor to form a decoding distribution over the input space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms comprises at least one of processor-executable instructions or data which cause the at least one processor to condition a latent variable corresponding to an organism on a first parental latent variable corresponding to a first parent of the organism and a second parental latent variable corresponding to a second parent of the organism. ### Claim 12 (depends on 11) The system of claim 11 wherein the at least one of processor-executable instructions or data which cause the at least one processor to form a decoding distribution over the input space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms further comprises at least one of processor-executable instructions or data which cause the at least one processor to condition the latent variable corresponding to the organism on a noise latent variable, the noise latent variable independent of the first and second parental latent variables. ### Claim 13 (depends on 10) The system of claim 10 wherein the at least one of processor-executable instructions or data which cause the at least one processor to form a decoding distribution over the input space, conditioned on the latent space comprises at least one of processor-executable instructions or data which cause the at least one processor to condition the decoding distribution on the random latent variables of the environmental latent subspace based on shared environmental characteristics of a subset of the plurality of organisms. ### Claim 14 (depends on 13) The system of claim 13 wherein the at least one of processor-executable instructions or data which cause the at least one processor to condition the decoding distribution on the random latent variables of the environmental latent subspace based on shared environmental characteristics of a subset of the plurality of organisms comprises at least one of processor-executable instructions or data which cause the at least one processor to, for a first subset of family latent variables in the environmental latent subspace and a second subset of sibling latent variables, condition the decoding distribution on the family latent variables and sibling latent variables when generating a representation of sibling organisms and conditioning on the family latent variables exclusive of the sibling latent variables when generating a representation of a parent organism of the sibling organisms. ### Claim 15 (depends on 10) The system of claim 10 wherein the at least one of processor-executable instructions or data which cause the at least one processor to train the model comprises at least one of processor-executable instructions or data which cause the at least one processor to train the model based on genetic sequence data for one or more of the plurality of organisms. ### Claim 16 (depends on 10) The system of claim 10 wherein the input space comprises diagnoses for a plurality of diseases for the plurality of organisms and the at least one of processor-executable instructions or data which cause the at least one processor to train the model comprises at least one of processor-executable instructions or data which cause the at least one processor to generate one or more generated organisms each having a prediction for each of the plurality of diseases. ### Claim 17 (depends on 10) The system of claim 10 comprises at least one of processor-executable instructions or data stored by the medium which cause the at least one processor to predict diagnoses for one or more of the plurality of diseases for a given organism by determining a latent representation of the given organism based on the approximating posterior distribution, sampling one or more times from the prior distribution based on the latent representation to obtain one or more samples, generating the one or more generated organisms based on the one or more samples, and determining a prediction for each disease based on the one or more generated organisms and the decoding distribution. ### Claim 18 (depends on 17) The system of claim 17 wherein at least one of processor-executable instructions or data which cause the at least one processor to form a decoding distribution over the input space comprises at least one of processor-executable instructions or data which cause the at least one processor to condition the decoding distribution based on age information of the plurality of organisms and determining a prediction for each disease based on the decoding distribution comprises conditioning the decoding distribution based on an age of the given organism. ### Claim 19 (depends on 10) The system of claim 10, further comprising: at least one interface communicatively coupled to receive information from at least one quantum computing system that includes at least one quantum processor. ### Claim 20 (depends on 10) The system of claim 10, further comprising: at least one quantum computing system that includes at least one quantum processor.