Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a graph reference genome customized for a particular sample genome and utilizing the customized graph reference genome to determine final nucleotide-base calls for the sample genome. To illustrate, the disclosed systems can generate a customized graph reference genome including various paths representing imputed haplotypes corresponding to a particular genomic region. Additionally, or alternatively, the disclosed system can determine and compare direct and imputed nucleotide-base calls for a sample genome as a basis for generating final nucleotide-base calls. In some such cases, the disclosed system weights (and selects between) direct nucleotide-base calls and imputed nucleotide-base calls for genomic coordinates based on sequencing metrics corresponding to the direct nucleotide-base calls or based on the variability of the genomic regions comprising the genomic coordinates.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a graph reference genome customized for a particular sample genome and utilizing the customized graph reference genome to determine final nucleotide-base calls for the sample genome. To illustrate, the disclosed systems can generate a customized graph reference genome including various paths representing imputed haplotypes corresponding to a particular genomic region. Additionally, or alternatively, the disclosed system can determine and compare direct and imputed nucleotide-base calls for a sample genome as a basis for generating final nucleotide-base calls. In some such cases, the disclosed system weights (and selects between) direct nucleotide-base calls and imputed nucleotide-base calls for genomic coordinates based on sequencing metrics corresponding to the direct nucleotide-base calls or based on the variability of the genomic regions comprising the genomic coordinates.

Disclosed herein include systems, devices, and methods for kinetic learning, which can include, for example, training and/or using a machine learning model, such as training a machine learning model and using the machine learning model to simulate a virtual strain of an organism or to determine possible modifications of an organism.

Disclosed herein include systems, devices, and methods for kinetic learning, which can include, for example, training and/or using a machine learning model, such as training a machine learning model and using the machine learning model to simulate a virtual strain of an organism or to determine possible modifications of an organism.

The present disclosure is directed to in silico techniques for identifying of genomic variants, and more specifically to iterative graph-based techniques for identifying genomic variants. An exemplary electronic device comprises one or more processors; a memory: and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for constructing a first graph representation of at least a portion of a reference sequence; constructing a second graph representation based on sequence reads associated with an individual and the first graph representation, identifying one or more candidate variants based on the second graph representation; and repeating the process by the one or more processors in accordance with a determination that termination conditions are not met.

The present disclosure is directed to in silico techniques for identifying of genomic variants, and more specifically to iterative graph-based techniques for identifying genomic variants. An exemplary electronic device comprises one or more processors; a memory: and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for constructing a first graph representation of at least a portion of a reference sequence; constructing a second graph representation based on sequence reads associated with an individual and the first graph representation, identifying one or more candidate variants based on the second graph representation; and repeating the process by the one or more processors in accordance with a determination that termination conditions are not met.

The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.

The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.