Abstract: A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences includes identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile. The immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile. A plurality of immune profiles are retrieved for a population. A plurality of representative immune profiles are generated for the population. The representative immune profiles overlap with the sample components of the immune profiles. The one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values, are selected.

Abstract: A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences includes identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile. The immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile. A plurality of immune profiles are retrieved for a population. A plurality of representative immune profiles are generated for the population. The representative immune profiles overlap with the sample components of the immune profiles. The one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values, are selected.

Abstract: A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences includes identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile. The immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile. A plurality of immune profiles are retrieved for a population. A plurality of representative immune profiles are generated for the population. The representative immune profiles overlap with the sample components of the immune profiles. The one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values, are selected.

Abstract: A computer-implemented method for predicting binding and presentation of peptides by MHC molecules includes collecting training data, wherein the training data includes a set of MHC molecules in a sample as well as a set of observed peptide sequences that are presented by the MHC molecules, wherein it is unknown to which specific MHC molecules a peptide sequence is bound, and wherein the training data is organized in bags with each bag having a set of training instances. Labels are known for the bags, but unknown for the training instances. The method also uses a loss function to train a classifier at an instance-level, and predicts the label of new instances by applying the classifier directly and/or predicts the label of new bags by applying the MIL classifier to each instance of a respective bag and aggregates the results among all instances of the respective bag.

Abstract: A method for quantifying cellular activity from high throughput sequencing data including generating a multimodal knowledge graph by combining a gene regulatory network (GRN) with gene annotations from domain knowledge, wherein nodes of the multimodal knowledge graph are genes and wherein the gene annotations enrich the relations among the genes. A number of gene modules (GMs) are created by clustering embeddings of the genes of the GRN and embeds samples of sequencing data into the multimodal knowledge graph. For each sample of the sequencing data, an activation vector is generated in which the respective sample is expressed as distances between the embedding and centroids of each of the number of GMs.

Abstract: A computer-implemented method for learning novel relationships among various entities, including biological entities such as chemicals, proteins, and diseases, includes establishing a knowledge graph wherein each of the entities is represented as a node and each relationship between the entities is represented as an edge between the respective nodes, and annotating entities in the knowledge graph with objects of one or more data modalities. A neural network system is trained with the knowledge graph, wherein the neural network system treats the knowledge graph and the objects of a respective one of the data modalities in a unified manner by jointly learning embeddings of the nodes from the knowledge graph and embeddings of the objects of the respective one of the data modalities. The learned embeddings are used for identifying novel relationships among the entities.

Abstract: A computer-implemented method of selecting one or more amino acid sequences for inclusion in a vaccine from a set of predicted immunogenic candidate amino acid sequences includes identifying an immune profile response value for each candidate amino acid sequence with respect to each one of a plurality of sample components of an immune profile. The immune profile response value represents whether the respective candidate amino acid sequence results in an immune response for the sample components of the immune profile. A plurality of immune profiles are retrieved for a population. A plurality of representative immune profiles are generated for the population. The representative immune profiles overlap with the sample components of the immune profiles. The one or more amino acid sequences for inclusion in the vaccine that minimises a likelihood of no immune response for each representative immune profile, based on the immune profile response values, are selected.

Abstract: A method for predicting a patient outcome from a caretaker episode includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph. The first and second embeddings are combined to obtain a complete embedding. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots.

Abstract: A method of ranking epitopes derived from neoantigens as targets for personalized immunotherapy includes collecting candidate epitopes based on patient data of a cancer patient. A set of scores are calculated for each of the candidate epitopes, each of the scores in a respective one of the sets for a respective one of the candidate epitopes representing an independent measure of a likelihood of the respective one of candidate epitopes to elicit an immune response in the cancer patient. The scores in each of the sets of scores are combined into a single score for each of the candidate epitopes. The single scores for the candidate epitopes in each case reflect an overall likelihood of eliciting the immune response in the patient. The candidate epitopes are ranked using the single scores for the immunotherapy.

Abstract: A method for hyperparameter selection (HPS) and algorithm selection (AS) for mixed integer linear programming (MILP) problems includes collecting MILP problems and performances of associated solvers for optimizing the MILP problems. Each of the MILP problems is mapped into a graph having nodes each comprising one of the variables and constraints of the MILP problems. Raw features of the nodes of the graphs are generated. For each of the graphs, a representation of the nodes of the graphs is learned using the raw features which is global to the MILP problems using the raw features. A machine learning model is trained using the learned representations. The trained learning model is used to select one of the solvers for a new MILP problem.

Abstract: A method for predicting a patient outcome from a caretaker episode includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph. The first and second embeddings are combined to obtain a complete embedding. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots.

Abstract: An appointment scheduling device for scheduling an appointment for a patient to visit a health provider includes an embedder, a predictor, and a scheduler. The embedder receives input data about the patient. The input data is associated with a request to schedule the appointment with the health provider. The embedder generates an embedding based on the input data. The predictor receives the embedding and predicts an appointment parameter based on the embedding. The scheduler schedules the appointment based on the appointment parameter.