Abstract: Methods, apparatus, system and computer-implemented method are provided for embedding a portion of text describing one or more entities of interest and a relationship. The portion of text describes a relationship for the one or more entity(ies) of interest, where the portion of text includes multiple separable entities describing the relationship and the entity(ies). The multiple separable entities including the one or more entity(ies) of interest and one or more relationship entity(ies). A set of embeddings for each of the separable entities is generated, where the set of embeddings for a separable entity includes an embedding for the separable entity and an embedding for at least one entity associated with the separable entity. One or more composite embeddings may be formed based on at least one embedding from each of the sets of embeddings. The composite embedding(s) may be sent for input to a machine learning model or classifier.

Abstract: Methods and apparatus are provided for generating an embedding of a graph. The graph includes a plurality of nodes and each node includes a connection to another one or more of the nodes. The method including and/or apparatus configured to: receiving data representative of at least a portion of the graph; transforming the nodes of the graph into a non-Euclidean geometry; iteratively updating an embedding model based the transformed nodes in the non-Euclidean geometry based on a causal loss function and a link prediction function associated with the non-Euclidean geometry.

Abstract: A computer-implemented method of stratifying a population of patients into disease endotypes is provided. The method comprises: encoding data relating to the patients as latent variables; determining one or more importance measures of the latent variables; prioritising the latent variables using the importance measures; interpreting one or more of the ranked latent variables; and identifying a disease endotype that is represented by one or more of the interpreted latent variables.

Abstract: A computer-implemented method and a system of selecting a cell line for an assay. The computer-implemented method and system encode data, which is comprised of one or more features, as one or more latent variables. The one or more features encoded in the one or more latent variables are identified and mapped to cell lines based on the one or more features. A relevance of one or more targets to each of one or more of the one or more latent variables is determined and the one or more targets to the cell lines are matched via the one or more latent variables.

Abstract: Methods, apparatus, system and computer-implemented method are provided for embedding a portion of text describing one or more entities of interest and a relationship. The portion of text describes a relationship for the one or more entity(ies) of interest, where the portion of text includes multiple separable entities describing the relationship and the entity(ies). The multiple separable entities including the one or more entity(ies) of interest and one or more relationship entity(ies). A set of embeddings for each of the separable entities is generated, where the set of embeddings for a separable entity includes an embedding for the separable entity and an embedding for at least one entity associated with the separable entity. One or more composite embeddings may be formed based on at least one embedding from each of the sets of embeddings. The composite embedding(s) may be sent for input to a machine learning model or classifier.

Abstract: Method(s), apparatus, and system(s) are provided for filtering a set of data, the set of data comprising multiple data instances by: receiving a set of scores for the set of data; determining attention filtering information based on prior knowledge of one or more relationships between the data instances in said set of data and calculating attention relevancy weights corresponding to the data instances and the set of scores; and providing the attention filtering information to a machine learning, ML, technique or ML model.

Abstract: Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model.

Abstract: Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model.

Abstract: Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model.

Abstract: Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model.

Abstract: Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model.