Abstract: Method(s), apparatus and system(s) are provided for entity type identification and/or disambiguation of entities within a corpus of text the method including: receiving one or more entity results, each entity result comprising data representative of an identified entity and a location of the identified entity within the corpus of text; identifying an entity type for each entity of the received entity results by inputting text associated with the location of said each entity in the corpus of text to a trained entity type (ET) model configured for predicting or extracting an entity type of said each entity from the corpus of text; and outputting data representative of the identified entity type of each entity in the received entity results.

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: Method(s) and apparatus are provided for generating a selection model based on a machine learning (ML) technique, the selection model for selecting a shortlist of compounds requiring validation with a particular property.

Abstract: Herein disclosed are a methods and systems of SAMMI—a machine learning-based workflow that uses human annotations as labels for training models—used to predict human-based annotations for drug discovery. SAMMI receives an input to a model trained using human-annotated data, wherein the human-annotated data comprises at least one annotation associated with a triage-progressability annotation of whether to progress the input for the drug discovery. SAMMI also receives a set of features. The set of features are associated with the input, the model, and the triage-progressability of the input. The set of features is applied to the model to predict whether the input is triage-progressible. A model output is provided based on the prediction.

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: A system for determining biological entities of interest is described. The system comprises a user input module configured to receive a search term comprising a representation of a biological entity; a search module configured to determine which biological entities of a set have a known association with the biological entity of the search term, those having a known association being results and those not having a known association being non-results, wherein biological entities of the set are related to each other by parent-child relationships in a relationship tree; and an analysis module configured to determine biological entities of interest by identifying non-results that have one or more results within a boundary in the relationship tree.

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: Methods, apparatus, system and computer-implemented method are provided for a computer-implemented method of automatically extracting entities associated with one or more domain(s) of interest from a corpus of text. A plurality of portions of text are received from the corpus of text, each portion of text comprising data representative of at least two entities and/or relationships thereto. For each received portion of text, identifying one or more subject-verb-object (SVO) entity data item(s) comprising data representative of at least two entities, a relationship associated with the at least two entities, a subject entity corresponding to an entity of said at least two entities, an object entity corresponding to an entity of the at least two entities, a verb portion associated with the relationship, and a direction of the relationship associated with the at least two entities.

Abstract: Methods, apparatus, system and computer-implemented method(s) are provided for creating a graph of entities of interest and relationships thereto. A search query is received corresponding to entities of interest. The search query including data representative of a first set of entities. An expanded search query is generated based on inputting the received search query to one or more entity expansion process(es) or engine(s). The expanded search query including data representative of a second set of entities and the first set of entities. Creating a graph of entities of interest and relationships thereto based on processing the expanded search query with data representative of a corpus of text. Creating the graph by processing the expanded search query to filter an existing graph of entities of interest and relationships thereto based on the expanded search query. The existing graph of entities of interest and relationships thereto is previously generated based on the corpus of text.

Abstract: A system for identifying a target for the treatment of a primary disease is provided. The system comprises: an input module configured to receive data for studying the primary disease, the data relating to individuals of a cohort; an encoder configured to use machine learning to encode the data as latent variables; an interpretation module configured to interpret the latent variables to stratify the individuals of the cohort into endotypes of the primary disease; and an identification module configured to identify a target that is associated with one of the endotypes.

Abstract: Embodiments of present disclosure provide a system, apparatus and method(s) for determining one or more target nodes and associated paths from a query of a graph structure. The method receives the query to the graph structure, where the query comprises a data representation of at least one query node. The method identifies one or more target nodes in response to the query based on a policy network, where the policy network is configured to determine the one or more target nodes in accordance with a latent policy distribution associated with the policy network. The method traverses the graph structure by a search in relation to the policy network, where the search is configured to navigate from the query node to the one or more identified target nodes to determine the associated paths. The method outputs a list of the one or more target nodes and the associated paths for the query, where the list are ranked in relation to the latent policy distribution.

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 a computer-implemented method of identifying candidate entities of interest associated with disease selection information.

Abstract: A computer-implemented method of prioritising biological targets is disclosed. The method comprises: receiving a selection of classes of one or more categories; and, for each of a plurality of biological targets, determining an extent of alignment of the biological target to each selected class. The method also comprises prioritising the biological targets based on the extents of alignment; and outputting a representation of one or more prioritised biological targets.

Abstract: A computer-implemented method of designing a molecule and determining a route to synthesise the molecule is provided. The method comprises: receiving one or more desired properties of the molecule; generating one or more candidate molecules using a first machine learning technique that uses the one or more desired properties of the molecule as an input; and for at least one candidate molecule, computing one or more routes to synthesise the candidate molecule using a second machine learning technique.

Abstract: A computer-implemented method of identifying a tool compound is provided. The method comprises: searching a database for first candidate compounds that each target one or more first target genes; generating a first fingerprint for each first candidate compound by: searching the database for genes associated with the first candidate compound, and predicting genes associated with the first candidate compound; and filtering the first candidate compounds using the first fingerprints to identify a first optimum compound for targeting the one or more first target genes.

Abstract: A computer-implemented method of electronically mining medical and scientific datasets to determine a ranking indicating a level of evidence for an association between two entities is disclosed. The method comprises receiving a representation of an entity pair, performing first data mining on one or more unstructured datasets to generate one or more first scores each representing an extent of association between the entities of the entity pair, and performing second data mining on one or more structured datasets to generate one or more second scores each representing an extent of association between the entities of the entity pair. The method also comprises using a classifier to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores, and providing the predicted ranking to a user as an indication of the strength of evidence for an association between the entities of the entity pair.

Abstract: Method(s), apparatus and system(s) are provided for entity type identification and/or disambiguation of entities within a corpus of text the method including: receiving one or more entity results, each entity result comprising data representative of an identified entity and a location of the identified entity within the corpus of text; identifying an entity type for each entity of the received entity results by inputting text associated with the location of said each entity in the corpus of text to a trained entity type (ET) model configured for predicting or extracting an entity type of said each entity from the corpus of text; and outputting data representative of the identified entity type of each entity in the received entity results.

Abstract: Systems, methods and apparatus are provided for identifying entities in a corpus of text. The system comprising: a first named entity recognition (NER) system comprising one or more entity dictionaries, the first NER system configured to identify entities and/or entity types within a corpus of text based on the one or more entity dictionaries, a second NER system comprising an NER model configured for predicting entities and/or entity types within the corpus of text; and a comparison module configured for identifying entities based on comparing the entity results output from the first and second NER systems, where the identified entities are different to the entities identified by the first NER system. The system may further include an updating module configured to update the one or more entity dictionaries based on the identified entities. The system may further include a dictionary building module configured to build a set of entity dictionaries based on at least the identified entities.

Abstract: A computer-implemented method of training a machine learning model to learn ligand binding similarities between protein binding sites is disclosed. The method comprises inputting to the machine learning model: a representation of a first binding site; a representation of a second binding site, wherein the representations of the first and second binding sites comprise structural information; and a label comprising an indication of ligand binding similarity between the first binding site and the second binding site. The method also comprises outputting from the machine model a similarity indicator based on the representations of the first and second binding sites; performing a comparison between the similarity indicator and the label; and updating the machine learning model based on the comparison.