Abstract: In one example, a method of extracting patient information for a medical application comprises: receiving patient data of a patient; processing the patient data using a learning system with Artificial Intelligence (AI)-assisted clinical extraction tool, the processing comprising: extracting, based on a trained language extraction model that reflects language semantics and a user's prior habit of entering other patient data, data elements from the patient data and data categories represented by the data elements, and mapping at least some of the extracted data elements to pre-determined data representations based on the data categories; populating fields of a data record of the patient based on the pre-determined data representations; and storing the populated data record in a database accessible by the medical application.

Abstract: The browsable database can allow for high-throughput analysis of protein sequences. One helpful feature may be a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators may have associated the ontology terms with Hidden Markov Models (HMMs), rather than individual sequences, so that they can be applied to additional sequences. To ensure accurate functional classification, HMMs may be constructed not only for families, but for curator-defined subfamilies, whenever family members have divergent functions or nomenclature. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, can be available for each family. Various versions of the browsable database may include training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs can be used to classify gene products across the entire genomes of human, and Drosophila melanogaster.