Abstract: An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback.

Abstract: A method is provided for utilizing multi-omic data indices for tumor profiling. The method can comprise storing a plurality of multi-omic data indices, wherein each of the plurality of multi-omic data indices comprises cancer-specific tokenized data; ingesting additional multi-omic data and any annotations associated with the additional multi-omic data, the additional multi-omic data related to one or more indices; indexing the ingested additional multi-omic data and annotations while preserving gene names, gene variant names and multi-omic mapping between different data streams for the same patient in the specific index, to produce tokenized ingested additional multi-omic data; receiving a user query; selecting one or more relevant multi-omic data indices based on the user query; ranking the selected one or more multi-omic data indices based on at least one of clinical actionability, pathogenicity, feature weight, or frequency; and returning the ranked one or more multi-omic data indices to the user.

Abstract: Described are methods and systems for identifying phenotypic traits of an individual from nucleotide sequence data. The methods and systems are useful even when the identity of the individual or phenotypic traits of the individual is unknown.