Multi-Agent Bioinformatics System for Single Cell Data Analytics

A multi-agent system for transforming single call data into one or more insights about one or more phenotypes exhibited by the single cell data, wherein the single cell data comprises data indicative of protein expression levels for a plurality of different protein markers in a plurality of different single cells with respect to a plurality of subjects, and wherein each subject is associated with status data for the defined outcome, the system comprising:

• one or more processors;
• and
• one or more memories configured to store a computer-readable data structure;
• wherein the computer-readable data structure defines a network model of related phenotypes, the network model comprising a plurality of connected nodes, each node corresponding to a different phenotype from among a plurality of phenotypes derived from the single cell data and being associated with (1) relationship data for its corresponding phenotype to the defined outcome and (2) metadata about one or more medical papers that reference the corresponding phenotype;
• wherein the one or more processors, in cooperation with the one or more memories, are configured to execute one or more first agents, one or more second agents, and one or more third agents;
• wherein the one or more first agents are configured to build a portion of the computer-readable data structure through transformation of the single cell data;
• wherein the one or more second agents are configured to (1) mine a corpus of the medical papers using natural language processing (NLP) to determine which of the medical papers qualify as phenotype-related medical papers that reference phenotypes and (2) link the phenotype-related medical papers with nodes of the network model by augmenting the nodes with associations to metadata about the phenotype-related medical papers based on correspondence between the nodes' corresponding phenotypes and the phenotypes referenced by the phenotype-related medical papers;
• wherein the one or more third agents are configured to access the computer-readable data structure to synthesize an insight about the single cell data with respect to one or more phenotypes of interest, wherein the insight is derived from medical paper metadata associated by the computer-readable data structure with the one or more phenotypes of interest;
• and
• wherein the one or more first, second, and third agents are configured for operating in parallel with each other.

An article of manufacture comprising:

• processor-executable code resident on a non-transitory computer-readable storage medium, wherein the processor-executable code is configured for execution by one or more processors to cause the one or more processors to:
• traverse a network model of related phenotypes that is derived from single cell data to identify one or more phenotypes of interest, wherein the network model comprises a plurality of nodes that are connected according to a network structure, wherein the nodes correspond to different phenotypes derived from the single cell data and are associated with relationship data between the nodes' corresponding phenotypes and a defined outcome according to the single cell data, wherein the phenotypes are defined by protein marker combinations of one or more protein markers from the single cell data, wherein the network structure defines connections within the network model between nodes where the nodes' corresponding phenotypes differ by the addition or removal of 1 protein marker, and wherein the one or more phenotypes of interest are identified based on (1) the network structure and (2) the relationship data;
• determine medical literature metadata that is linked to the identified one or more phenotypes of interest and has been derived from a plurality of phenotype-related medical papers;
• and
• synthesize an insight about the single cell data with respect to the identified one or more phenotypes of interest based on the determined medical literature metadata.