Overview
Last updated
Last updated
The BioBox Platform is a knowledge management and engineering infrastructure. A core axiom and guiding principle is the belief that having data is not the same as having knowledge. The tools, processes, and philosophy of the platform provides scientists an ecosystem of purpose-built software that facilitates the transformation of data into knowledge.
At the core of the platform is a data graph. It's a knowledge graph-esque representation of biological quantitative data (-omics) that enables a semantic encoding of biological complexity.
The platform is divided into 3 layers: Ontology, Model, and Application.
The ontology is the single source of truth for what things mean and how they relate to each other inside your graph. A real world data item is an Object and is uniquely identifiable and idempotent. To make them useful in describing real world phenomenon, objects are assigned to one or more Concepts. Objects connect to each other through Relationships that are semantic descriptions of how concepts are associated.
The model layer is where users will build and assemble graph models that compute associations between objects from two Concepts using the connections found within the knowledge graph. These designed to capture the scientific criterion and considerations at an expert-level. These models can be applied to a variety of associations that are useful across multiple problem domains. For example, a graph model is built against Gene <--> Disease connections for target prioritization.
The ontology, data graph, and graph models are valuable resources, but on their own, they don't directly influence decision-making. By integrating them into specific configurations, we can harness their power to answer scientific questions. The application layer represents an ecosystem of ontology-aware tools that operate on top of your data graph and models that help users:
Automatically Generate Ranked Reports
Run Advanced Graph Algorithms
Perform Exploratory Data Analysis
