Welcome to BioBox!

Knowledge Infrastructure to Fuel Discovery

What is the BioBox Platform?

The BioBox Platform a vertically integrated platform, from frontend to backend, that provides infrastructure for knowledge management and engineering in biotechnology and pharmaceutical research. It empowers organizations to effectively transform and integrate proprietary and public knowledge into a singular resource that can be leveraged to improve the quality and speed of decision making.

BioBox graphs are radically different from traditional knowledge graphs. Each graph is designed with a custom ontology unique to your scientific needs and populated using your own proprietary data, not just public data. The graph is assembled modularly using a collection of data adapters and connectors that make data flow. Instead of generic entities and relationships that are shallow, qualitative encoding of text information, BioBox graphs are built using the quantitative data directly in the graph. Relationships can be weighted, positive or negative values, and represented in a wholly unbiased way. This unlocks the ability to quantify uncertainty and help users reason about data.

The BioBox platform operationalizes knowledge graphs with an ecosystem of ontology-aware tools that makes them useful for solving complex problems, with functionality for all users in your organization from business units, data teams, to therapeutics areas.

Who is it for?

I have a knowledge graph, now what?

If you've asked yourself this question, then the BioBox platform is for you. Knowledge graphs are just resources, that alone, have no meaningful impact on decision making. The difference is going to come down to who is better at interacting with it.

For those that haven't worked with knowledge graphs before, the BioBox platform is for you if you need to:

• Integrate multi-omic and multi-modal data

• Build and scale ontologies

• Quantify uncertainty from data

• Augment decision-making with expert-tuned graph inference models

Use Cases

BioBox has been deployed to tackle complex challenges such as:

• Target Prioritization

• Biomarker Discovery

• Antibody Manufacturing

• Process Optimization

• Indication Selection

• Mechanism of Action Analysis

• Safety & Efficacy Testing

Getting Started

The best way to get started is to get in touch with a BioBox team member for a personalized demo. To learn more about the platform first, check out the rest of our docs, starting with the FRAMEWORK section.

Reading Guide

The documentation is organized into the following sections:

1. Framework Background information, assumptions, and key concepts central to the BioBox method.

2. Data Packages Details around common data formats, transformations, and patterns used to generate graph data packages.

3. How To Short step-by-step guides to accomplish specific objectives using the platform.

Need Help?

In case you do not find the information you need in these docs, please contact us via email at [e-mail]