Identifying a target, designing an asset and submitting it for IND is a 5-6 multi-million dollar process that involves many high impact data driven decisions. We provide teams with a platform identify complex patterns across multi-modal data to make these decisions efficiently and with confidence
Experience a fully integrated data intelligence platform designed for exploring biology.
Put the power of your data moat in the hands of your scientists by allowing them to explore it interactively. The BioBox Graph Explorer is a ontology-aware way to navigate the rich connectivity and context of your data graph.
Put your graph models to work and generate comprehensive data reports. For example, prioritize a list of drug targets for an indication in seconds. One-click data snapshot and sharing to foster collaboration and improve data driven decision making.
Specialized layers to transform data into insight
Construct an ontology to capture the semantic concepts, relationships, and properties needed to model scientific information in your own way.
Formalize your scientific reasoning into traceable graph models to transparently and systematic determine biologically important findings.
Put the data graph and the graph models to work and rapidly generate scored reports in seconds. Innovate faster by interactively exploring your data graph for hidden connections.
Share a compelling narrative with your team. Leverage collaborative, interactive and customizable multi-omic dashboards to explore large cohorts of data.
Tools to wrangle, process, and analyze multi-omics data, including Bulk RNAseq, scRNAseq, WGS, WES, ChIP-seq and more.
Developers
REST API to connect the BioBox platform to internal tools.
Public Data
Instantly access over 85,000 analysis ready publicly available sequencing datasets from TCGA, GEO, SRA, and more.
Ready to build your graph?
Learn more about how modern biotechs are deploying BioBox to hit R&D milestones and make mission-critical decisions with confidence.
Identify and prioritize disease specific targets. Obtain a comprehensive understanding of the biological underpinnings of a disease. Compare and contrast targets identified in the public domain and in your proprietary data.
Find compelling evidence across public and private data to support the efficacy of a target. Identify target and disease relationships across multi-omic observations. Share findings and collaborate with team members.
Find additional indications for your therapeutic asset to expand its value. Use the BioBox Knowledge Engine to create in-silico models and uncover hidden connections between drug and diseases.
