
Bio Co-Scientist
Researchers' pain points
Meet your co-scientist
Answers backed by a knowledge graph (HyperKG) that connects bio knowledge
It finds relationships among genes, proteins, pathways, diseases, and more in HITS' HyperKG and uses them as evidence for its answers.

200+ data sources, connected
A source for every relationship
A graph viewer you can explore
Research outputs
02 · Execute
Design, prediction, and analysis, run right in the conversation
Even tasks that need GPUs can run on the web, without setting up your own compute environment.
03 · Orchestrate
AI that picks the right approach for your research
04 · Deliver
Reports and slides, ready to share
Use Case in HyperLab
Use cases
Visium and scRNAseq cell type annotation-TAMs, CAFs
Cell type annotation on 10x single-cell RNA sequencing (scRNA-seq) data and Visium spatial transcriptomics data (GLMF1, GLMF2).
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TP53 knockout protocol design in HEK293T
An end-to-end experimental protocol for TP53 knockout in HEK293T cells, developed through an iterative, decision-driven workflow rather than a single-pass response.
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KG-guided clinical trial design for lazertinib + amivantamab in EGFR-mutant NSCLC
Designing a lazertinib + amivantamab trial for EGFR-mutant NSCLC, with patient selection backed by knowledge-graph evidence.
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DEG and KG usage for analyzing ARID1A mutation effect in breast cancer
ARID1A knockout RNA-seq in MCF7 breast cancer cells, with each finding checked against knowledge-graph evidence.
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How is this different from general-purpose AI?
General-purpose AI tools like ChatGPT can also analyze and write documents. Bio Co-Scientist bases its answers on HyperKG, the biological knowledge graph built by HITS, and runs specialized bio tools that need GPUs, such as structure prediction, in the same conversation.
That means you can go from checking evidence to running computations and organizing results, and on to your next research step, all in one conversation instead of switching between programs.







