AI service — AI Medical · Genomics
Single-cell analysis
Annotate cell types, integrate batches and predict perturbation response from single-cell expression data.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Single-cell datasets are large, batchy and expensive to label. Foundation models over expression profiles produce cell and gene embeddings that make annotation, batch integration and in-silico perturbation a downstream step rather than a project each.
Expression matrices arrive from your pipeline and come back as embeddings, annotations or perturbation predictions. Throughput here is measured in cells per hour, and the numbers are large enough that a full atlas is an overnight job.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the single-cell analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Cell-type annotation — Label cell types across a dataset consistently rather than by marker gene.
- Batch integration — Combine datasets from different runs or sites into one embedding space.
- Perturbation prediction — Estimate response to a perturbation before running the experiment.
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