01
Define the task
Define the research question, labels, experimental context and evaluation objective in a task brief and annotation protocol.
LUMESAGE / BIOLOGICAL AI
Built on an expert team
Turn life-science questions into verifiable data tasks.
For life-science research: expert involvement in task definition, annotation protocols, data review and evaluation design. Expert fit, data sources and deliverables are confirmed for each project.
Discuss your project →02
Curate and review
Scope cleaning, annotation, expert review, disagreement handling and version records for client data or verified sources.
03
Validate and iterate
Agree on independent splits and scoring rules; document failure types and data gaps to inform the next iteration.
Omics, cell and microscopy images, proteins and molecules, and experimental data are project directions for discussion—not claims of available datasets or completed work. Delivery follows confirmation of expert fit, permissions, samples and acceptance criteria.
Scope data around the research task
Omics and cell states
Discuss sample stratification, batch effects and cell-type or state labels; design independent validation by donor or experimental batch.
Cell and microscopy images
Discuss imaging conditions, instance boundaries, phenotype labels and reviewer agreement; retain acquisition context and review records.
Proteins, molecules and experiments
Discuss sequence or structure provenance, assay conditions, activity units and negative results; distinguish predictions from experimental measurements.
Research context
These studies provide task context; they do not imply Langhui participation, partnership or ownership of the data.
AlphaFold 3 · Nature 2024AlphaGenome · Nature 2026Preserve the scientific context
Capture provenance and permissions, species and samples, experimental methods and conditions, controls and batches, label evidence and uncertainty, versions and splits. Distinguish model predictions from experimental observations.
Delivery is defined by the task, permissions, quality and acceptance criteria.
Discuss your project →How experts contribute
Match the expertise
Confirm the research question, disciplines and expert responsibilities. Verify individual qualifications, relevant experience, availability and confidentiality arrangements during scoping.
Define review responsibilities
Agree who owns the task, annotates, reviews and resolves disagreements. Independent double review or blinded review is selected to fit the task and risk.
Document acceptance
Agree on protocol versions, review records, issue logs and an acceptance report. Specify expert involvement and review coverage in the project scope.
Confirmed foundation: an expert team. Individual disciplines, qualifications and publishable examples are verified per project. Unconfirmed titles or institutional affiliations are not used as endorsements.
Explore expert involvement and verification →Start with an assessable pilot
01 · Scope the task
Define the research question, existing data, intended use and expert support needed. Prepare a task brief and field list.
02 · Validate a pilot
Agree on controlled samples, annotation protocols, expert review and acceptance criteria. Examine feasibility and sources of disagreement.
03 · Review before scaling
Use pilot quality, rework, cost and timing to decide whether to scale. Freeze versions and change-control rules before expansion.
Make better data an acceptance specification
Scientific context
Record provenance, sampling unit, experimental conditions, batches and controls. Identify observations, expert judgments and model predictions.
Engineering quality
Check field completeness, units, formats, versions and permissions. Maintain issue and revision logs.
Model validation
Choose grouping, deduplication and leakage checks for the task. Record baselines, scoring versions, failure types and limitations.
Agree quality thresholds, sampling coverage, rework rules and timelines before the pilot. A generic accuracy claim does not replace task-specific acceptance.
View the biological data specification and field template →LUMESAGE / BIOLOGICAL AI + MEDICAL AI
Two domains. Shared data expertise.
01 / BIOLOGICAL AI
Biological AI
For life-science research: expert involvement in task definition, annotation protocols, data review and evaluation design. Expert fit, data sources and deliverables are confirmed for each project.
Explore the domain →02 / MEDICAL AI
Medical AI
For medical imaging, clinical text and multimodal tasks: expert-reviewed data, targeted collection and annotation, and Medical Gate private evaluation.
Explore the domain →