Skip to main content

Researchers

Wet lab and dry lab, treated as equal class.

For academic, biotechnology, and pharmaceutical R&D, hospital research arms, contract research organisations, and university bioinformatics cores. The literature grounds the design, the design grounds the analysis, and the bench feeds back into the next design.

Methodology consensus

Begin from what the literature actually did.

Lumen reads full-text Methods sections, not abstracts, and aggregates concrete parameter values with citation counts and a strength indicator. Where there is no evidence, it says so.

Full-text Methods section · PubMed Central

Libraries were sequenced to a depth of approximately 30 million reads per sample. Differential expression was assessed with DESeq2; genes with absolute log2 fold-change ≥ 1.5 and Benjamini–Hochberg adjusted p < 0.05 were considered significant. Each condition comprised six biological replicates.

Extracted consensus
Methodology parameters aggregated across the corpus with citation counts and evidence-strength indicators.
ParameterConsensus valueEvidence
Sequencing depth30M reads / sample28 citations· Strong
Fold-change threshold|log2FC| ≥ 1.519 citations· Moderate
Multiple-testing correctionBenjamini–Hochberg FDR 0.0534 citations· Strong
Sample sizen = 6 per group12 citations· Moderate
Batch-effect modelNo literature evidence availableNo literature evidence

Lab-in-the-loop

Design-Build-Test-Learn, closed.

Labtrack outcomes feed back into Praxis, attributed distinctly from published literature so you always know whether a recommendation rests on your bench or the record.

Design-Build-Test-Learn loop. Praxis produces a design; Labtrack builds the protocol, records the experiment, and captures the outcome, yield, purity, and success or failure with reason, which feeds back into the next Praxis design.DesignPraxisBuildLabtrack protocolTestExperimental recordLearnOutcome capturedoutcome signal, yield, purity, success / failure with reasonWet lab and dry lab, equal class

Across the roles

One loop, many practitioners

Wet-lab researchers

Capture experimental outcomes in Labtrack, yield, purity, success or failure with reason, and feed them back into the next Praxis design.

Dry-lab and bioinformatics

Plan analyses in Nexora with citation-justified parameters, then execute in Novora over Nextflow with full provenance.

Machine-learning practitioners

Run predictive models in Omnix with mandatory SHAP interpretability. Every prediction carries its feature attributions and the research-only annotation.

University bioinformatics cores

Serve multiple groups with reproducibility certificates and transparent quotas, not credit-based consumption pricing.

Free for qualifying academic use.

Transparent quotas against documented limits. No credits, no consumption metering.