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GaiaLab

AI-Powered Biological Intelligence · Any Gene Set · Any Disease · 35 Seconds

23-Database Parallel Synthesis · FDR-Corrected Pathway Enrichment · Drug Repurposing Candidates · Multi-Agent AI Debate · Executive Intelligence Brief · 4D Network Visualization

Transform any gene list into publication-quality biological insights. GaiaLab fetches live data from 23 integrated databases simultaneously, synthesizes with multi-model AI (DeepSeek → OpenAI → Google → Anthropic), and delivers pathways, drug repurposing candidates, mechanistic hypotheses, and a confidence-scored evidence ledger — in under 35 seconds. Built for translational oncology teams, drug discovery groups, and academic researchers.

Integrated biological databases (PubMed · ChEMBL · OpenTargets · BioGRID · and more)

AI agents debate every conclusion (Hypothesis · Critic · Evidence · Risk · Innovation · Synthesis)

2.4M+

Bioactive compounds indexed across ChEMBL, DrugBank, PubChem, DGIdb

<35s

Fresh analysis · <1s cached · 100% citation coverage on top papers

Mechanism Derivation

Maps checkpoint and resistance genes to IO escape mechanisms via pathway enrichment, interaction topology, and cross-validated source agreement. Mechanisms are derived from structured data, not generative text.

Interaction Network

Force-directed protein interaction graph with timeline overlay. Surfaces hub centrality, predicted edges, and topological context across curated interaction databases.

Evidence Ledger

Per-claim PMID-linked evidence trail covering pathways, therapeutic signals, and mechanistic hypotheses. Polarity scoring classifies each citation as supporting, contradicting, or mixed.

Immutable Snapshots

Every audit run is captured as an immutable snapshot: gene inputs, model versions, scoring parameters, and full outputs. Diff against prior runs or replay for independent verification.

Credibility Gating

Evidence Depth Score (EDS), Contention Index (CI), and grounded ratio gate every output. Claims below threshold are flagged or suppressed with transparent rationale.

Structured Export

Export audit results as JSON evidence packages or structured briefs. Includes scoring context, PMID links, contradiction annotations, and model configuration metadata.

Signal Credibility Metrics

Citation Coverage

94%

Run Stability

8.7/10

Contradiction Rate

2.1%

Avg Credibility Score

8.9/10

Derived from most recent melanoma IO resistance audit across 23 sources (22 external + internal SAE)

Platform

About GaiaLab

GaiaLab is an AI-powered biological intelligence platform that transforms any gene list into publication-quality insights. It fetches live data from 23 integrated databases in parallel, synthesizes with multi-model AI, and delivers FDR-corrected pathways, drug repurposing candidates scored 0–100, AI-generated mechanistic hypotheses, and a confidence-scored evidence ledger — all in under 35 seconds.

Built in Houston, TX · Used for glioblastoma, Alzheimer's, AML, breast cancer, pancreatic cancer and more · Research use · partnerships@gailabai.com

Integrated databases

<35s

Fresh analysis time

AI debate agents

Origin

Built in Houston, TX to give translational teams the same biological intelligence depth as large pharma groups — without the cost, latency, or irreproducibility of manual literature synthesis. Analysed GBM, AML, Alzheimer's, breast cancer and more.

Mission

Surface the non-obvious biological signal — drug repurposing candidates, pathway contradictions, and mechanistic hypotheses — so research teams move from gene list to validated hypothesis faster.

Principles

Evidence-first: Cite primary sources with PMID links and show confidence tiers. No claim without traceability.
Reproducibility: Snapshot every run — inputs, model versions, scoring parameters, and outputs — for independent verification.
Human-in-the-loop: Decision support and audit trail, not clinical recommendation. The researcher reviews; the system surfaces evidence.

Methods & Scoring

Confidence tiers derived from cross-source agreement, study design classification, and citation depth.
Polarity scoring with contradiction notes and contention flags where evidence diverges.
Evidence ledger with per-claim PMID traceability, support/contradict/mixed polarity, and scoring context.
Reproducible snapshots capture inputs, database versions, model settings, and credibility gate outcomes.

See a Sample Snapshot

Download a saved audit snapshot with evidence, scoring, sources, and configuration metadata.

Download sample snapshot Load melanoma IO panel

Includes reproducible inputs, data sources, and model config details.

Melanoma IO Resistance Panel

Oncology reference panels

Internal demos / reference panels

Anti-PD-1 Resistance Audit: Melanoma

Load 10-gene IO resistance panel: PDCD1, CD274, CTLA4, LAG3, HAVCR2, PTEN, B2M, JAK1, STK11, BRAF
IO Response Score + TCGA SKCM mutation frequencies (n=440) fetched live from cBioPortal
Export reproducible JSON evidence package with per-claim PMID traceability

Breast Cancer Panel

Load TP53/BRCA1/EGFR with breast cancer context
Inspect pathway enrichment and grounded ratio
Compare against a prior snapshot for stability

Colorectal KRAS Panel

Load KRAS/NRAS/BRAF with colorectal context
Explore 3D interaction network hubs
Review mechanism classifications and drug overlap

IO Responder Profile: Inflamed TME

Load inflamed panel: CD8A, CXCL9, CXCL10, PDCD1, LAG3, TIGIT — cytotoxic T-cell infiltration + chemoattractants
IO Response Score 100/100 → Strong Response Likelihood — contrast with 16/100 resistance panel
See which checkpoints are targetable (LAG3 → relatlimab, TIGIT → tiragolumab)

Configure Audit Run

Specify checkpoint or resistance-associated gene targets and a melanoma disease context. The pipeline aggregates 23 sources (22 external + internal SAE interpretability) — including ClinicalTrials.gov, OpenFDA safety signals, and PharmGKB pharmacogenomics — and returns a structured evidence ledger with per-claim PMID links and credibility gating.

Gene Symbols

Enter 2–10 gene symbols separated by commas

Try:

Disease Context

Be specific — disease, subtype, and mechanism context improve output quality

Try:

Output Perspective

Include therapeutic signal mapping (drug targets, trials, safety)

Adds DGIdb, ChEMBL, DrugCentral, ClinicalTrials.gov, OpenFDA, and PubChem. Increases audit runtime.

Clinical Biomarkers FDA-Approved IO Predictors optional

TMB (mut/Mb) FDA-Approved

≥10 mut/Mb = TMB-H (KEYNOTE-158). ≥20 = very high.

MSI / MMR Status FDA-Approved

Not tested MSS / pMMR MSI-L MSI-H / dMMR

PD-L1 Expression FDA-Approved

CPS (Combined Positive Score)

TPS (Tumor Proportion, %)

CPS≥1 nivolumab eligible · CPS≥10 pembrolizumab preferred · TPS≥50% monotherapy

Executing Evidence Audit

Databases

Literature

AI Synthesis

Evidence Gate

Assembly

Initializing pipeline...