An open framework for benchmarking gene-indication evidence against clinical trial outcomes.

ct-validation tests whether a set of gene-indication pairs is enriched for clinical success. It computes risk ratios and odds ratios with confidence intervals across clinical phase transitions and supports semantic disease matching through ontology-based similarity.

Paper: Kostiuk K, Igumnov D, Fedichev P, Feizi A. ct-validation: an open framework for benchmarking gene-indication evidence against clinical trial outcomes. (2026)

Installation

Requires Python 3.11+.

pip install ct-validation

Optional extras:

pip install ct-validation[plot]  # forest plot visualization
pip install ct-validation[mcp]   # MCP server for agent workflows
pip install ct-validation[parse] # data source parsers
pip install ct-validation[fetch] # ChEMBL fetching script dependencies

To run notebooks/benchmark.py, which reproduces the paper, install its pinned environment instead — Jupyter and the plotting theme on top of an editable install of this package:

pip install -r notebooks/requirements.txt

Quick start

Python API

import ct_validation as ctv

results = ctv.validate(
    clinical_trials="data/clinical_trials/gene_indication_max_phase.parquet",
    targets="data/genetic_evidence/genetic_evidence.parquet",
    similarity_lookup="data/mappings/efo_similarity_lookup_0.5.parquet",
)
print(results)
#   phase_label  n_yes   n_no  rr  rr_ci_lower  rr_ci_upper  p_value  ...

Export annotated trials and/or matched-pair audit rows (returned in order):

enrichment, trials = ctv.validate(..., return_trials=True)
enrichment, matched = ctv.validate(..., return_matched_pairs=True)
enrichment, trials, matched = ctv.validate(..., return_trials=True, return_matched_pairs=True)

Or build the match-audit table directly (columns: gene, ct_efo_id, ge_efo_id, similarity, match_type):

matched = ctv.create_matched_pairs_df(
    genetic_evidence=..., clinical_trials=..., similarity_pairs=...,
    similarity_threshold=0.8,
)

Batch mode — compare multiple evidence sources at once:

results = ctv.validate(
    clinical_trials="data/clinical_trials/gene_indication_max_phase.parquet",
    targets=[
        "data/genetic_evidence/gwas_catalog.parquet",
        "data/genetic_evidence/clinvar.parquet",
        "data/genetic_evidence/genebass.parquet",
    ],
    similarity_lookup="data/mappings/efo_similarity_lookup_0.5.parquet",
)
# returns a list of DataFrames, one per evidence source

Prioritized mode — test whether a novel source adds value over an established baseline:

results = ctv.validate(
    clinical_trials="data/clinical_trials/gene_indication_max_phase.parquet",
    targets="data/genetic_evidence/novel_score.parquet",
    baseline_evidence="data/genetic_evidence/established_genetics.parquet",
    similarity_lookup="data/mappings/efo_similarity_lookup_0.5.parquet",
)
# pairs supported only by baseline are excluded

Expand a disease set using semantic similarity:

expanded = ctv.get_expanded_disease_set(
    efo_ids={"EFO:0000270", "EFO:0000384"},
    similarity_pairs="data/mappings/efo_similarity_lookup_0.5.parquet",
    similarity_threshold=0.8,
)

CLI

# With config file
ct-validation --config configs/default.yaml

# With explicit arguments
ct-validation \
    --clinical-trials ct.parquet \
    --targets evidence.parquet \
    --similarity-lookup similarity.parquet \
    -o results/

# Batch mode (multiple evidence sources)
ct-validation \
    --clinical-trials ct.parquet \
    --targets gwas.parquet --targets clinvar.parquet --targets genebass.parquet \
    -o results/

# Save annotated trials and/or matched-pair audit rows
ct-validation --config configs/default.yaml \
    --save-trials --save-matched-pairs -o results/

MCP server

ct-validation-mcp

Exposes two tools for agent-based workflows:

• ct_validate — compute phase-transition enrichment (includes p_value)
• expand_disease_set — expand EFO IDs via semantic similarity

Input schemas

Input	Columns	Description
clinical_trials	gene, efo_id, max_phase, is_ongoing (optional)	Target-indication pairs with highest phase reached, and whether that phase is still running
targets	gene, efo_id	Gene-indication pairs with supporting evidence
similarity_lookup	efo_id_1, efo_id_2, similarity	Pairwise EFO similarity, symmetric with diagonal (optional)
baseline_evidence	gene, efo_id	Baseline evidence for prioritized mode (optional)
gene_universe	one gene per line (text file)	Restrict analysis to these genes (optional)

All inputs accept Parquet files or pandas DataFrames (except gene_universe, which is a text file or a Python set).

clinical_trials must carry one row per (gene, efo_id) pair — rows are counted individually, so a repeated pair would inflate the denominators. validate() raises on duplicates rather than collapsing them, since only the caller knows what max_phase and is_ongoing should be for a merged row.

Output schema

Column	Description
phase_from, phase_to	Phase transition (e.g. 1→2, 1→4)
n_yes, n_no	Pairs entering phase, less censored (with/without evidence)
x_yes, x_no	Pairs reaching target phase
rate_yes, rate_no	Progression rates
rr, rr_ci_lower, rr_ci_upper	Risk ratio with 95% CI (Katz log method)
or, or_ci_lower, or_ci_upper	Odds ratio with 95% CI (Woolf logit method)
p_value	Two-sided Fisher's exact test p-value

When either comparison group is empty (n_yes=0 or n_no=0), rr, or, their confidence intervals, and p_value are undefined (NaN).

Clustered bootstrap intervals (optional)

Katz and Woolf assume independent rows, but one gene spans many indications — so their intervals run narrow when the correlated unit is the gene rather than the pair. Pass cluster_col to add percentile intervals that resample clusters with replacement instead of rows:

results = ctv.validate(..., cluster_col="gene")

This adds four columns — rr_boot_ci_lower, rr_boot_ci_upper, or_boot_ci_lower, or_boot_ci_upper — and nothing else: resampling only re-estimates spread, so point estimates, counts and closed-form intervals are unchanged. Without cluster_col they are absent, so the default schema is the table above.

bootstrap_replicates (default 10,000) and bootstrap_seed (default 0) control the resampling. The percentile bounds carry Monte Carlo error falling as 1/√n; at the default each bound shifts by a few percent of the interval's width across seeds. Both are ignored without cluster_col.

No continuity correction is applied, unlike the closed-form intervals — a percentile bootstrap needs neither log(ratio) nor its standard error. A replicate that empties a cell keeps its ratio, so a bound may come back 0 or infinite; relevant if you log-scale these columns.

Bounds are NaN when an arm holds fewer than two clusters, when the observed table has a zero cell, or when over 25% of replicates leave a ratio undefined (0/0) — past 5% you get a BootstrapReplicateLossWarning. The closed-form columns stay populated unless an arm is empty outright (n_yes=0 or n_no=0).

Matched-pairs export (optional)

When return_matched_pairs=True (API) or --save-matched-pairs (CLI) is set, a separate audit table is returned/saved with:

Column	Description
gene	Gene symbol
ct_efo_id	Disease on the clinical trial row
ge_efo_id	Supporting genetic-evidence disease
similarity	Match score (1.0 for exact matches)
match_type	exact when ct_efo_id == ge_efo_id, else similarity

Enrichment logic

For each phase transition, target-indication pairs that reached at least the starting phase are divided into supported and unsupported groups. The risk ratio is:

RR = (x_yes / n_yes) / (x_no / n_no)

A risk ratio greater than one indicates that genetically supported pairs are more likely to progress.

When a similarity lookup is provided, a pair (gene, disease) is considered supported if there exists evidence (gene, disease') with similarity above the threshold (default 0.8). Matching probes a single orientation of each pair, so the lookup must be symmetric — every pair (a, b) stored as (b, a) too, plus a diagonal (self-similarity=1.0). Validate one with check_similarity_lookup(sim) (or pass check_similarity=True to validate()); it raises rather than silently dropping half of each disease neighbourhood.

Censoring undetermined outcomes

When clinical_trials carries an is_ongoing column, a pair whose highest phase is still running is censored from any transition it has not yet completed — dropped from both n and x where is_ongoing and max_phase < phase_to. Such a pair has not failed the transition; its outcome is not known yet. Censoring is per transition, so a pair that already reached phase_to still counts as a success. Without the column every pair counts as concluded, nothing is censored, and validate() warns. A missing value in the column is read as not-ongoing, matching that default; non-boolean values raise.

is_ongoing is the caller's to define. The parsers in scripts/ set it where nothing concluded at the pair's highest phase and that phase is below approval — reaching approval settles the outcome however many post-marketing trials are still recruiting.

Prioritized mode

When baseline_evidence is provided, pairs supported only by the baseline are excluded. This tests whether a novel evidence source adds predictive value beyond an established benchmark.

Visualization

import ct_validation as ctv

results = ctv.validate(...)
ctv.forest_plot(results, metric="rr", title="Phase I → Approved")

Data source parsers

The scripts/ directory contains reproducible parsers for public databases:

Genetic evidence (scripts/parse/genetic_evidence/):

• GWAS Catalog — genome-wide significant associations (p < 5e-8)
• ClinVar — pathogenic/likely pathogenic variants
• Open Targets — germline genetic evidence streams (score ≥ 0.5); somatic sources (eva_somatic, intogen) are opt-in via --include-somatic
• Genebass — exome-wide associations (p ≤ 1e-7)

Clinical trials (scripts/parse/clinical_trials/):

• ChEMBL — gene-drug links (pChEMBL ≥ 7.0); its indications are parsed but excluded from the aggregate, since they carry no trial status to censor on
• Open Targets — known drug and indication data
• STITCH — high-confidence activation/inhibition links
• DGIdb — drug-gene interactions
• TrialPanorama — interventional studies

Ontology (scripts/r/):

• EFO semantic similarity matrix (Lin + Resnik information content)

Benchmark comparator (scripts/parse/minikel.py):

• Minikel et al. (2024) — Citeline programmes with per-transition censoring, and their genetic associations, for the published-vs-ours comparison

See DATA_SOURCES.md for download links, versions, and fetching instructions.

Configure paths in configs/parsing.yaml and run:

python scripts/parse/gene_universe.py
python scripts/parse/minikel.py
python scripts/parse/run_parsing.py

Configuration

See configs/default.yaml for validation settings and configs/parsing.yaml for data source paths. All config values can be overridden via CLI arguments.

Output options in configs/default.yaml:

output:
  dir: "results/"
  save_trials: false
  save_matched_pairs: false

Development

CI runs on push/PR to main (Python 3.11–3.13, ruff, pytest):

uv sync --extra mcp --extra parse
uv run ruff check src tests scripts
uv run pytest tests/ -v

Availability

Resource	Location
Source code	https://github.com/gero-science/ct-validation
Archived code snapshot	10.5281/zenodo.21840866
Benchmark data	10.5281/zenodo.21839217
Package	PyPI

The data deposit carries every input notebooks/benchmark.py reads. Extract it at the repository root and the notebook runs without fetching anything:

tar xzf ct-validation-data.tar.gz
pip install -r notebooks/requirements.txt
cd notebooks && python benchmark.py

See DATA_SOURCES.md for what the archive contains, per-source licences, and how to regenerate anything it omits.

License

MIT