Human Phenotype Project · predictive benchmark

What can each measurement predict?

PhenoBench maps predictive information across 90 clinically grounded tasks, from blood markers and body composition to sleep, glucose dynamics, and longitudinal physiology.

measurement>representation>task>matched evaluation
No universal score. Methods are ranked only within a Benchmark Track that fixes the task, cohort, split, metric, and allowed information.

160 matched cells · 52 tasks

Matched model-family leaderboard

Mean rank compares methods only where task, cohort, feature set, split, and metric are fixed. Lower is better. The 10-method comparison includes tuned XGBoost and CatBoost. Averaged within each cell, the six pretrained models improved over ridge by a median of 0.004 R² (95% task-cluster bootstrap CI, 0.003–0.007).

#MethodMean rankMatched cells
1TabPFN 3.53.52160
2TabDPT3.87160
3TabFM4.14160
4TabICL4.77160
5TabPFN v25.03160
6TabSwift5.28160
7RealMLP6.01160
8Ridge6.77160
9CatBoost7.16160
10XGBoost8.46160

Benchmark atlas

90 clinical tasks across 15 domains

3 framework diagnostics test benchmark mechanics alongside the clinical task set.

Each card links a clinical question to its valid comparison routes. Aggregate result rows contain no participant-level records or private execution identifiers.

Proof of concept

PhenoBench-LLM

Descriptive validation results on 11 shared tasks. Win rate is the fraction of same-task model comparisons won. It does not measure participant-level prediction accuracy. Models fitted on the same fields generally performed better. The all-available analysis remains downloadable with task coverage.

Win rate with task-bootstrap 95% confidence intervals
82.5%Gemini 3.7 Flash
73.4%Claude Opus 4.8
72.7%Claude Sonnet 5
63.3%Gemini 3.1 Pro
62.2%GPT 5.6 Luna
56.6%Gemini 3.1 Flash Lite
47.2%Claude Haiku 4.5
46.9%Nova Pro
46.2%Grok 4.3
39.5%Llama 4 Maverick
32.9%Llama 3.3 70B
30.1%DeepSeek V4 Pro
25.9%Pixtral Large
20.6%GPT 5.4 Nano

Maintainer-run evaluation

Submit a container

Develop against fabricated, schema-faithful examples. Then send an OCI image pinned by digest. Maintainers run accepted images on private evaluation data when capacity allows.

  1. Choose a synthetic task bundle
  2. Package the prediction contract
  3. Open a submission issue with the immutable image digest

Source, Task Cards, and contribution guide on GitHub.

Manual intake. No automated queue, turnaround guarantee, or guarantee of publication.