docs/METHODS.md

Ranking methods

Semantic similarity

For two phenotype terms, VariantHound uses Resnik similarity: the information content of their most informative common ancestor.

For two phenotype profiles, it calculates symmetric best-match average (BMA):

1. for every query term, find the most similar evidence term;
2. average those best matches;
3. repeat from evidence terms to query terms;
4. average both directions and normalize to 0–1.

The two directions prevent a large generic evidence profile from receiving an unfair advantage.

Evidence fusion

Each gene receives independent canine, human, and mouse phenotype scores. They are combined as the best weighted match plus corroboration at a discount: take the weighted source scores in descending order s1 >= s2 >= s3 and compute

1 - (1 - s1) x (1 - d.s2) x (1 - d^2.s3)

Read it as the chance that at least one species' annotation identifies this gene, where a second and third agreeing species help with diminishing returns. The rule is monotone in every source score, bounded in [0, 1], and depends on nothing but the scores.

Track	Weight
Direct canine evidence	1.000
Human disease evidence	0.833
Mouse model evidence	0.708

The corroboration discount d is 0.6, chosen by sweeping the benchmark rather than by judgement: mean reciprocal rank runs 0.082, 0.087, 0.091, 0.095, 0.083, 0.076 at d = 0.2, 0.3, 0.45, 0.6, 0.8, 1.0. Re-run npm run sweep after any change to the evidence mix; the optimum moves with it.

Why not reciprocal rank fusion

Earlier revisions used semantic-strength-weighted reciprocal rank fusion, which added weight x score^2 / (k + rank_within_source). It was retired because rank within a source depends on how many candidates that source annotates at all, and the sources differ by fortyfold — 354 canine profiles, 5,329 human and 14,590 mouse in the current build. A gene with any canine annotation sat near the top of a 300-long list almost by default, while a genuinely good human match sat at rank 500 of twelve and a half thousand and earned nothing. On the benchmark this let genes with zero human and zero mouse similarity win outright, and it made adding mouse evidence halve discovery performance. The implementation is retained as reciprocalRankFusion so the comparison stays reproducible, but it is not used in production ranking.

Evaluation modes

• Mode 1 — clinical retrieval (known): all available evidence is allowed. It measures retrieval of established knowledge, not discovery.
• Mode 2 — discovery simulation (discovery): canine evidence is withheld in the data layer, before any profile or feature is constructed, under one of two policies that are reported side by side and never combined.
  • no-canine withholds every direct canine observation from every candidate. This is the headline: it is the leave-the-species-out question the project claims to answer.
  • own-disease withholds only the canine records for the case's own disorder. It sounds like the more realistic simulation, but it makes the expected gene the single candidate guaranteed to have no canine evidence while every competitor keeps theirs, so any rule that values canine evidence penalises the right answer by construction. It is reported, and it reads five times worse than no-canine for exactly that reason.
• Mode 3 — temporal validation (temporal): uses a pre-materialized historical knowledge snapshot and removes evidence that was unavailable after the cutoff. Undated evidence is excluded rather than guessed.

Temporal mode fails closed: the ranker refuses to run unless the dataset declares a snapshot cutoff no newer than the requested evaluation cutoff. This code-level check complements, but does not replace, source-release archiving in the ETL layer.

Excluded phenotypes

Explicitly absent phenotypes produce a bounded penalty when they strongly match a gene's evidence profile. Absence is not treated as decisive because veterinary phenotyping is often incomplete and age-dependent.

Interpretation

Scores are relative evidence-ranking scores. They are not calibrated probabilities, diagnostic conclusions, or variant classifications.

Learned ranking gate

The deterministic weighted model is a valid final model. Learned ranking is attempted only after leakage filtering leaves enough independent diseases for nested disease-level and temporal evaluation. Random row splits, duplicate publications across folds, and tuning on the temporal test set are prohibited. A learned model is adopted only if the versioned readiness and promotion gate passes; otherwise the deterministic ranker remains the production method.