Rare Disease, Real Kid

The challenge, without the jargon

Think of it as a medical detective story.

The child’s DNA contains many differences that are completely harmless. The difficult part is deciding which few deserve attention—and doing so in a way that is fast, traceable and honest about uncertainty.

01

Read the enormous book

Sequencing turns DNA into data. We check its quality and line it up against a reference genome so each difference has an address.

02

Remove ordinary spelling changes

Most variants are common or biologically unlikely to explain the disease. Evidence-based filters remove this background noise.

03

Match DNA clues to symptoms

The remaining candidates are compared with the child’s clinical features, then several models rank the most coherent explanations.

The challenge outcome

The accepted BUB1B pair rose to the very top.

The system ranked two changes in the same gene as the strongest candidate pair. This is the type of pattern known as compound heterozygosity: two different variants may jointly disrupt the two copies of one gene.

This is a research ranking result—not a clinical diagnosis. Laboratory and family-based confirmation remain essential.

100Rank
Points

Rank #1

F-max 1.000

Our approach

A chain of evidence, not a black box.

Each stage has one job. A candidate can be traced from raw genome data to its final position in the ranked list.

1

Map

Place each DNA read at the right location in the reference genome.

2

Filter

Keep rare, high-quality changes with plausible biological effects.

3

Connect

Form candidate pairs and compare their genes with the child’s symptoms.

4

Question

Ask multiple AI models—and measure when their answers are uncertain.

5

Rank

Deliver a short, auditable list for expert review.

Why nine models?

We treated the models like a panel of specialists.

One model can be confidently wrong. We therefore built different kinds of learners and tested them under the same rules. Some are fast pattern finders, some act like a committee, and Bayesian models keep a distribution of plausible answers instead of pretending one set of parameters is certainly correct.

What we found

The challenge score was perfect. The stronger story is what happened next.

We froze the key choices, moved forward in time, tried independent solved cases and finally searched realistic whole-genome backgrounds. Each test asks a harder question.

Challenge child

1

accepted pair rank

The BUB1B pair was placed first. All eight submission-era learned models agreed.

100 Rank Points · F-max 1.000

Later variants

84.35%

Top-1 accuracy

The selected SBNN found the correct item first when ranking later ClinVar variants among 100 candidates.

PR-AUC 0.9736 · F-max 0.9235

Independent solved cases

719

post-2023 cases

We tested symptom-aware reranking over five independently resampled candidate cohorts.

Includes 64 two-variant cases

Full-genome backgrounds

86.63%

Top-100 recall

The augmented SBNN recovered the causal variant in its first 100 suggestions across 275,739 trials.

Median causal rank: 14

Beyond one child

Would the idea survive outside the hackathon?

A perfect score on one known case is encouraging, but it is not enough. We deliberately tested whether the models could face newer records, different patients and far larger search spaces.

A

Time shift

Train on the past, test on the future

Models learned from the December 2023 ClinVar release and were tested only on later, unseen identifiers.

B

Patient shift

Move from variants to solved cases

Phenotype information improved ranking across every evaluated model in repeated post-2023 case cohorts.

C

Scale shift

Put the signal back into genome-scale noise

2,577 causal variants were crossed with 107 real backgrounds to create 275,739 locked trials.

The hardest stress test

Competitive with widely used protein-effect predictors.

On the full-background VarPB benchmark, our predictor-augmented SBNN nearly matched REVEL at Top-20 and surpassed it at Top-100. It also substantially outperformed AlphaMissense at both shortlist sizes.

Higher is better. “Top-100” asks whether the causal variant appears anywhere in the first 100 candidates sent for review.

Top-20Top-100

Our SBNNuncertainty-aware

65.70%

86.63%

REVELexternal predictor

65.90%

84.82%

AlphaMissenseexternal predictor

44.24%

69.72%

0%50%100%

Why Bayesian models?

A useful system should know when it might be wrong.

Traditional models often return one confident-looking score. Our Bayesian models can generate many plausible predictions, revealing where the answer is stable and where more evidence or human review is needed. That uncertainty is part of the output—not an afterthought.

Scientific transparency

Strong evidence, clearly bounded.

The repository contains the models, ranked outputs, evaluation tables, integrity hashes, tests and a full technical report. It intentionally excludes the private 85 GB patient bundle and all personally sensitive source data.

Browse the complete code Read the 33-page report

What this work does not claim

It is a research prioritization system, not a clinical diagnostic device.
Constructed case cohorts do not replace prospective hospital validation.
The two child variants still require direct phase and functional confirmation.
REVEL and AlphaMissense are inputs to the augmented model, so that model is an evidence integrator—not an independent replacement.

Next: broader clinical cohorts, additional ancestries and sequencing platforms, long-read or family phasing, and prospective calibration studies.

The team

Built across machine learning and computational genomics.

MT

Mame Diarra ToureBayesian deep learning · uncertainty · evaluation

BD

Bonaventure F. P. DossouGenome processing · annotation · biological pipeline

GitHub repository Mame’s research homepage

Finding one meaningful clue in an entire genome.