Research Program

ML Reliability Assessment Framework & Platform for GWAS Research Output Reliability

Build any GWAS ML pipeline - Get every metric that matters

NHDSL’s proposed research program focuses on a reproducibility-first framework for evaluating GWAS-derived biomedical machine-learning research outputs.  The project focuses on transforming GWAS-based machine learning from isolated performance reporting into structured reliability assessment using two complementary scores: the Biological Reliability Score (BRS) and the Computational Reliability Score (CRS).

Instead of relying on a single accuracy value or a single experimental run, the framework evaluates model discrimination, probability calibration, multi-run robustness, external dataset generalization, and feature stability together. The platform is being prepared as a research workbench intended to support modular GWAS reliability workflows, reproducible artifacts, repeated-run evaluation, and structured scientific reports. 

Core reliability dimensions:
Biological signal survival · External support · Ranking quality · Calibration · Cross-run stability · Leakage-safe validation · Pathway interpretation

BRS evaluates whether candidate SNPs, genes, and pathways remain biologically credible across runs, models, validation settings, external datasets, and annotation layers.

CRS evaluates whether machine-learning predictions are computationally reliable through ranking quality, calibration, cross-run stability, and generalization behavior.

GWAS data are evaluated through base models, ensemble integration, validation layers, repeated seeded runs, and integrity checks to produce two complementary reliability scores: the Biological Reliability Score (BRS) and the Computational Reliability Score (CRS).

Framework Methodology 

The framework treats variance as evidence. Instead of selecting a single best run, it evaluates how predictions, metrics, calibration behavior, and selected genomic features behave across models, seeds, and datasets. 

Reliable ML Platform

The development-stage workbench is intended to support the framework as a modular research system for GWAS research-output reliability assessment. It is designed to support reusable workflow steps, reproducible artifacts, repeated-run evaluation, ensemble comparison, provenance tracking, and structured reliability reports. Researchers can assemble GWAS-ML workflows from reusable pipeline steps, where each step can be edited, replaced, extended, or combined with custom logic. This design supports flexible experimentation across preprocessing choices, feature representations, model families, ensemble architectures, validation datasets, repeated seeded runs, and reliability reports.

Current Status

The NHDSL reliability framework and platform are in documentation, compliance, and reproducibility-readiness review. The development-stage workbench is intended to support GWAS/PRS research-output reliability assessment, including modular workflow construction, repeated-run evaluation, ensemble comparison, provenance tracking, and structured report generation.

Project-specific software development, controlled-data analysis, validation, grant deliverables, or operational research work will be performed only by personnel with appropriate work authorization and applicable data-use approvals.

A methodology preprint, public repository, and reproducible release package are planned after authorship, licensing, documentation, and compliance review.

Sample report formats

The reports below are illustrative development-stage examples showing the structure of platform-generated outputs. They are intended to demonstrate reporting capabilities, not finalized biological findings. The illustrative reports use development-stage, synthetic, public, or non-controlled examples and should not be interpreted as finalized biological findings or clinical evidence. 

Illustrative BRS/CRS Reliability Report

Illustrative Model Evaluation Report