Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

Source

arXiv

arxiv.org/abs/2606.24779v1

Read the source

Source

Plain-Language Brief

Researchers present DeepBD, an AI system designed to help diagnose genetic birth defects by prioritizing patient-specific candidate variants. The system uses a workflow with various components such as rule evidence, sequence and variant-effect representations, phenotype-conditioned biological context, and specialist modules for refinement. It was tested on an in-house cohort of 18,622 cases and outperformed other methods on a benchmark. Components like rule evidence, mechanistic context, and specialist refinement were found to provide complementary signals.

What the Document Says

  • Researchers present DeepBD for variant prioritization and diagnosis of genetic birth defects.
  • DeepBD is a grounded agentic workflow with LLM-assisted case structuring, pretrained evidence engine, specialist evidence modules, and a grounded diagnostic review layer.
  • The evidence engine learns from structured rule evidence, sequence and variant-effect representations, and phenotype-conditioned biological context.
  • Specialized modules and the agentic layer provide tool-based refinement, candidate-pool review, and diagnosis-oriented synthesis.
  • DeepBD was developed using an in-house fetal and infant cohort of 18,622 cases.
  • DeepBD achieved high Recall@n on an internal held-out solved-case benchmark compared to standalone Exomiser, DeepRare, and LLM reranking baselines.
  • Ablation and overlap analyses show that rule evidence, mechanistic context, and specialist refinement provide complementary signals.

Key Concepts Mentioned

  • DeepBD
  • genetic birth defects
  • variant prioritization
  • workflow
  • LLM-assisted case structuring
  • pretrained evidence engine
  • specialist evidence modules
  • grounded diagnostic review layer

Claims Made by the Document

  • DeepBD outperformed standalone Exomiser, DeepRare, and LLM reranking baselines.
  • Rule evidence, mechanistic context, and specialist refinement provide complementary signals.

Evidence Used

  • Recall@1/3/5/10 of 0.658/0.882/0.912/0.929 on an internal held-out solved-case benchmark.
  • Ablation and overlap analyses.
  • Comparison with standalone Exomiser, DeepRare, and LLM reranking baselines.

Methods / Approach

  • DeepBD workflow
  • rule evidence
  • sequence and variant-effect representations
  • phenotype-conditioned biological context
  • specialist modules
  • tool-based refinement
  • diagnosis-oriented synthesis

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not specify limitations of the authors' work. As an assistant, I don't have access to external information beyond the provided text.

Open Questions

  • What specific tools were used for tool-based refinement?
  • What was the nature of the in-house fetal and infant cohort?

Notes for Future Q&A

May include interpretation. Labeled where it does.

It would be helpful to know the specific tools used for tool-based refinement and more details about the in-house fetal and infant cohort.

Raw Abstract / Excerpt

Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved many cases from variant detection to post-sequencing interpretation: clinicians must rank patient-specific candidate variants under incomplete fetal or infant phenotypes and heterogeneous evidence from population genetics, variant-effect prediction, gene-disease validity, phenotype ontologies, cellular and pathway context, protein structure and clinical literature. We present DeepBD, a grounded agentic workflow for variant prioritization and diagnostic interpretation of genetic birth defects. DeepBD organizes the workflow into LLM-assisted case structuring, a pretrained evidence engine, specialist evidence modules and a grounded diagnostic review layer. The evidence engine learns patient-specific variant scores from structured rule evidence, sequence and variant-effect representations and phenotype-conditioned biological context, whereas specialist modules and the agentic layer provide tool-based refinement, candidate-pool review and diagnosis-oriented synthesis from ranked candidates. Developed using an in-house fetal and infant cohort comprising 18,622 cases, DeepBD achieved Recall@1/3/5/10 of 0.658/0.882/0.912/0.929 on an internal held-out solved-case benchmark, outperforming standalone Exomiser, DeepRare and prompted LLM reranking baselines evaluated on Exomiser-derived top-20 candidate variants. Ablation and overlap analyses show that rule evidence, mechanistic context, and specialist refinement provide complementary signals. These findings support a grounded agentic workflow that separates evidence integration, tool-based refinement, and LLM-assisted diagnostic review for retrospective variant prioritization in genetic birth defects.