1 / 15

Benchmarking Methods for Identifying Causal Mutations

Our goal is to identify and diagnose rare genetic diseases, challenging for clinicians due to low exposure. PhenomeCentral allows clinicians to upload patient data, get matched with similar cases, and gather evidence. Using the Human Phenotype Ontology and Exomiser, we aim to reproduce performance, expand to new patient domains, and test hypotheses on disease-causal genes. Patient Pair Simulation introduces control genomes, mutations, and HPO terms, tackling challenges like data scarcity and noise.

veta
Télécharger la présentation

Benchmarking Methods for Identifying Causal Mutations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Benchmarking Methods for Identifying Causal Mutations Tal Friedman

  2. Rare Genetic Diseases • Our goal: identify and diagnose rare genetic diseases • Difficult for clinicians due to incredibly low exposure • Often not already documented

  3. PhenomeCentral • Clinicians upload patient data

  4. PhenomeCentral • Matchmaking algorithm displays most similar patients • Get additional evidence from other clinicians

  5. Background • Phenotype: Observable characteristics • Human Phenotype Ontology (HPO) Robinson et. al

  6. Exomiser (Robinson et. al, 2014)

  7. Objectives • Reproduce Exomiser performance • Expand to new patient similarity domain

  8. Patient Simulation • Control Genome • Disease • Infected Patient • Mutation • HPO Terms

  9. Results

  10. Patient Similarity • Phenotypic similarity algorithm • Hypothesis: same disease/causal gene • Combine Exomiser results

  11. Patient Pair Simulation • Patient 1 • Patient 2 • Control Genome A • Control Genome B • Sampled mutation • Sampled mutation • Disease • Sampled HPO terms • Sampled HPO terms • Phenotypic Noise & Imprecision • Phenotypic Noise & Imprecision

  12. Results (preliminary)

  13. Challenges • Data • Data • More data

  14. Challenges ROC Curve for Phenotypic Similarity Algorithm

  15. Questions!

More Related