Work

AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

Year: 2017

Type: preprint

Abstract: The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE ( A utomatic M endelian L iterature E valuation) greatly automates this process. AMELIE parses hundreds of thousands of full text articles to find an underlying diagnosis to explain a patient’s phenotypes given the patient’s exome. AMELIE prioritizes patient candidate genes for their likelihood of causing the patient’s phenotypes. Diagnosis of singleton patients (without relatives’ exomes) is the most time-consuming scenario. AMELIE’s gene ranking method was tested on 215 singleton Mendelian patients with a clinical diagnosis. AMELIE ranked the causal gene among the top 2 in the majority (63%) of cases. Examining AMELIE’s top 10 genes, amounting to 8% of 124 candidate genes with rare functional variants per patient, results in diagnosis for 95% of cases. Strikingly, training only on gene pathogenicity knowledge from 2011 leads to identical performance compared to training on current data. An accompanying analysis web portal has launched at AMELIE.stanford.edu .

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/171322