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AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

View ORCID ProfileJohannes Birgmeier, Maximilian Haeussler, Cole A. Deisseroth, Karthik A. Jagadeesh, Alexander J. Ratner, Harendra Guturu, Aaron M. Wenger, Peter D. Stenson, David N. Cooper, Christopher Ré, Jonathan A. Bernstein, Gill Bejerano
doi: https://doi.org/10.1101/171322
Johannes Birgmeier
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
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  • ORCID record for Johannes Birgmeier
Maximilian Haeussler
2Santa Cruz Genomics Institute, MS CBSE, University of California Santa Cruz, California 95064, USA
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Cole A. Deisseroth
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
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Karthik A. Jagadeesh
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
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Alexander J. Ratner
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
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Harendra Guturu
3Department of Pediatrics, Stanford School of Medicine, Stanford, California 94305, USA
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Aaron M. Wenger
3Department of Pediatrics, Stanford School of Medicine, Stanford, California 94305, USA
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Peter D. Stenson
4Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK
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David N. Cooper
4Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK
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Christopher Ré
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
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Jonathan A. Bernstein
3Department of Pediatrics, Stanford School of Medicine, Stanford, California 94305, USA
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Gill Bejerano
1Department of Computer Science, Stanford University, Stanford, California 94305, USA
3Department of Pediatrics, Stanford School of Medicine, Stanford, California 94305, USA
5Department of Developmental Biology, Stanford University, Stanford, California 94305, USA
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  • For correspondence: bejerano@stanford.edu
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Abstract

The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE (Automatic Mendelian Literature Evaluation) 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 02, 2017.
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AMELIE accelerates Mendelian patient diagnosis directly from the primary literature
Johannes Birgmeier, Maximilian Haeussler, Cole A. Deisseroth, Karthik A. Jagadeesh, Alexander J. Ratner, Harendra Guturu, Aaron M. Wenger, Peter D. Stenson, David N. Cooper, Christopher Ré, Jonathan A. Bernstein, Gill Bejerano
bioRxiv 171322; doi: https://doi.org/10.1101/171322
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AMELIE accelerates Mendelian patient diagnosis directly from the primary literature
Johannes Birgmeier, Maximilian Haeussler, Cole A. Deisseroth, Karthik A. Jagadeesh, Alexander J. Ratner, Harendra Guturu, Aaron M. Wenger, Peter D. Stenson, David N. Cooper, Christopher Ré, Jonathan A. Bernstein, Gill Bejerano
bioRxiv 171322; doi: https://doi.org/10.1101/171322

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