Conference proceeding
AudioGene: Computer-based prediction of genetic factors involved in non-syndromic hearing impairment
2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp.75-79
IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 9th
12/2011
DOI: 10.1109/AICCSA.2011.6126605
Abstract
AudioGene is a software system developed at the University of Iowa to classify and predict gene mutations that indicate causal or increased risk factors of disease. We focus on a concise example - the most likely genetic causes of a particular form of inherited hearing loss - ADNSHL. Whereas the cost and throughput involved in the collection of genomic data have advanced dramatically during the past decade, gathering and interpreting clinical information regarding disease diagnosis remains slow, costly and error-prone. AudioGene employs machine-learning techniques in an iterative procedure to prioritize probable genetic risk factors of disease, which are then verified with a molecular (wet lab) assay. In our current implementation AudioGene achieves 67% first-choice accuracy (versus 23% using a majority classifier). When the top three choices are considered, accuracy increases to 83%. This has numerous implications for reducing the cost of genetic screening as well as increasing the power of novel gene discovery efforts. While AudioGene is focused on hearing loss, the design and underlying mechanisms are generalizable to many other diseases including heart disease, cancer and mental illness.
Details
- Title: Subtitle
- AudioGene: Computer-based prediction of genetic factors involved in non-syndromic hearing impairment
- Creators
- K. R Taylor - University of IowaA. P DeLuca - University of IowaC. W Goodman - University of IowaB. W Tompkins - University of IowaT. E Scheetz - University of IowaM. S Hildebrand - University of IowaP. L. M Huygen - Department of Otorhinolaryngology, University Hospital, P.O. Box 9101, 6500 HB Nijmegen, The NetherlandsR. J. H Smith - University of IowaT. A Braun - University of IowaT. L Casavant - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp.75-79
- Conference
- IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 9th
- DOI
- 10.1109/AICCSA.2011.6126605
- ISSN
- 2161-5322
- eISSN
- 2161-5330
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Molecular Physiology and Biophysics; Anatomy and Cell Biology; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Center for Bioinformatics and Computational Biology; Otolaryngology; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984187181502771