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A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates
Ying Lin, Anjali M. Rajadhyaksha, James B. Potash, Shizhong Han
doi: https://doi.org/10.1101/463547
Ying Lin
1Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA
Anjali M. Rajadhyaksha
2Department of Pediatrics, Division of Pediatric Neurology, Weill Cornell Medicine, New York, NY, 10065, USA
3Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, 10065, USA
4Weill Cornell Autism Research Program, Weill Cornell Medicine, New York, NY, 10065, USA
James B. Potash
5Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
Shizhong Han
5Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA

Article usage
Posted November 07, 2018.
A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates
Ying Lin, Anjali M. Rajadhyaksha, James B. Potash, Shizhong Han
bioRxiv 463547; doi: https://doi.org/10.1101/463547
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