Medical School Case Western Reserve University School of Medicine Graduated: 1981
Procedures:
Eye Muscle Surgery Corneal Surgery Lens and Cataract Procedures Ophthalmological Exam
Conditions:
Cataract Fractures, Dislocations, Derangement, and Sprains Glaucoma Keratitis Macular Degeneration
Languages:
English
Description:
Dr. Weaver graduated from the Case Western Reserve University School of Medicine in 1981. He works in Billings, MT and specializes in Ophthalmology. Dr. Weaver is affiliated with Billings Clinic Hospital.
Dr. Weaver graduated from the University of South Alabama College of Medicine in 1989. He works in West Winfield, NY and specializes in Family Medicine. Dr. Weaver is affiliated with Bassett Medical Center.
- Austin TX, US Stephen Ayers - Houston TX, US Daniel Weaver - Austin TX, US Justin Reese - Milledgeville GA, US
Assignee:
Genformatic, LLC - Austin TX
International Classification:
G06F 21/62 G06F 17/30 G06F 19/22
US Classification:
713189, 707756
Abstract:
The present disclosure presents methods, systems, and devices for encrypting and comparing genomic data. The comparison of genomic data allows the owner of the data to ensure security of the data even when the party conducting the comparison is beyond the control of the owner of the data. The encryption of the genomic data enables the transmission, storage, and use of the genomic data in a secure media.
Method Of Machine Learning, Employing Bayesian Latent Class Inference: Combining Multiple Genomic Feature Detection Algorithms To Produce An Integrated Genomic Feature Set With Specificity, Sensitivity And Accuracy
- Austin TX, US Brandi CANTAREL - Austin TX, US Justin REESE - Austin TX, US Daniel B. WEAVER - Austin TX, US
Assignee:
GENFORMATIC, LLC - Austin TX
International Classification:
G06F 19/24 G06N 99/00 G06N 7/00
US Classification:
706 12, 706 52
Abstract:
BAYSIC (BAYesian System for Integrated Combination) combines sets of genomic and other biological data features to optimize selected data feature attributes, for example, detecting genome variants including single nucleotide variants (SNVs) and small insertion/deletions in genomes. The present disclosure presents one possible embodiment employing BAYSIC to combine single nucleotide variants detected by several distinct variant calling methods into an integrated SNV call set that is more accurate than any single SNV calling method or any ad hoc method of combining call sets. BAYSIC is a, tested and validated method using unsupervised machine learning, employing Bayesian latent class inference to combine variant sets produced by different packages.