Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping
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چکیده
منابع مشابه
Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping
High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discrimina...
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High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new ...
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Introduction Classifying and understanding genetic variation between populations and individuals is an important aim in the field of genomics. Many common diseases (diabetes, cancer, osteoporosis, etc.) and clinically relevant phenotypic traits are elicited from the complex interaction between a subset of multiple gene products and environmental factors. The unique genetic profile of an individ...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2016
ISSN: 2045-2322
DOI: 10.1038/srep19218