SHALLOW LEARNING MODEL FOR DIAGNOSING NEURO MUSCULAR DISORDER FROM SPLICING VARIANTS

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DC Field	Value	Language
dc.contributor.author	Sathyavikasini, Kalimuthu	-
dc.contributor.author	Vijaya, Vijayakumar	-
dc.date.accessioned	2023-11-20T07:18:02Z	-
dc.date.available	2023-11-20T07:18:02Z	-
dc.date.issued	2017-08-07	-
dc.identifier.uri	https://www.emerald.com/insight/content/doi/10.1108/WJE-09-2016-0075/full/html	-
dc.description.abstract	Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Emerald Publishing Limited	en_US
dc.subject	Machine learning	en_US
dc.subject	Descriptors	en_US
dc.subject	Disease Identification	en_US
dc.title	SHALLOW LEARNING MODEL FOR DIAGNOSING NEURO MUSCULAR DISORDER FROM SPLICING VARIANTS	en_US
dc.type	Article	en_US
Appears in Collections:	2.Article (26)

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