SHALLOW LEARNING MODEL FOR DIAGNOSING NEURO MUSCULAR DISORDER FROM SPLICING VARIANTS

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dc.contributor.authorSathyavikasini, Kalimuthu
dc.contributor.authorVijaya, Vijayakumar
dc.date.accessioned2023-11-20T07:18:02Z
dc.date.available2023-11-20T07:18:02Z
dc.date.issued2017-08-07
dc.description.abstractDiagnosing 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.identifier.urihttps://www.emerald.com/insight/content/doi/10.1108/WJE-09-2016-0075/full/html
dc.language.isoen_USen_US
dc.publisherEmerald Publishing Limiteden_US
dc.subjectMachine learningen_US
dc.subjectDescriptorsen_US
dc.subjectDisease Identificationen_US
dc.titleSHALLOW LEARNING MODEL FOR DIAGNOSING NEURO MUSCULAR DISORDER FROM SPLICING VARIANTSen_US
dc.typeArticleen_US

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