Wilson rsquo s disease A new perspective review on its genetics diagnosis and treatment
| العنوان: | Wilson rsquo s disease A new perspective review on its genetics diagnosis and treatment |
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| المؤلفون: | Rui Tato Marinho, Narendra N. Khanna, J. Miguel Sanches, Mainak Biswas, Suneet Kumar Gupta, Monika Turk, Jasjit S. Suri, Harman S. Suri, Sandro Orru, Amrita Chaturvedi, Anurag Tiwari, Elisa Godia-Cuadrado, Madhusudhan B K, Christopher Kwaku Asare, Luca Saba, Carlo Carcassi |
| المصدر: | Frontiers in Bioscience. 11:166-185 |
| بيانات النشر: | IMR Press, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Genetics, General Immunology and Microbiology, Scope (project management), business.industry, Computer science, Deep learning, Gene regulatory network, Context (language use), Disease, Genetic analysis, General Biochemistry, Genetics and Molecular Biology, Deep Learning, Hepatolenticular Degeneration, Copper-Transporting ATPases, Pattern recognition (psychology), Humans, Unsupervised learning, Artificial intelligence, business |
| الوصف: | Wilson's disease (WD) is an autosomal recessive disorder which is caused by poor excretion of copper in mammalian cells. In this review, various issues such as effective characterization of ATP7B genes, scope of gene network topology in genetic analysis, pattern recognition using different computing approaches and fusion possibilities in imaging and genetic dataset are discussed vividly. We categorized this study into three major sections: (A) WD genetics, (B) diagnosis guidelines and (3) treatment possibilities. We addressed the scope of advanced mathematical modelling paradigms for understanding common genetic sequences and dominating WD imaging biomarkers. We have also discussed current state-of-the-art software models for genetic sequencing. Further, we hypothesized that involvement of machine and deep learning techniques in the context of WD genetics and image processing for precise classification of WD. These computing procedures signify changing roles of various data transformation techniques with respect to supervised and unsupervised learning models. |
| تدمد: | 1945-0508 1945-0494 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e795002290c1840441025ce64e3fd16 https://doi.org/10.2741/e854 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....6e795002290c1840441025ce64e3fd16 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19450508 19450494 |
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