Gene Variant Analysis for the Detection of Hemophilia: A Literature Review

N.      Sumathi; K. Anitha      Kumari
Abstract

Background: Proteins act as clotting factors to stop bleeding at the lesion site. This implies that people with hemophilia tend to bleed longer after an injury and are more prone to internal bleeding. Depending on the type of hemophilia, individuals with hemophilia will have lesser amounts of factor VIII or factor IX than people without it.
Objective: By analyzing the gene variant of hemophilia affected patient we can predict the severity of disease at earlier stage which helps to avoid further complications.
Methods: Predicting hemophilia can be achieved through potential technologies like machine learning. Using these technologies, one can detect and predict the severity of hemophilia, such as mild, moderate, or severe.
Results: By comparing the methods used in protein structure analysis, the advantages and limitations of methods used in protein structure analysis are discussed.
Conclusion: The best practices in predicting hemophilia are highlighted in this patent study and particularly aim at the basic understanding of applying the potential technologies in the prediction of hemophilia and its severity. This study represents recent research on hemophilia and the use of different machine learning techniques (MLT) in this area.
Keywords: Hemophilia, machine learning technique, protein structure, gene variant analysis, protein residue network, prophylaxis, FVIII.
Graphical Abstract

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Cite As
Recent Patents on Engineering

Gene Variant Analysis for the Detection of Hemophilia: A Literature Review

Abstract

Graphical Abstract
