Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body

2022 ◽  
Author(s):  
Enas M. F. El Houby
Keyword(s):  
Machine Learning ◽  
Single Nucleotide Polymorphisms ◽  
Human Body ◽  
Machine Learning Techniques ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Learning Techniques ◽  
Pathogenicity Prediction

scholarly journals Performance Analysis of Diabetes Mellitus Using Machine Learning Techniques

2021 ◽  
Vol 12 (6) ◽  
pp. 225-230
Author(s):  
Kandala Srujana Kumari Et.al
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Near Neighbor ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Training Methods ◽  
Learning Techniques ◽  
Many Body Systems

Diabetes is a common disease in the human body caused by a set of metabolic disorders in which blood sugar levels are very long. It affects various organs in the human body and destroys many-body systems, especially the kidneys and kidneys. Early detection can save lives. To achieve this goal, this study focuses specifically on the use of machine learning techniques for many risk factors associated with this disease. Technical training methods achieve effective results by creating predictive models based on medical diagnostic data collected on Indian sugar. Learning from such data can help in predicting diabetics. In this study, we used four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), Near Neighbor K (KNN), and Decision Tree C4.5 (DT), based on statistical data. people. adults in sugar. , preview. The results of our experiments show that the C4.5 solution tree has greater accuracy compared to other machine learning methods.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

2021 ◽  
pp. 2150047
Author(s):  
Marwa M. Abd El Hamid ◽  
Mohamed Shaheen ◽  
Mai S. Mabrouk ◽  
Yasser M. K. Omar
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Personalized Medicine ◽  
Complex Diseases ◽  
Genetic Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nucleotide Polymorphisms ◽  
Learning Techniques

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

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