{"id":371378,"date":"2017-03-15T23:49:03","date_gmt":"2017-03-16T06:49:03","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=371378"},"modified":"2018-10-16T21:48:42","modified_gmt":"2018-10-17T04:48:42","slug":"new-generation-machine-learning-analysis-single-nucleotide-polymorphism-data-deep-learning","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/new-generation-machine-learning-analysis-single-nucleotide-polymorphism-data-deep-learning\/","title":{"rendered":"New Generation of Machine Learning: Analysis of Single Nucleotide Polymorphism Data with Deep Learning"},"content":{"rendered":"<p><strong>Objective<\/strong><\/p>\n<p>In recent years, both bioinformatics experts and medical doctors have been working on programming languages such as R, Python, C #. Most of these studies are focused on machine learning, biostatistics and optimization. The biggest problem with these analyzes was that the &#8220;computing capacity&#8221; of the PCs or on-prem server was low and unsustainable. However, thanks to cloud systems, this problem has also been eliminated. On the other hand, &#8220;Machine Learning&#8221;, which brought a new breath to genome data analysis, has been included in almost every genomic data analysis in the last 10 years. This study aimed to compare Deep Learning, which emerged as &#8220;Next Generation Machine Learning&#8221;, for the analysis of Single Nucleotide Polymorphism (SNP) data with the classical Neural Network and Random Decision Forest methods. In this way, the performance and effect of a new generation method group will be revealed.<\/p>\n<p><strong>Method<\/strong><\/p>\n<p>The data used in this study has been simulated with PLINK software. Data prepared for different SNP numbers (one, two and three million SNPs) were screened as population-based (equally distributed patient-control). (100,250,500 patient-control) For analysis, <em><u>&#8220;Microsoft Azure Machine Learning with Microsoft R Server&#8221;<\/u><\/em> and &#8220;h2o&#8221; package has been used used. Here, all parameters for both groups of methods have been optimized by the &#8220;Hyper Search&#8221; technique. The results have been compared with accuracy, precision and recall measurements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Objective In recent years, both bioinformatics experts and medical doctors have been working on programming languages such as R, Python, C #. Most of these studies are focused on machine learning, biostatistics and optimization. The biggest problem with these analyzes was that the &#8220;computing capacity&#8221; of the PCs or on-prem server was low and unsustainable. 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