INTEGRATIVE TRANSCRIPTOMIC, PROTEOMIC, AND MACHINE LEARNING APPROACH TO IDENTIFYING FEATURE GENES OF ATRIAL FIBRILLATION USING ATRIAL SAMPLES FROM PATIENTS WITH VALVULAR HEART DISEASE

Abstract Background Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms.We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.Methods At the transcriptomic level, four microarray datasets (GSE41177, GSE79768,

Disease samples are naturally WOVEN TOPLONG SLV fewer than healthy samples which introduces bias in the training of machine learning (ML) models.Current study focuses in learning discriminating patterns between cesarean and non-cesarean phenomena based on a dataset consisting of 161 features of total 692 cesarean and 5465 non-cesarean samples which

Loss to follow-up occurs in randomized controlled trials.Missing data methods, including multiple imputation (MI), can be used but often rely upon untestable assumptions.Sensitivity analysis can quantify violations of these assumptions.Since an adequate sensitivity analysis requires evaluation of multiple scenarios, presenting this information in a

Parachordoma is an extremely rare soft tissue tumor of unknown lineage.Parachordoma develops most often on the extremities.Only 2 cases have been reported as pelvic parachordoma.A 46-year old Egyptian woman Underwear with a huge painful pelvic mass was found to have a parachordoma with ectopic pelvic right kidney.There is only one report in the lit

Focus of the paper is on the capital price in the Republic of Srpska, which is composed of the price of shareholder capital and the price of debt.The purpose of the research is to compare the capital price before and after beginning of the financial crisis, that Transportation Art corresponds to the comparison of the interest rates levels before an