The success of deep learning has been shown in various fields including computer vision, speech recognition, natural language processing and bioinformatics. The advance of Deep Learning in Computer Vision has been an important source of inspiration for other research fields. The objective of this work is to adapt known deep learning models borrowed from computer vision such as VGGNet, Resnet and AlexNet for the classification of biological sequences. In particular, we are interested by the task of splice site identification based on raw DNA sequences. We focus on the role of model architecture depth on model training and classification performance.We show that deep learning models outperform traditional classification methods (SVM, Random Forests, and Logistic Regression) for large training sets of raw DNA sequences. Three model families are analyzed in this work namely VGGNet, AlexNet and ResNet. Three depth levels are defined for each model family. The models are benchmarked using the following metrics: Area Under ROC curve (AUC), Number of model parameters, number of floating operations. Our extensive experimental evaluation show that shallow architectures have an overall better performance than deep models. We introduced a shallow version of ResNet, named S-ResNet. We show that it gives a good trade-off between model complexity and classification performance. Author summaryDeep Learning has been widely applied to various fields in research and industry. It has 1 been also succesfully applied to genomics and in particular to splice site identification. 2We are interested in the use of advanced neural networks borrowed from computer

Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10−300) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.

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Background Coronavirus Disease 2019 (COVID-19) is a rapidly expanding global pandemic resulting in significant morbidity and mortality. COVID-19 patients may present with acute myocardial infarction (AMI). The aim of this study is to conduct detailed analysis on patients with AMI and COVID-19. Methods We included all patients admitted with AMI and actively known or found to be COVID-19 positive by PCR between the 4th February 2020 and the 11th June 2020 in the State of Qatar. Patients were divided into ST-elevation myocardial infarction (STEMI) and Non-STE (NSTEMI). Results There were 68 patients (67 men and 1 woman) admitted between the 4th of February 2020 and the 11th of June 2020 with AMI and COVID-19. The mean age was 49.1, 46 patients had STEMI and 22 had NSTEMI. 38% had diabetes mellitus, 31% had hypertension, 16% were smokers, 13% had dyslipidemia, and 14.7% had prior cardiovascular disease. Chest pain and dyspnea were the presenting symptoms in 90% and 12% of patients respectively. Fever (15%) and cough (15%) were the most common COVID-19 symptoms, while the majority had no viral symptoms. Thirty-nine (33 STEMI and 6 NSTEMI) patients underwent coronary angiography, 38 of them had significant coronary disease. Overall in-hospital MACE was low; 1 patient developed stroke and 2 died. Conclusion Contrary to previous small reports, overall in-hospital adverse events were low in this largest cohort of COVID-19 patients presenting with AMI. We hypothesize patient profile including younger age contributed to these findings. Further studies are required to confirm this observation.