Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve = 0.980). Then, we calculated the weight of each key gene using ANN. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls).Ī total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC). Early diagnosis of AMI contributes to improving prognosis. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.Īcute myocardial infarction (AMI) is one of the most common causes of mortality around the world. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Hence, DL methods could be better if we analyze genomic data bigger than this study. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. The performance differences between DL and non-deep ML decrease as the sample size of data increases. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). There are 205,238 participants have recorded disease outcomes for these three diseases. We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). The original UK Biobank data has about 500k participants. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The concern of overusing DL methods motivates us to evaluate DL methods’ performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. However, genomic data usually has too small a sample size to fit a complex network. Recently, many DL methods have been applied to analyze genomic studies. Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing.
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