Results: Using all patientrelated variables, DNN using SMOTE was the topperforming model in predicting postoperative VTE after spinal surgery (area under the curve [AUC] =0.904), followed by lasso regression (AUC = 0.894), ridge regression (AUC = 0.873), and linear regression (AUC = 0.864)....
2. The rest of the section describes model specification, hyperparameters, and training in more detail. Fig. 2: The top shows an overview of the three Machine Learning (ML) models used for each experimental condition: the convolutional neural network (CNN), the HLF-PC GBDTs, and the HLF-...
Deep learningis an approach to machine learning characterized by deep stacks of computations. This depth of computation is what has enabled deep learning models to disentangle the kinds of complex and hierarchical patterns found in the most challenging real-world datasets. Through their power...
framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The... G Mehmet,AA Margolin - 《Bioinformatics》 被引量: 35发表: 2014年 Regularization Strategies and Empirical Bayesian Learning for MKL Multiple...
SpatialDecon [77] is a non-negative linear regression-based method that assumes a log-normal multiplicative error model between the mixed-cell data and a cell-profile (signature) matrix. dance.modules.spatial.cell_type_deconvo.card CARD [78] applies a conditional autoregressive (CAR) assumption ...
The field of deep neural networks in machine learning has experienced tremendous progress in recent years. Taking advantage of these developments, researchers have started to apply neural networks to batch alignment problems, giving rise to alternate approaches in batch correction. For example, Shaham ...
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that diff
(2004) Active learning using pre-clustering. In Proceedings of the 21rst international conference on machine learning (ICML), p. 79. O’Neill, J., Delany, S. J., & MacNamee, B. (2017). Model-free and model-based active learning for regression. In Advances in computational intelligence...
We included in this comparison both deep learning models (scVI15, scANVI24 and MARS26) and other types of methods (Seurat v312, Symphony20 and a linear support vector machine (SVM)). Out of these models, only our method, scANVI and Seurat v3 tackle both data integration and label ...
By using a large amount of data, our machine-learning algorithm was able to classify the 4D vectors for different gases with high consistency when they are tested individually. The gas-sensing patterns in binary mixture conditions of water and methanol vapors were qualitatively distinguished. The ...