Steele, E., Tucker, A.: Consensus and meta-analysis regulatory networks for com- bining multiple microarray gene expression datasets. Journal of Biomedical Infor- matics 41(6), 914-926 (2008)Steele, E., Tucker,
These methods, however, have primarily been applied to static datasets in conventional machine learning domains such as vision task classification8,17, leaving their effectiveness in robotic learning unclear. Regularization can lead to improper parameter shifting and error accumulation, while structure ...
et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). Article PubMed PubMed Central CAS Google Scholar Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016). Article CAS Pub...
As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for versatile methods to decipher cell-cell communication in such scenarios. By integrating LIANA and Tensor-cell2cell, we present a protocol that enables the use of a diverse array...
The datasets generated and analyzed during the current study are available in the China Meteorological Data Service Center repository (http://data.cma.cn/). Code availability The model in this study is developed in MATLAB R2019b and the code is available from the corresponding author upon request...
Did you learn something new? Figure out a creative way to solve a problem by combining complex datasets? Let us know in the comments below! Watch NowThis tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understand...
Gönen and Alpaydin [31] also performed experi- ments on real datasets for comparison of existing MKL algorithms and gave an overall comparison between algo- rithms in terms of misclassification error. It concluded that using multiple kernels is better than using a sin- gle one and nonlinear ...
Under each of three scenarios we simulate 5000 datasets from two surveys with respective sample sizes n1 = 150 and n2 = 150, and specify an isotropic exponential correlation function for each Gaussian process. We do not include covariates, i.e. d(xij) = 1 for all i and j. We set β1...
due to the distributed machine learning models. This approach enables big data-based joint learning by enabling multiple data owners to perform machine learning locally using their own datasets and then sharing their local model parameters to obtain a global model. Although, in the above description...
Nevertheless, constructing datasets for training deep learning models requires substantial resources, and the existing image segmentation networks have reached a bottleneck in segmentation accuracy. Improving the accuracy of deep learning algorithms is a major research direction. Multisource data fusion is a...