In this paper, we have tried to design a novel Backtracking based unsupervised learning algorithm which uses K-mean (In future it can be extended to K-mode/K-medoid algorithms) as subroutine to calculate and decide the affinity among data elements and run time dynamism-expansion can be ...
Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated.
Mesmer4uses a convolutional neural network (CNN)5backbone and a feature pyramid network with the watershed algorithm for both nuclear and cell segmentation. Cellpose6and Cellpose27use a CNN with a U-net8architecture to predict the gradient of topological map. A gradient tracking algorithm is then ...
15. Decision tree is a which type of machine learning algorithm? Semi-supervised Machine learning Unsupervised Machine learning Supervised Machine learning Reinforcement Machine learning Answer:C) Supervised Machine learning Explanation: A decision tree is a supervised machine-learning algorithm. ...
The aim of this paper is to discuss the class of leap-frog-type neural learning algorithms having the unitary group of matrices as parameter space. In the discussed framework, each step of a learning algorithm computes as an unconstrained learning step followed by a projection step. The present...
To group tissue types into three distinct categories, we can employ clustering algorithms. In this example, we will use the k-means clustering algorithm, which is widely used for unsupervised learning tasks. We can use thekmeans()function in R to perform k-means clustering: ...
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Sterile inflammation after myocardial infarction is classically credited to myeloid cells interacting with dead cell debris in the infarct zone1,2. Here we show that cardiomyocytes are the dominant initiators of a previously undescribed type I interferon
With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method ...
We used two different computational methods, the Slingshot package27 and the RNA velocity-based scVelo algorithm28, to minimize analysis bias. The results obtained from two methods were consistent with each other. These trajectory maps depicted the progression of germ cells, as they undergo from ger...