The first article in this series will introduce perceptrons and the adaline (ADAptive LINear NEuron), which fall into the category of single-layer neural networks. The perceptron is not only the first algorithmically described learning algorithm [1], but it is also very intuitive, easy to impleme...
In context of pattern classification, such an algorithm could be useful to determine if a sample belongs to one class or the other.To put the perceptron algorithm into the broader context of machine learning: The perceptron belongs to the category of supervised learning algorithms, single-layer ...
In this work, we develop a computational celltyping method for scATAC-seq, named Cellcano. Cellcano implements a two-round supervised learning algorithm. It first trains a multi-layer perceptron (MLP) on the reference dataset and predicts cell types in target data. From the prediction results,...
4b and ‘Fine-tuning PINNACLE for context-specific target prioritization’ section in Methods). The binary classification model can be of any architecture; our results for nominating RA and IBD therapeutic targets are generated by a multilayer perceptron (MLP) trained for each therapeutic area (Fig...
therapeutic area (Fig.4band ‘Fine-tuning PINNACLE for context-specific target prioritization’ section inMethods). The binary classification model can be of any architecture; our results for nominating RA and IBD therapeutic targets are generated by a multilayer perceptron (MLP) trained for each ...
Single-cell transcriptomics (SCT) sequencing technology enables the simultaneous measurement of thousands of genes in cells [5,6,7]. In addition to gene expression, the data usually also contain additional characteristics such as cell type. Since different cell types have different gene expression pro...
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In this work, we develop a computational celltyping method for scATAC-seq, named Cellcano. Cellcano implements a two-round supervised learning algorithm. It first trains a multi-layer perceptron (MLP) on the reference dataset and predicts cell types in target data. From the prediction results,...
[251] adopts multiple-layer perceptron bagging to identify regulons, DeepDRIM [252] utilizes supervised deep neural network to reconstruct gene regulatory networks. In particular, DeepDRIM is shown to be tolerant to dropout events in scRNA-seq and identify distinct regulatory networks of B cells ...
In addition to the systematical difference among omics layers, single-cell data are often complicated by batch effect within the same layer. For example, the SHARE-seq data was processed in four libraries, one of which showed batch effect compared to the other three in scRNA-seq (Supplementary...