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 ...
Fourth, the learned latent embedding (Vu) and the TE family embedding (Ti) are used to predict the multi-mapping ratio (α) for the specific TE locus via a multi-layer perceptron regressor. The total loss to learn the model is composed of two components (L1 and L2). The former ...
The most predictive cell type contexts for nominating therapeutic targets of IBD are CD4+αβ memory T cells, enterocytes of epithelium of large intestine, T follicular helper cells, plasmablasts and myeloid dendritic cells (Fig.5d). The intestinal barrier comprises a thick mucus layer with antimic...
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...
2. Essential concepts in deep learning The field of DL has a rich history, beginning with the development of the MCP artificial neural model by McCulloch and Pitts in 1943 [14]. The concept of the perceptron was later introduced by Rosenblatt, building on the foundation of artificial neurons...
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...
[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 ...
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FC, fully connected layer; Conv3D, 3D convolution; ST, spatial transformer (which back-projects the 2D image to a 3D volume). The number of channels of the convolution kernel can be derived from the dimensions of its input and output. The ellipsis represents the repeating of the preceding ...