Spread the love“`html As the digital landscape evolves, content remains king. However, the demand for consistent, high-quality content can be overwhelming for creators and marketers alike. This is ...

Abstract: Monitoring student activity manually constantly is a laborious endeavor. Over the past few years, there has been a rapid expansion in the usage of cameras and the automatic identification of ...

Batch effect correction has been recognized to be indispensable when integrating scRNA-seq data from multiple batches. Here, we propose SMNN for batch effect correction of scRNA-seq data via ...

Internal covariate shift [1,2,3] refers to the phenomenon where the distribution of inputs to a deep neural network changes as the network's weights are updated during training. This can result in ...

This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network (SNN), which takes advantage of the sophisticated training regime of Convolutional Neural Network (CNN) ...

Overcorrection has been one of the main concerns in employing various data integration methods, which risk removing the biological distinction and are harmful for cell-type identification. Here, we ...

Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose ...

Metabolomics aims at characterizing metabolic biomarkers by analytically describing complex biological samples 1. At present, the metabolomics based on liquid chromatography mass spectrometry (LC/MS) ...