However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. In this study, employing the deep learning methodology, we have revealed novel markers for splenic DNT cells in C57BL/6 ...
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map ...
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised traini...
focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed....
deepcell-tfis a deep learning library for single-cell analysis of biological images. It is written in Python and built usingTensorFlow 2. This library allows users to apply pre-existing models to imaging data as well as to develop new deep learning models for single-cell analysis. This library...
28] have shown promising performance in single-cell analysis, the customized interfaces of such tools are largely missing in the existing packages. Those motivate the development of our DANCE system which not only acts as a benchmark platform but also provides customized deep learning infrastructure ...
Deep learning is a complex machine learning algorithm that involves learning inherent rules and representation levels of sample data through large neural networks with multiple layers. It is popular for its automatic feature extraction capabilities and is applied in various areas such as CNN, LSTM, RN...
A high-throughput hyperspectral microscope imaging (HMI) technology with hybrid deep learning (DL) framework defined as “Fusion-Net” was proposed for rapid classification offoodborne bacteriaat single-cell level. HMI technology is useful in single-cell characterization, providing spatial, spectral and...
Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in...
However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep ...