DEEP learningNON-small-cell lung carcinomaRNA sequencingInferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. ...
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over...
dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze ... Y Xu...
The binding specificities of RNA- and DNA-binding proteins are determined from experimental data using a ‘deep learning’ approach. Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory
Two GNN-based methods and one neural network-based method have been reimplemented under this task. scGNN [40] employs an integrative AE framework that combines gene regulatory signals for scRNA-seq gene expression imputation. GraphSCI [41] employs a graph autoencoder on a cell graph and ...
Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and ...
Modern computer vision research is built on massive image datasets containing image features. The deep learning network model of choice for learning to classify these images is the CNN. In the CNN design, the neurons in the layer responsible for extracting features are not connected to all the ...
Over the past five years, deep learning has been incorporated into bioinformatics studies. For example, DeepBind used a convolutional neural network (CNN) to predict binding proteins and showed higher prediction power than traditional classifiers [16]; DeepSEA learned DNA regulatory codes via a CNN ...
Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions 来自 NCBI 喜欢 0 阅读量: 123 作者:SG Kim,N Theera-Ampornpunt,CH Fang…摘要: Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent ...
Inferring gene regulatory networks with graph convolutional network based on causal feature reconstruction ArticleOpen access12 September 2024 Deep neural network prediction of genome-wide transcriptome signatures – beyond the Black-box ArticleOpen access23 February 2022 ...