Journal: PLOS Biology

Article Title: scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data

doi: 10.1371/journal.pbio.3002369

Figure Lengend Snippet: ( A ) The input was the gene–cell matrix of scRNA-Seq or gene activity matrix generated from scATAC-seq. A graph-based autoencoder, which contained a DNNAE and a VGAE, learned the latent associations between genes and cells. The RWR algorithm quantified pathway activity and identified cell phenotype–associated gene modules. ( B ) Main capabilities of scapGNN included inferring single-cell pathway activity profiles, constructing cell cluster association networks, identifying cell phenotype–associated gene modules under multiple cell phenotypes, and quantifying the importance of genes in the pathway. DNNAE, deep neural network autoencoder; LTMG, left truncated mixture Gaussian; RWR, random walk with restart; scATAC-seq, single-cell ATAC sequencing; scRNA-seq, single-cell RNA sequencing; VGAE, variational graph autoencoder.

Article Snippet: Cobolt constructed a multimodal variational autoencoder based on a hierarchical Bayesian generative model that projected the single-cell multi-omics data into shared latent space to perform visualization and clustering [ ].

Techniques: Activity Assay, Generated, Sequencing, RNA Sequencing Assay

Journal: BioImpacts : BI

Article Title: Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality

doi: 10.34172/bi.30586

Figure Lengend Snippet: Comparative analysis of feature extraction strategies and classifiers, showing both the actual accuracies achieved on original datasets and the observed accuracies obtained when these methods were re-implemented and tested on the Figshare dataset

Article Snippet: Wu et al , AutoEncoder , SoftMax , Pilots’ EEG dataset (collected from 20 subjects) , 91.68% , Figshare , 93.78%.

Techniques: Extraction, Modification