scCAN: single-cell clustering using autoencoder and network fusion
Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell …
A novel method for single-cell data imputation using subspace regression
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the …
scIDS: Single-cell Imputation by combining Deep autoencoder neural networks and Subspace regression
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful high throughput technique that enables the characterization of …
SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis
Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low …
Single-Cell RNA Sequencing Data Imputation Using Deep Neural Network
Recent research in biology has shifted the focus toward single-cell data analysis. The new single-cell technologies have allowed us to …
Fast and precise single-cell data analysis using a hierarchical autoencoder
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. …
Disease subtyping using community detection from consensus networks
Cancer is a complex disease including a range of disorders that are activated simultaneously by multiple biological processes on …
A comprehensive survey of regulatory network inference methods using single-cell RNA sequencing data
Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living …
RIA: a novel Regression-based Imputation Approach for single-cell RNA sequencing
Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive …
