With the development of single-cell technologies, more and more sequencing data is available, including single-cell RNA sequencing (scRNA-seq), single-cell DNA sequencing (scDNA-seq), and single-cell assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq). These data cover genomics, epigenomics, transcriptomes, and proteomics.

Given that single-cell sequencing remains a relatively nascent field, there is immense potential for further growth and discovery. It is critical to use these sequences to investigate the genomic and transcriptomic profiles of individual cells. Usually, researchers need to distinguish or cluster the sequences for cell heterogeneity and function analysis. In addition, the genomics function also provides an assessment of motif identification, cell-type-specific transcriptional regulon detection, and even disease relationships. In similar pursuits, with the copious number of cells increasing the dimensionality and scale of the data, numerous machine learning models were urgently developed to make these predictions. Machine learning methods are important techniques for analyzing single-cell sequencing data, especially for ensemble learning, large-scale data processes, various kernel designs, and imbalanced classification methods.

In this Special Issue, we invite authors to contribute original research manuscripts or reviews, which introduce the improved machine learning algorithms and their application in single-cell data and single-cell multi-omics sequence analysis.

Potential topics include but are not limited to the following:

• Single-cell omics cell types prediction with machine learning methods
• Cell-type-specific regulon identification methods
• Single-cell omics and disease relationship prediction
• Single-cell heterogeneity and functional diversity analysis
• Advanced machine learning methods with the application to single-cell multi-omics
• Cloud computing and parallel machine learning techniques for single-cell omics and genomics function analysis