As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology
researchers with vast amounts of data but not necessarily the ability to analyze complex data …

The development of high-throughput, data-intensive biomedical research assays and
technologies has created a need for researchers to develop strategies for analyzing …

Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular
differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq …

Background: Single-cell RNA-seq is a powerful tool for measuring gene expression at the
resolution of individual cells. A challenge in the analysis of this data is the large amount of …

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the
genome-wide gene expression levels at the single-cell resolution, bringing a precise …

Motivation New single-cell technologies continue to fuel the explosive growth in the scale of
heterogeneous single-cell data. However, existing computational methods are inadequately …

Background Deep learning has emerged as a versatile approach for predicting complex
biological phenomena. However, its utility for biological discovery has so far been limited …

Highlights•Machine learning increasingly important for NGS.•Deep learning can improve
many NGS applications.•New AI techniques vital for life sciences.Next-generation …

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of
data generated by single-cell technologies are high dimensional, sparse, and …