Drug Discovery from Gene Expression/Gene Regulation.

Multi-model biological and sequence information fusion for gene regulatory network inference from single-cell transcriptomics

Identification of transcription factor–target gene interactions and construction of the gene regulatory networks (GRNs) are essential for understanding the molecular mechanisms underlying transcriptional gene regulation. Large-scale single-cell transcriptomics across different tissues offers unprecedented resolution of...

Comprehensive Guide to Gene Expression Analysis from Bulk and Single-Nucleus Transcriptomes.

This chapter covers quality control, normalization, sparse data handling, and transcript quantification for both bulk and snRNA-seq, along with strategies to address challenges such as multi-mapped reads and batch effects, and examines how artificial intelligence and machine learning techniques can improve data process...

Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.

Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer’s disease

A personalized functional genomics atlas for Alzheimer’s disease that uses knowledge-guided graph neural networks to analyze donor-level functional genomics, identify disease subpopulations and trajectories, and link genetic variants to gene regulation is summarized.

Constructing cell-type-specific gene regulatory networks from cell–cell communication and a global gene regulatory network

Abstract Accurately inferring cell-type-specific gene regulatory networks (GRNs) is crucial for understanding cellular heterogeneity, lineage determination, and disease progression mechanisms. Although single-cell RNA sequencing (scRNA-seq) enables high-resolution expression profiling, its inherent sparsity and high no...

MicroRNAs and Their Profiling via Single-Cell Sequencing Technologies.

The potential of single-cell miRNA profiling to contribute to precision medicine and therapeutic development is outlined, including its possible use in biomarker discovery, monitoring tumor heterogeneity, and informing personalized treatment strategies.