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Gene Expression Regulation

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A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

Briefings in bioinformatics
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particu...

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectivel...

Potential diagnostic biomarkers in heart failure: Suppressed immune-associated genes identified by bioinformatic analysis and machine learning.

European journal of pharmacology
Heart failure (HF) threatens tens of millions of people's health worldwide, which is the terminal stage in the development of majority cardiovascular diseases. Recently, an increasing number of studies have demonstrated that bioinformatics and machin...

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots.

Journal of colloid and interface science
The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their survival, such as biofilm development. Precise regulation of bacterial motility enhances our understandi...

Deciphering the cellular and molecular landscape of pulmonary fibrosis through single-cell sequencing and machine learning.

Journal of translational medicine
Pulmonary fibrosis is characterized by progressive lung scarring, leading to a decline in lung function and an increase in morbidity and mortality. This study leverages single-cell sequencing and machine learning to unravel the complex cellular and m...

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.

Nature communications
Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the...

miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

Nucleic acids research
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start si...

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

Nucleic acids research
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors li...