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Gene Expression Regulation

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Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

Nucleic acids research
The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction i...

From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).

Human molecular genetics
Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels t...

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Briefings in bioinformatics
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governi...

Design and deep learning of synthetic B-cell-specific promoters.

Nucleic acids research
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synt...

Single-cell gene regulatory network prediction by explainable AI.

Nucleic acids research
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding ...

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Briefings in bioinformatics
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confir...

SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...

A deep learning model to identify gene expression level using cobinding transcription factor signals.

Briefings in bioinformatics
Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the...

WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Briefings in bioinformatics
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...