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Gene Expression Regulation

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Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Nature genetics
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Dro...

Ensemble learning based assessment of the role of transcription factors in gene expression.

Computers in biology and medicine
Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cell growth by activating a proper set of genes. Regulation of gene expression is controlled through th...

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Nature biotechnology
CysHis zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate...

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 ...

A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular sta...

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Journal of biomolecular structure & dynamics
Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS...

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Computational biology and chemistry
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-c...

IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions.

IEEE journal of biomedical and health informatics
Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput experimental techniques, there is an urgent need to develop computational methods for predicting ch...

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...

Cell-type-directed design of synthetic enhancers.

Nature
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...