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Transcription Factors

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The shared biomarkers and immune landscape in psoriatic arthritis and rheumatoid arthritis: Findings based on bioinformatics, machine learning and single-cell analysis.

PloS one
OBJECTIVE: Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker gen...

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Briefings in bioinformatics
Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various as...

MLSNet: a deep learning model for predicting transcription factor binding sites.

Briefings in bioinformatics
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be ...

Identification of mitophagy-related genes and analysis of immune infiltration in the astrocytes based on machine learning in the pathogenesis of major depressive disorder.

Journal of affective disorders
BACKGROUNDS: Major depressive disorder (MDD) is a pervasive mental and mood disorder with complicated and heterogeneous etiology. Mitophagy, a selective autophagy of cells, specifically eliminates dysfunctional mitochondria. The mitochondria dysfunct...

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning.

BMC genomics
BACKGROUND: Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions ...

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Journal of assisted reproduction and genetics
PURPOSE: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics meth...

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning.

Scientific reports
This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 2412...

Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

Journal of medicinal chemistry
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recogniz...

Transcriptional regulation of hypoxic cancer cell metabolism and artificial intelligence.

Trends in cancer
Gene expression regulation in hypoxic tumor microenvironments is mediated by O responsive transcription factors (OR-TFs), fine-tuning cancer cell metabolic demand for O according to its availability. Here, we discuss key OR-TFs and emerging artificia...