AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Gene Expression Regulation

Showing 61 to 70 of 260 articles

Clear Filters

Identification of active transcriptional regulatory elements from GRO-seq data.

Nature methods
Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detec...

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

Bioinformatics (Oxford, England)
MOTIVATION: Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER ...

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...

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...

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Nucleic acids research
Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene t...

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...

A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Briefings in bioinformatics
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Her...

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...