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Gene Expression Regulation

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A tuberculosis ontology for host systems biology.

Tuberculosis (Edinburgh, Scotland)
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a mea...

Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic.

Computers in biology and medicine
To address important challenges in bioinformatics, high throughput data technologies are needed to interpret biological data efficiently and reliably. Clustering is widely used as a first step to interpreting high dimensional biological data, such as...

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...