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Promoter Regions, Genetic

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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...

Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.

PloS one
Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolu...

Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. ...

A knowledgebase of the human Alu repetitive elements.

Journal of biomedical informatics
Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participate in multiple processes related with genome regulation and compartmentalization. Moreover, they hav...

Characterization and machine learning prediction of allele-specific DNA methylation.

Genomics
A large collection of Single Nucleotide Polymorphisms (SNPs) has been identified in the human genome. Currently, the epigenetic influences of SNPs on their neighboring CpG sites remain elusive. A growing body of evidence suggests that locus-specific ...

Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors.

PloS one
As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, whi...

EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Enhancers and promoters, as regulatory DNA elements, play pivotal roles in gene expression, homeostasis, and disease development across various biological processes. With advancing research, it has been uncovered that distal enhancers may...

Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.

Bioinformatics (Oxford, England)
MOTIVATION: Advances in bacterial promoter predictors based on machine learning have greatly improved identification metrics. However, existing models overlooked the impact of negative datasets, previously identified in GC-content discrepancies betwe...

[Machine learning-aided design of synthetic biological parts and circuits].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up approach to progressively design biological parts, devices, and circuits, aiming to create artificial bi...

[Intelligent design of nucleic acid elements in biomanufacturing].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Nucleic acid elements are essential functional sequences that play critical roles in regulating gene expression, optimizing pathways, and enabling gene editing to enhance the production of target products in biomanufacturing. Therefore, the design an...