AIMC Topic: Promoter Regions, Genetic

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Promoter analysis and prediction in the human genome using sequence-based deep learning models.

Bioinformatics (Oxford, England)
MOTIVATION: Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many at...

DeepCNPP: Deep Learning Architecture to Distinguish the Promoter of Human Long Non-Coding RNA Genes and Protein-Coding Genes.

Studies in health technology and informatics
Promoter region of protein-coding genes are gradually being well understood, yet no comparable studies exist for the promoter of long non-coding RNA (lncRNA) genes which has emerged as a global potential regulator in multiple cellular process and dif...

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Nucleic acids research
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. Ho...

ME-Class2 reveals context dependent regulatory roles for 5-hydroxymethylcytosine.

Nucleic acids research
Since the discovery of 5-hydroxymethylcytosine (5hmC) as a prominent DNA modification found in mammalian genomes, an emergent question has been what role this mark plays in gene regulation. 5hmC is hypothesized to function as an intermediate in the d...

Modulation of aldosterone levels by aldosterone synthase promoter polymorphism and association with eGFR decline in patients with chronic kidney disease.

Discovery medicine
To determine whether -344T/C CYP11B2 promoter polymorphism affects serum aldosterone levels and whether this polymorphism is an indicator of eGFR decline in patients with chronic kidney disease. -344 C/T CYP11B2 gene polymorphism analysis was perform...

TSSPlant: a new tool for prediction of plant Pol II promoters.

Nucleic acids research
Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). M...

Higher order methylation features for clustering and prediction in epigenomic studies.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-under...

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns.

Oncotarget
Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome pr...

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

Nucleosides, nucleotides & nucleic acids
In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA...

Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer.

Epigenomics
AIMS: We applied artificial neural networks (ANNs) to understand the connections among polymorphisms of genes involved in folate metabolism, clinico-pathological features and promoter methylation levels of MLH1, APC, CDKN2A(INK4A), MGMT and RASSF1A i...