What is an enhancer?

Thomas, Henry F.; Buecker, Christa

doi:10.1002/bies.202300044

Cited by 6 publications

(2 citation statements)

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“…Each gene is made up of different sections, like a promotor sequence, various exons, and introns [ 17 , 18 , 19 ]. However, other compartments, like RNA polymerase, transcription factors (TF), and enhancer sequences, are essential for the expression of a gene [ 20 , 21 ]. Humans also have a diversity in their genomes called alleles, which affect how they respond to different diseases.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Ashayeri,

Sobhi,

Pławiak

et al. 2024

Cancers

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Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

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Section: Introductionmentioning

confidence: 99%

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Ashayeri,

Sobhi,

Pławiak

et al. 2024

Cancers

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“…promoters and enhancers) [ 2 ]. In the 1980s, enhancers were first discovered in simian virus 40 (SV40) [ 3 ]. Subsequently, researchers have gradually characterized different types of enhancers, and various techniques have been developed to predict and study the function of enhancers ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Integrative approaches based on genomic techniques in the functional studies on enhancers

Wang,

Zhang,

Liu

et al. 2023

Briefings in Bioinformatics

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With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

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The Role of CTCF-Mediated Chromatin Looping in Enhancer-Promoter Communication

Logie

2024

Epigenetics in Biological Communication

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What is an enhancer?

Cited by 6 publications

References 93 publications

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Integrative approaches based on genomic techniques in the functional studies on enhancers

The Role of CTCF-Mediated Chromatin Looping in Enhancer-Promoter Communication

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