Machine Learning Based Comparative Analysis of Methods for Enhancer Prediction in Genomic Data

Kaur, Amandeep; Chauhan, Ajay Pal Singh; Aggarwal, Ashwani Kumar

doi:10.1109/icct46177.2019.8969054

Cited by 13 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous review articles have been published in recent decades concerning: enhancer interactions, including their role [21] at the genome‐wide level; transcription enhancers in animal development, evolution [22], and disease [23]; functional contributions to transcription [24,25]; the functional significance of enhancer chromatin modification [26]; models that describe dynamic three‐dimensional chromosome topology related to development enhancers; methods for identifying enhancer target genes [27] and enhancers [28–30]; the mechanisms of EPIs in higher eukaryotes [31]; bioinformatics analysis methods related to EPIs prediction [32–35]; analysis from sequence data [36,37]; and how EPIs control gene expression [38]. However, with the advancement of computational methods in the past decade, research has increasingly proposed methods for detecting enhancer‐promoter interaction tools based on traditional machine learning or deep learning, but there has yet to be a global overview of solutions specifically for EPI identification.…”

Section: Introductionmentioning

confidence: 99%

Computational methods for identifying enhancer‐promoter interactions

Gong,

Chen,

Tang

et al. 2023

Quant. Biol.

View full text Add to dashboard Cite

BackgroundAs parts of the cis‐regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer‐promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.ResultsIn this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer‐promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer‐promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer‐promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer.ConclusionsThe advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer‐promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions.

show abstract

Section: Introductionmentioning

confidence: 99%

Computational methods for identifying enhancer‐promoter interactions

Gong,

Chen,

Tang

et al. 2023

Quant. Biol.

View full text Add to dashboard Cite

show abstract

“…To recognize the image described by the audio, training process must be done which would aid blind people [4] . Prediction of enhancer in genomic data using machine learning techniques was discussed in the work [5] . To estimate the position and orientation of the camera from the 2D images was carried out in the work [6] .…”

Section: Introductionmentioning

confidence: 99%

Identifying the probability of genetic mutations in lung cancer using predictive and prognostic biomarkers from histopathological images

Venkataramana,

Prasad,

Varma

et al. 2023

Med. Imag. Proc. Tech.

View full text Add to dashboard Cite

Background: Lung cancer is the highest deadliest disease and second largest disease being diagnosed worldwide. In the age of precision medicine, determining a patient’s genetic status is critical. Finding the percentage of gene mutation of a particular biomarker will help in targeted therapy of a patient at an early stage. Objective: Histopathology images are larger in size which needs to be converted into smaller tiles for the computational purpose. Deep Learning Techniques could be applied on this huge number of histopathological images to derive the probability of gene mutation occurrence in predictive and prognostic biomarkers of lung cancer. Methods: In this work, a deep learning convolutional neural network (CNN) model (InceptionV3) is trained on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately predict the mutated genes in lung adenocarcinoma. The convolutional neural network-based model predicts 10 major genetic mutations percentage, i.e., EGFR, FAT1, FAT4, KEAP1, KRAS, LRP1B, NF1, SETBP1, STK11, TP53. Results: InceptionV3 predicted the probability of gene mutation from the histopathology images and categorized the genes as predictive and prognostic. InceptionV3 yielded an accuracy of 82.36% and cross entropy of 37.62%. Conclusion: InceptionV3 was trained on histopathology images to predict gene mutations with an accuracy of 82%. Prediction of gene mutations with different CNN models like AlexNet and ResNet can be explored further.

show abstract

Causal inference in the medical domain: a survey

Wu,

Peng,

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Machine Learning Based Comparative Analysis of Methods for Enhancer Prediction in Genomic Data

Cited by 13 publications

References 35 publications

Computational methods for identifying enhancer‐promoter interactions

Computational methods for identifying enhancer‐promoter interactions

Identifying the probability of genetic mutations in lung cancer using predictive and prognostic biomarkers from histopathological images

Causal inference in the medical domain: a survey

Contact Info

Product

Resources

About