GMFR-CNN

Wong, Yu Shiong; Lee, Nung Kion; Omar, Norshafarina

doi:10.1145/3029375.3029380

Search citation statements

Order By: Relevance

Paper Sections

Select...

Existing Enhancer Prediction Methods1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

2023

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remarkably, some methods [62,65,[122][123][124][125][126][127][128] have been excluded because of a lack of clarity regarding training or model development.…”

Section: Existing Enhancer Prediction Methodsmentioning

confidence: 99%

A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome

Phan

He³

et al. 2023

Proteomics

View full text Add to dashboard Cite

show abstract

“…Remarkably, some methods [62,65,[122][123][124][125][126][127][128] have been excluded because of a lack of clarity regarding training or model development.…”

Section: Existing Enhancer Prediction Methodsmentioning

confidence: 99%

A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome

Phan

He³

et al. 2023

Proteomics

View full text Add to dashboard Cite

show abstract

Computational biology: deep learning

Jones

Alasoo

Fishman

et al. 2017

Emerging Topics in Life Sciences

View full text Add to dashboard Cite

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

show abstract