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

Phan, Le Thi; Oh, Changmin; He, Tong‐Chuan; Manavalan, Balachandran

doi:10.1002/pmic.202200409

Cited by 5 publications

(1 citation statement)

References 146 publications

(296 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, pregnant women and fetuses have a special relationship in which any physical changes in the mother will affect the fetus and even affect its future growth and development (pbg → hdop → ngr in KDB). Neonatal gestational diabetes affects neonatal lung development, neonatal lung maturation and associated lung diseases (np → pbg; np → hdop in TAN), with a high incidence of neonatal pneumonia, neonatal respiratory distress syndrome and bronchopulmonary dysplasia in gestational diabetes patients [ 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

Lin,

Chen,

Zhong

et al. 2023

BMC Med Res Methodol

View full text Add to dashboard Cite

Objective To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia. Method Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set. Results In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%). Conclusion KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.

show abstract

Section: Discussionmentioning

confidence: 99%

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

Lin,

Chen,

Zhong

et al. 2023

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

Machine and Deep Learning Methods for Predicting 3D Genome Organization

Wall,

Nguyen,

Harrell

et al. 2024

Methods in Molecular Biology

View full text Add to dashboard Cite

Enhancer target prediction: state-of-the-art approaches and future prospects

Umarov,

Hon

2023

Biochemical Society Transactions

View full text Add to dashboard Cite

Enhancers are genomic regions that regulate gene transcription and are located far away from the transcription start sites of their target genes. Enhancers are highly enriched in disease-associated variants and thus deciphering the interactions between enhancers and genes is crucial to understanding the molecular basis of genetic predispositions to diseases. Experimental validations of enhancer targets can be laborious. Computational methods have thus emerged as a valuable alternative for studying enhancer–gene interactions. A variety of computational methods have been developed to predict enhancer targets by incorporating genomic features (e.g. conservation, distance, and sequence), epigenomic features (e.g. histone marks and chromatin contacts) and activity measurements (e.g. covariations of enhancer activity and gene expression). With the recent advances in genome perturbation and chromatin conformation capture technologies, data on experimentally validated enhancer targets are becoming available for supervised training of these methods and evaluation of their performance. In this review, we categorize enhancer target prediction methods based on their rationales and approaches. Then we discuss their merits and limitations and highlight the future directions for enhancer targets prediction.

show abstract

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

Cited by 5 publications

References 146 publications

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

Machine and Deep Learning Methods for Predicting 3D Genome Organization

Enhancer target prediction: state-of-the-art approaches and future prospects

Contact Info

Product

Resources

About