Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali; Zamani, Behzad

doi:10.1016/j.fob.2015.10.003

Cited by 12 publications

(7 citation statements)

References 41 publications

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“…Remarkably, miRNAs have been associated with diverse biological processes, including cell proliferation, differentiation, apoptosis, development, the immune response, tumorigenesis, DNA methylation, and chromatin modification (Chen et al, 2014;Ghildiyal & Zamore, 2009;Girard, Jacquemin, Munnich, Lyonnet, & Henrion-Caude, 2008;Moazed, 2009;O'Connell, Rao, Chaudhuri, & Baltimore, 2010;Xu, Zhu, et al, 2012). To date, more than 1,500 miRNAs encoding genes have been identified which regulate the activity of more than half of all protein-coding genes in humans (Rabiee-Ghahfarrokhi, Rafiei, Niknafs, & Zamani, 2015).…”

mentioning

confidence: 99%

Hedgehog signaling pathway: Epigenetic regulation and role in disease and cancer development

Fattahi

Langroudi

Akhavan‐Niaki

2018

Journal Cellular Physiology

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The evolutionarily conserved Hedgehog (Hh) signaling pathway have critical roles in development and homeostasis of tissues. Under physiological conditions, Hh is controlled at different levels via stem cell maintenance and tissue regeneration. Aberrant activation of this signaling pathway may occur in a wide range of human diseases including different types of cancer. In this review we present a concise overview on the key genes composing Hh signaling pathway and provide recent advances on the molecular mechanisms that regulate Hh signaling pathway from extracellular and receptors to the cytoplasmic and nuclear machinery with a highlight on the role of microRNAs. Furthermore, we focus on critical studies demonstrating dysregulation of the Hh pathway in human disease development, and potential therapeutic implications. Finally, we introduce recent therapeutic drugs acting as Shh signaling pathway inhibitors, including those in clinical trials and preclinical studies.

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mentioning

confidence: 99%

Hedgehog signaling pathway: Epigenetic regulation and role in disease and cancer development

Fattahi

Langroudi

Akhavan‐Niaki

2018

Journal Cellular Physiology

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“…This classification helps to create a new approach to discover new miRNAs. In [33], the authors use the decision tree method to improve the accuracy of prediction. The authors discover the relationship between the data where the classified data are extracted based on rules.…”

Section: B Researches Based On Machine Learning Methodsmentioning

confidence: 99%

Increasing the Target Prediction Accuracy of MicroRNA Based on Combination of Prediction Algorithms

Shatnawi¹,

Alhammouri²,

Kholoud³

2016

ijacsa

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Abstract-MicroRNA is an oligonucleotide that plays a role in the pathogenesis of several diseases (mentioning Cancer). It is a non-coding RNA that is involved in the control of gene expression through the binding and inhibition of mRNA.In this study, three algorithms were implemented in WEKA software using two testing modes to analyze five datasets of miRNA families. The data mining techniques are used to compare the interactions of miRNA-mRNA that it either belongs to the same gene-family or to different families, and to establish a biological scheme that explains how the biological parameters are involved or less involved in miRNA-mRNA prediction.The factors that were involved in the prediction process includs match, mismatch, bulge, loop, and score to represent the binding characteristics, while the position, 3'UTR length, and chromosomal location and chromosomal categorizations represent the characteristics of the target mRNA. These attributes can provide an empirical guidance for study of specific miRNA family to scan the whole human genome for novel targets. This research provides promising results that can be utilized for current and future research in this field.

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“…Behzad Rabiee-Ghahfarrokhi use genetic algorithm with combination of C4.5 decision tree to predict microRNA molecules . The superiority of the method is evaluated with TarBase database (version 3.0) with 10-fold cross validation technique and achieves 93.9% classification accuracy [9]. Privacy-Preserving for ID3 decision tree proposed using Vertical partitioning technique [10] similarly Hari seetha et.al discussed a vertical partitioning approach using SVM classifier where features of the dataset are divided based on the mutual exclusive property [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

Nutheti¹,

Hasyagar²,

Shettar³

et al. 2020

IJMSC

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Decision tree is a known classification technique in machine learning. It is easy to understand and interpret and widely used in known real world applications. Decision tree (DT) faces several challenges such as class imbalance, overfitting and curse of dimensionality. Current study addresses curse of dimensionality problem using partitioning technique. It uses partitioning technique, where features are divided into multiple sets and assigned into each block based on mutual exclusive property. It uses Genetic algorithm to select the features and assign the features into each block based on the ferrer diagram to build multiple CART decision tree. Majority voting technique used to combine the predicted class from the each classifier and produce the major class as output. The novelty of the method is evaluated with 4 datasets from UCI repository and shows approximately 9%, 3% and 5% improvement as compared with CART, Bagging and Adaboost techniques.

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Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree

Cited by 12 publications

References 41 publications

Hedgehog signaling pathway: Epigenetic regulation and role in disease and cancer development

Hedgehog signaling pathway: Epigenetic regulation and role in disease and cancer development

Increasing the Target Prediction Accuracy of MicroRNA Based on Combination of Prediction Algorithms

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

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