Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

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Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
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"Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features"
written by Malik Yousef, Jens Allmer, Waleed Khalifa,
published by Journal of Intelligent Learning Systems and Applications, Vol.8 No.1, 2016

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	44 Current Challenges in miRNomics
	Moussa… - miRNomics, 2022

[2]	miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification
	2021

[3]	Machine learning for plant microRNA prediction: A systematic review
	2021

[4]	miRNAFinder: A pre-microRNA classifier for plants and analysis of feature impact
	2020

[5]	Pre-Cursor microRNAs from Different Species classification based on features extracted from the image
	2020

[6]	Computational methods for the ab initio identification of novel microRNA in plants: a systematic review
	2019

[7]	Discovery and functional annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach
	2019

[8]	Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach
	2019

[9]	Species Categorization via MicroRNAs
	2018

[10]	一种改进的 microRNA 预测模型集成方法
	2018

[11]	Distinguishing Between MicroRNA Targets From Diverse Species Using Sequence Motifs And K-Mers
	2017

[12]	MicroRNA categorization using sequence motifs and k-mers
	2017

[13]	Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers
	EURASIP Journal on Advances in Signal Processing, 2017

[14]	Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers.
	2017

[15]	The impact of feature selection on one and two-class classification performance for plant microRNAs
	PeerJ, 2016

[16]	Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants
	Advances in bioinformatics, 2016

[1]	miRNomics Methods in Molecular Biology, 2022 DOI:10.1007/978-1-0716-1170-8_19

[2]	miRNAFinder: A pre-microRNA classifier for plants and analysis of feature impact 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020 DOI:10.1109/CIBCB48159.2020.9277723

[3]	Computational methods for the ab initio identification of novel microRNA in plants: a systematic review PeerJ Computer Science, 2019 DOI:10.7717/peerj-cs.233

[4]	Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants Advances in Bioinformatics, 2016 DOI:10.1155/2016/5670851

[5]	The impact of feature selection on one and two-class classification performance for plant microRNAs PeerJ, 2016 DOI:10.7717/peerj.2135

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