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Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

Malik Yousef, Dawit Nigatu, Dalit Levy, Jens Allmer, Werner Henkel
2017 EURASIP Journal on Advances in Signal Processing  
The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient.  ...  To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species' pre-miRNAs  ...  Funding The work was supported by the Scientific and Technological Research Council of Turkey (grant number 113E326), Zefat Academic College, and German Research Foundation (DFG).  ... 
doi:10.1186/s13634-017-0506-8 fatcat:eg4uvb4nvja5jdhsgmqxzqj5du

MicroRNA categorization using sequence motifs and k-mers

Malik Yousef, Waleed Khalifa, İlhan Erkin Acar, Jens Allmer
2017 BMC Bioinformatics  
Results: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned  ...  The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences.  ...  Funding The work was supported by the Scientific and Technological Research Council of Turkey [grant number 113E326] to JA and by Zefat Academic College to MY.  ... 
doi:10.1186/s12859-017-1584-1 pmid:28292266 pmcid:PMC5351198 fatcat:itjjzalzu5d4lmbsx4rxhsrbji

Classification of Precursor MicroRNAs from Different Species Based on K-mer Distance Features

Malik Yousef, Jens Allmer
2021 Algorithms  
of origin is known, and open up a new strategy for analyzing miRNA evolution.  ...  Combining the features leads to accurate classification for larger evolutionary distances. For example, categorizing Homo sapiens versus Brassicaceae leads to an accuracy of 93%.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a14050132 doaj:ef01af41922545cc878e3a182b36b050 fatcat:74ejw4ia7faxfgcduorginrige

Non-coding yet non-trivial: a review on the computational genomics of lincRNAs

Travers Ching, Jayson Masaki, Jason Weirather, Lana X. Garmire
2015 BioData Mining  
We review the emerging characteristics of lincRNAs, the experimental and computational approaches to identify lincRNAs, their known mechanisms of regulation, the computational methods and resources for  ...  Long intergenic non-coding RNAs (lincRNAs) represent one of the most mysterious RNA species encoded by the human genome.  ...  In recent years, machine learning based classification approaches have been used to detect lincRNAs [17, 27, [32] [33] [34] .  ... 
doi:10.1186/s13040-015-0075-z pmid:26697116 pmcid:PMC4687140 fatcat:6adlfaqsuzblri4w66mqrdl6rq

Deep Neural Network Based Precursor microRNA Prediction on Eleven Species [article]

Jaya Thomas, Lee Sael
2017 arXiv   pre-print
We propose a deep learning based classification model, called DP-miRNA, for predicting precursor miRNA sequence that contains the miRNA sequence.  ...  Also, experimental results are sensitive to the experimental environment. These limitations inspire the development of computational methods for predicting the miRNAs.  ...  Table III shows a comparative result of the proposed DP-miRNA against the common machine learning approach for miRNA prediction.  ... 
arXiv:1704.03834v1 fatcat:uzs2pkfcavhltj5h2y3qxyz3ua

miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes

P. W.C. Hsu
2006 Nucleic Acids Research  
The mature miRNA of the putative miRNA genes is accurately determined using a machine learning approach, mmiRNA.  ...  The miRNAMap also provides the expression profiles of the known miRNAs, cross-species comparisons, gene annotations and cross-links to other biological databases.  ...  ACKNOWLEDGEMENTS The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 94-2213-E-009-025.  ... 
doi:10.1093/nar/gkj135 pmid:16381831 pmcid:PMC1347497 fatcat:4du5wv54anbvlluwvgv76pagea

Computational analysis of regulatory mechanism and interactions of microRNAs

Takaya Saito, Pål Sætrom
2011 Zenodo  
To solve this possible fault, we developed a two step support vector machine (SVM) model.  ...  This discovery led to identification of many classes of functional ncRNAs. MicroRNA (miRNA) is a class of such ncRNAs with ∼22 nucleotides that are quite abundant and found in most eukaryotic cells.  ...  Some of these evaluation methods for machine learning are explained in the next chapter. 6 Machine learning theory and Support vector machine The support vector machine (SVM) is a machine learning technique  ... 
doi:10.5281/zenodo.4902326 fatcat:2z2um4cglfaydanvwtvzu3ufn4

Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery [chapter]

Yanju Zhang, Jeroen S. de Bruin, Fons J.
2010 Machine Learning  
Acknowledgements We would like to thank Dr. Erno Vreugdenhil for discussing some biological implications of the results and Peter van de Putten for suggestions on the use of WEKA.  ...  This research has been partially supported by the BioRange program of the Netherlands BioInformatics Centre (BSIK grant).  ...  We intend to transfer our approach to other groups of microRNAs as well as the broader application to the important model species. microRNAs (miRNAs) are a novel class of post-transcriptional gene expression  ... 
doi:10.5772/9140 fatcat:gedflwuorjdvtgrcmhgs2534lq

Computational tools for plant small RNA detection and categorization

Lionel Morgado, Frank Johannes
2017 Briefings in Bioinformatics  
Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary  ...  To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on  ...  the precursor(s); (iii) inspired by machine learning; (iv) rule-based and (v) target-centered.  ... 
doi:10.1093/bib/bbx136 pmid:29059285 fatcat:2cgfz3s4tfdx5oo2vwahhq2l3e

miTarget: microRNA target gene prediction using a support vector machine

Sung-Kyu Kim, Jin-Wu Nam, Je-Keun Rhee, Wha-Jin Lee, Byoung-Tak Zhang
2006 BMC Bioinformatics  
The functions of animal miRNAs are generally based on complementarity for their 5' components.  ...  It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features.  ...  Acknowledgements This work was supported by the National Research Laboratory program (M10412000095-04J0000-03610) of the Korean Ministry of Science and Technology and by a Seoul Science Fellowship from  ... 
doi:10.1186/1471-2105-7-411 pmid:16978421 pmcid:PMC1594580 fatcat:fbk34qt5frfrdkeygwltpxfqvy

Comprehensive overview and assessment of miRNA target prediction tools in human and drosophila melanogaster [article]

Muniba Faiza, Khushnuma Tanveer, Saman Fatihi, Yonghua Wang, Khalid Raza
2017 arXiv   pre-print
A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time their results are often inconsistent.  ...  MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking  ...  Some part of the manuscript is written during this period.  ... 
arXiv:1711.01632v1 fatcat:hfpalqpqyjgbbhutj6tgvk52mm

Computational analysis of regulatory mechanism and interactions of microRNAs

Takaya Saito
2021 figshare.com  
To solve this possible fault, we developed a two step support vector machine (SVM) model.  ...  This discovery led to identification of many classes of functional ncRNAs. MicroRNA (miRNA) is a class of such ncRNAs with ∼22 nucleotides that are quite abundant and found in most eukaryotic cells.  ...  Some of these evaluation methods for machine learning are explained in the next chapter. 6 Machine learning theory and Support vector machine The support vector machine (SVM) is a machine learning technique  ... 
doi:10.6084/m9.figshare.14923851.v1 fatcat:5toszhgiyrgqzik2jzdo2ihqxu

Comparison and integration of target prediction algorithms for microRNA studies

Yanju Zhang, Fons J Verbeek
2010 Journal of Integrative Bioinformatics  
Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed.  ...  microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression.  ...  This research has been partially supported by the BioRange program of the Netherlands Bioinformatics Centre (NBIC, BSIK grant).  ... 
doi:10.2390/biecoll-jib-2010-127 pmid:20375447 fatcat:bmaba6soevdt3kwweapqrvlbei

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

Buwani Manuweera, Gillian Reynolds, Indika Kahanda
2019 PeerJ Computer Science  
Study selection The search results were further filtered using the selection criteria that only included studies on novel plant miRNA identification using machine learning.  ...  Data sources Five databases were searched for relevant articles, according to a well-defined review protocol.  ...  ACKNOWLEDGEMENTS The authors would like to gratefully acknowledge the assistance provided for reviewing the manuscript by Dr.  ... 
doi:10.7717/peerj-cs.233 pmid:33816886 pmcid:PMC7924660 fatcat:u35l5jpsvfdd3okxjbacaoleee

The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

Nícia Rosário-Ferreira, Catarina Marques-Pereira, Manuel Pires, Daniel Ramalhão, Nádia Pereira, Victor Guimarães, Vítor Santos Costa, Irina S. Moreira
2021 BioChem  
TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms' application in its various steps.  ...  This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.  ...  The funding agencies had no role in the design of the study, in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/biochem1020007 fatcat:qve6xgoxuvbwzpqz6q45s7wlly
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