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Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection
2017
Journal of Integrative Bioinformatics
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Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. ...
To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. ...
Acknowledgements The work was supported by the Scientific and Technological Research Council of Turkey [grant numbers 113E326 and 114Z177] and by a research grant by Amazon Web Services to JA. ...
doi:10.1515/jib-2017-0032
pmid:28753538
fatcat:koztgftkdzdgbdjrbfycv6zvae
Delineating the impact of machine learning elements in pre-microRNA detection
2017
PeerJ
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Furthermore, we established two new negative datasets and analyzed the impact of using them for training and testing. ...
used for model establishment. ...
Machine learning algorithms like support vector machine (SVM), naïve Bayes (NB), random forest (RF), and many more have been employed to establish models for pre-miRNA detection (Ng & Mishra, 2007; Jiang ...
doi:10.7717/peerj.3131
pmid:28367373
pmcid:PMC5374968
fatcat:dhvth22zzferljshimtyfmc3he
Sequence Motif-Based One-Class Classifiers Can Achieve Comparable Accuracy to Two-Class Learners for Plant microRNA Detection
2015
Journal of Biomedical Science and Engineering
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To overcome this limitation, machine learning has been applied to miRNA detection. In general binary learning (two-class) approaches are applied to miRNA discovery. ...
For machine learning, miRNAs need to be transformed into a feature vector and some currently used features like minimum free energy vary widely in the case of plant miRNAs. ...
Acknowledgements The work was supported by the Scientific and Technological Research Council of Turkey [grant number 113E326] to JA. Conflict of interest: none declared. ...
doi:10.4236/jbise.2015.810065
fatcat:37yizm54tjglleovpgtlqzq27q
Machine learning for plant microRNA prediction: A systematic review
[article]
2021
arXiv
pre-print
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This systematic review focuses on the machine learning methods developed for miRNA identification in plants. ...
With the involvement of data science and machine learning in biology, multiple research studies have been conducted to find microRNAs with different computational methods and different miRNA features. ...
Acknowledgments The authors would like to thank Asst. Prof. Indika Kahanda, School of Computing, University of North Florida, USA and Mrs. ...
arXiv:2106.15159v1
fatcat:5ykoisyhi5dbditcv7am44eyqi
MiRANN: A reliable approach for improved classification of precursor microRNA using Artificial Neural Network model
2012
Genomics
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This article has proposed a reliable model -miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. ...
MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance. ...
Nazmul Alam for the implementation of ScorePin algorithm to construct negative hairpin set. ...
doi:10.1016/j.ygeno.2012.02.001
pmid:22349176
fatcat:kgzgr7wq3vaitberxj3ywn4t2a
Computational Prediction of MicroRNAs from Toxoplasma gondii Potentially Regulating the Hosts' Gene Expression
2014
Genomics, Proteomics & Bioinformatics
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Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. ...
We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. ...
Acknowledgements This work was supported by the Scientific and Technological Research Council of Turkey (Grant No. 113E326) awarded to JA. ...
doi:10.1016/j.gpb.2014.09.002
pmid:25462155
pmcid:PMC4411416
fatcat:h3y3dwffyvfazbtazowkwyqhfe
HuntMi: an efficient and taxon-specific approach in pre-miRNA identification
2013
BMC Bioinformatics
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It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. ...
Machine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. ...
Then, using a probabilistic model, the hairpins are scored based on the compatibility of the position and frequency of sequenced reads with the secondary structure of the pre-miRNA. ...
doi:10.1186/1471-2105-14-83
pmid:23497112
pmcid:PMC3686668
fatcat:bmz56hdch5fero5cknsb4blgrm
On the performance of pre-microRNA detection algorithms
2017
Nature Communications
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Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes. ...
In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. ...
Acknowledgements This work was supported by the Scientific Research Council of Turkey (TUBITAK, Grant No: 113E326). ...
doi:10.1038/s41467-017-00403-z
pmid:28839141
pmcid:PMC5571158
fatcat:ntux7m5iq5eypkfyrkwn522jki
MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features
2010
BMC Bioinformatics
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Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those ...
A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs ...
Acknowledgements The authors appreciate Prof. Malik Yousef and Mr. Manohara Rukshan Batuwita for predicting the test sequences. ...
doi:10.1186/1471-2105-11-s11-s11
pmid:21172046
pmcid:PMC3024864
fatcat:bfspbeuj3nf5fowlpylvu3br6m
Computational Tools for Genome-Wide miRNA Prediction and Study
2012
The Open Biology Journal
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MicroRNAs bind a seed sequence to the 3´-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein ...
However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. ...
ACKNOWLEDGEMENTS T.R. and T.M. are supported by King Abdullah University of Science and Technology. ...
doi:10.2174/1874196701205010023
fatcat:qzbzm6kbarap3ctqlr7jutplnu
MuStARD: Deep Learning for intra- and inter-species scanning of functional genomic patterns
[article]
2019
bioRxiv
pre-print
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We demonstrate that MuStARD can be trained without changes on different classes of human small RNA loci (pre-microRNAs and snoRNAs) and accurately build prediction models for both, outperforming state ...
Deep Learning is a family of Machine Learning algorithms recently applied to a variety of pattern recognition problems. ...
ACKNOWLEDGEMENT We would like to acknowledge 'MetaCentrum NGI' for providing computational infrastructure necessary for running parts of the analyses presented in this study. ...
doi:10.1101/547679
fatcat:32rfjjvhvfadrcoswfsy5b7xgi
Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies
2016
Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies
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Traditionally, machine learning algorithms build classification models from positive and negative examples. ...
Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. ...
ACKNOWLEDGEMENTS The work was supported by the Scientific and Technological Research Council of Turkey [grant number 113E326] to JA. ...
doi:10.5220/0005701602160225
dblp:conf/biostec/YousefAK16
fatcat:htjihucumrbdvcjiqy737kyp4a
Prediction of Mature MicroRNA and Piwi-Interacting RNA without a Genome Reference or Precursors
2015
International Journal of Molecular Sciences
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The discovery of novel microRNA (miRNA) and piwi-interacting RNA (piRNA) is an important task for the understanding of many biological processes. ...
Most of the available miRNA and piRNA identification methods are dependent on the availability of the organism's genome sequence and the quality of its annotation. ...
The paper's contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Conflicts of Interest The authors declare no conflict of interest. ...
doi:10.3390/ijms16011466
pmid:25580537
pmcid:PMC4307313
fatcat:hrildyi4mfba5gquiaqujsvpvi
Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
2017
eLife
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After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3–97.6%) and ...
The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. ...
Acknowledgement The authors wish to acknowledge funding support from the Robert and Deborah First Family Fund (KME, RSB, DC), NICHD K12HD13015 (KME), the Ruth N White Research ...
doi:10.7554/elife.28932
pmid:29087294
pmcid:PMC5679755
fatcat:isj6ivxcl5cdjm2cy55ynj3zya
MicroRNA Identification Based on Bioinformatics Approaches
[chapter]
2011
Systems and Computational Biology - Molecular and Cellular Experimental Systems
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., (2008) presented a study using one-class machine learning for microRNA using only positive data to build the classifier (One-ClassMirnaFind [58] ). ...
Two machine learning approaches have recently appeared for identifying microRNAs without the necessity of defining a negative class. ...
The two volumes of this book presents a series of high-quality research or review articles in a timely fashion to this emerging research field of our scientific community. ...
doi:10.5772/22587
fatcat:m6y6surtcjbcvpjzbmbd4c2nji
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