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Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection

Müşerref Duygu Saçar Demirci, Jens Allmer
2017 Journal of Integrative Bioinformatics  
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

Müşerref Duygu Saçar Demirci, Jens Allmer
2017 PeerJ  
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

Malik Yousef, Jens Allmer, Waleed Khalifa
2015 Journal of Biomedical Science and Engineering  
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]

Shyaman Jayasundara, Sandali Lokuge, Puwasuru Ihalagedara, Damayanthi Herath
2021 arXiv   pre-print
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

Md. Eamin Rahman, Rashedul Islam, Shahidul Islam, Shakhinur Islam Mondal, Md. Ruhul Amin
2012 Genomics  
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

Müşerref Duygu Saçar, Caner Bağcı, Jens Allmer
2014 Genomics, Proteomics & Bioinformatics  
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

Adam Gudyś, Michał Szcześniak, Marek Sikora, Izabela Makałowska
2013 BMC Bioinformatics  
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

Müşerref Duygu Saçar Demirci, Jan Baumbach, Jens Allmer
2017 Nature Communications  
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

Jiandong Ding, Shuigeng Zhou, Jihong Guan
2010 BMC Bioinformatics  
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

Tareq B. Malas
2012 The Open Biology Journal  
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]

Georgios K Georgakilas, Andrea Grioni, Konstantinos G Liakos, Eliska Malanikova, Fotis C Plessas, Panagiotis Alexiou
2019 bioRxiv   pre-print
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

Malik Yousef, Jens Allmer, Waleed Khalifa
2016 Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies  
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

Mark Menor, Kyungim Baek, Guylaine Poisson
2015 International Journal of Molecular Sciences  
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

Kevin M Elias, Wojciech Fendler, Konrad Stawiski, Stephen J Fiascone, Allison F Vitonis, Ross S Berkowitz, Gyorgy Frendl, Panagiotis Konstantinopoulos, Christopher P Crum, Magdalena Kedzierska, Daniel W Cramer, Dipanjan Chowdhury
2017 eLife  
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]

Malik Yousef, Naim Najami, Walid Khaleif
2011 Systems and Computational Biology - Molecular and Cellular Experimental Systems  
., (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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