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Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features

Shiva Kumar, Faraz A Ansari, Vinod Scaria
2009 Virology Journal  
The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.  ...  In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are  ...  Samir K Brahmachari for continuous guidance.  ... 
doi:10.1186/1743-422x-6-129 pmid:19691855 pmcid:PMC2743665 fatcat:h76hxa5jbjfufge77dikcourfi

Computational Prediction of microRNAs and their Targets

Salim A Vinod Chandra
2014 Journal of Proteomics & Bioinformatics  
Support Vector Machine (SVM) as machine learning technique has been widely in microRNA predictions.  ...  This triplet structure, on each hairpin is counted and used as input feature vector of Support Vector Machine(SVM) in the case TripletSVM.  ... 
doi:10.4172/jpb.1000320 fatcat:hwxjs3uzerbyzg4plhpeq5nkmu

MicroRNA Identification Based on Bioinformatics Approaches [chapter]

Malik Yousef, Naim Najami, Walid Khaleif
2011 Systems and Computational Biology - Molecular and Cellular Experimental Systems  
Pfeffer, et al., (2005) used support vector machines (SVMs) for predicting conserved miRNAs in herpesviruses.  ...  Support Vector Machines (SVMs) are widely used machine learning algorithms developed by Vapnik [38] .  ...  order to correctly reference this scholarly work, feel free to copy and paste the following:  ... 
doi:10.5772/22587 fatcat:m6y6surtcjbcvpjzbmbd4c2nji

Prediction of human microRNA hairpins using only positive sample learning

Dang Hung Tran, Tho Hoan Pham, Kenji Satou, Tu Bao Ho
2008 Journal of Biomedical Science and Engineering  
In this paper, we introduce a one-class support vector machine (SVM) method to predict miRNA hairpins among the hairpin structures.  ...  Most existing computational methods for predicting miRNA hairpins are based on a two-class classifier to distinguish between miRNA hairpins and other sequence segments with hairpin structures.  ...  ACKNOWLEDGMENTS The research described in this paper was partially supported by the Institute for Bioinformatics Research and Development of the Japan Science and Technology Agency, and by COE project  ... 
doi:10.4236/jbise.2008.12023 fatcat:qwzbvspr4zac7bfwalhgyxrna4

Deep Multiple Kernel Learning for Prediction of MicroRNA Precursors

Hengyue Shi, Dong Wang, Peng Wu, Yi Cao, Yuehui Chen
2021 Scientific Programming  
RBF kernel support vector machines (RBF-SVMs) and shallow multiple kernel support vector machines (MK-SVMs) are often used in microRNA precursors prediction.  ...  Therefore, the prediction of microRNAs is of great significance for basic biological research and disease treatment. MicroRNA precursors are the necessary stage of microRNA formation.  ...  Kernels and Support Vector Machine. e kernels are the inner products of the mapping relationship.  ... 
doi:10.1155/2021/9969282 doaj:76742b705e824cbb9fc919e4989838a4 fatcat:ugf6nun3ufewjjmzyih6q56bku

Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

Malik Yousef, Jens Allmer, Waleed Khalifa
2016 Journal of Intelligent Learning Systems and Applications  
For the prediction of pre-miRNAs, usually machine learning approaches are employed.  ...  We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.  ...  Acknowledgements The work was supported by the Ministry of Science, Israel to MY and WK. and the Scientific and Technological Research Council of Turkey [113E326 to JA].  ... 
doi:10.4236/jilsa.2016.81002 fatcat:45bueolypjdfvjkaadvkrlwure

Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data

J. Hertel, P. F. Stadler
2006 Bioinformatics  
Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence  ...  Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures.  ...  ACKNOWLEDGEMENTS Financial support by the German DFG in the framework of the Bioinformatics Initiative (BIZ-6/1-2) and the SPP 'Metazoan Deep Phylogeny' is gratefully acknowledged.  ... 
doi:10.1093/bioinformatics/btl257 pmid:16873472 fatcat:ju4mlv4vrven5jakgg3prz3sla

Identification of microRNA precursors with new sequence-structure features

Ying-Jie Zhao, Qing-Shan Ni, Zheng-Zhi Wang
2009 Journal of Biomedical Science and Engineering  
of pulled stem) as features vector of Support Vector Machine (SVM).  ...  Identification of precursor microRNA (pre-miRNA) is essential step to target microRNA in whole genome.  ...  Moreover, most of de novo methods employed machine learning techniques to identify pre-miRNAs, such as Hidden Markov Models (HMM) [21, 22] , Support Vector Machine (SVM) [17] [18] [19] 23] , Naïve Bayes  ... 
doi:10.4236/jbise.2009.28091 fatcat:ztg6hfvvmjbofeypt3gzg6o43y

DP-miRNA: An improved prediction of precursor microRNA using deep learning model

Jaya Thomas, Sonia Thomas, Lee Sael
2017 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)  
The deep neural network based classification outperformed support vector machine, neural network, naive Bayes classifiers, k-nearest neighbors, random forests as well as hybrid systems combining SVM and  ...  This paper proposes a deep learning based classification model for predicting precursor miRNA sequence that contains the miRNA sequence.  ...  Many approaches have been developed using naive Bayes classifier (NBC), artificial neural networks (ANN), support vector machines (SVM), and random forests (RF).  ... 
doi:10.1109/bigcomp.2017.7881722 dblp:conf/bigcomp/ThomasTS17 fatcat:gmaolgiwhfbrzevuwz7kynxpjy

MiRmat: Mature microRNA Sequence Prediction

Chenfeng He, Ying-Xin Li, Guangxin Zhang, Zuguang Gu, Rong Yang, Jie Li, Zhi John Lu, Zhi-Hua Zhou, Chenyu Zhang, Jin Wang, Lukasz Kurgan
2012 PLoS ONE  
MiRmat outperforms other state-of-the-art methods and has a high degree of efficacy for the prediction of mature microRNA sequences of vertebrates.  ...  Based on the analysis of microRNAs from 12 species, we found that the patterns of free energy profiles are conserved among vertebrate microRNA hairpins.  ...  Acknowledgments The authors would acknowledge the Center of High Performance Computation of Nanjing University for the support of computational resources.  ... 
doi:10.1371/journal.pone.0051673 pmid:23300555 pmcid:PMC3531441 fatcat:hkwo2wxv5fcarofct25lccmkra

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Chenghai Xue, Fei Li, Tao He, Guo-Ping Liu, Yanda Li, Xuegong Zhang
2005 BMC Bioinformatics  
Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data.  ...  Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins.  ...  Acknowledgements The authors wish to thank Xiaowo Wang, Jin Gu, Jing Zhang, Yanbin Yin, Zuozhou Chen and Qinghua Cui for helpful discussions.  ... 
doi:10.1186/1471-2105-6-310 pmid:16381612 pmcid:PMC1360673 fatcat:s7bt7oq5pvekxdhigh7tweggji

miREval 2.0: a web tool for simple microRNA prediction in genome sequences

Dadi Gao, Robert Middleton, John E. J. Rasko, William Ritchie
2013 Computer applications in the biosciences : CABIOS  
Result: We have developed miREval 2.0, an online tool that can simultaneously search up to 100 sequences for novel microRNAs (miRNAs) in multiple organisms. miREval 2.0 uses multiple published in silico  ...  ACKNOWLEDGEMENTS The authors thank their colleagues at the Gene and Stem Cell Therapy Lab of Centenary Institute for useful comments on Web site display. Conflict of Interest: none declared.  ...  miRNA prediction SVM prediction of miRNAs: Support vector machines (SVM) are an efficient machine learning technique used to predict miRNAs (Xue et al., 2005) .  ... 
doi:10.1093/bioinformatics/btt545 pmid:24048357 pmcid:PMC5994938 fatcat:vc5tgplgdzezfobzzenubqdo3e

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

Jaya Thomas, Lee Sael
2017 arXiv   pre-print
The deep neural network based classification outperformed support vector machine, neural network, naive Baye's classifiers, k-nearest neighbors, random forests, and a hybrid system combining support vector  ...  These limitations inspire the development of computational methods for predicting the miRNAs.  ...  BACKGROUND Machine learning approaches are the most commonly used for miRNA prediction.  ... 
arXiv:1704.03834v1 fatcat:uzs2pkfcavhltj5h2y3qxyz3ua

Circular RNA–MicroRNA–MRNA interaction predictions in SARS-CoV-2 infection

Yılmaz Mehmet Demirci, Müşerref Duygu Saçar Demirci
2021 Journal of Integrative Bioinformatics  
In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection.  ...  In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures.  ...  workflow was created by using 70% learning and 30% testing ratios and three different classifiers; random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP) were trained with 100  ... 
doi:10.1515/jib-2020-0047 pmid:33725751 fatcat:li7ckr5f2veqvfktmmp7dio5zq

ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors

Kai-Yao Huang, Tzong-Yi Lee, Yu-Chuan Teng, Tzu-Hao Chang
2015 BMC Bioinformatics  
Support vector machine and random forest models were established using 54 features from RNA sequences and secondary structural information.  ...  Thus far, a specific predictive model for viral miRNA identification has yet to be developed.  ...  Acknowledgements The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under grant no.  ... 
doi:10.1186/1471-2105-16-s1-s9 pmid:25708359 pmcid:PMC4331708 fatcat:dsn6vcwd4bf5ljaieg2yc2ikj4
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