scholarly journals MatPred: Computational Identification of Mature MicroRNAs within Novel Pre-MicroRNAs

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jin Li ◽  
Ying Wang ◽  
Lei Wang ◽  
Weixing Feng ◽  
Kuan Luan ◽  
...  
Keyword(s):  
Mature Mirnas ◽  
Minimum Free Energy ◽  
Mirna Biogenesis ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Mature Micrornas ◽  
Low Levels ◽  
Flanking Regions ◽  
Precursor Transcript ◽  
Insight Into

Background.MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement.Methodology/Principal Findings.Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods.Conclusions.MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.

scholarly journals Isolation and sequencing of AGO-bound RNAs reveals characteristics of mammalian stem-loop processing in vivo

2018 ◽  
Author(s):  
Ian M. Silverman ◽  
Sager J. Gosai ◽  
Nicholas Vrettos ◽  
Shawn W. Foley ◽  
Nathan D. Berkowitz ◽  
...  
Keyword(s):  
Mature Mirnas ◽  
Human Cells ◽  
Processing Efficiency ◽  
Stem Loop ◽  
Mature Micrornas ◽  
Mouse Cells ◽  
Low Levels ◽  
Short Hairpin Rnas ◽  
Human And Mouse

ABSTRACTMicroRNA precursors (pre-miRNAs) are short hairpin RNAs that are rapidly processed into mature microRNAs (miRNAs) in the cytoplasm. Due to their low abundance in cells, sequencing-based studies of pre-miRNAs have been limited. We successfully enriched for and deep sequenced pre-miRNAs in human cells by capturing these RNAs during their interaction with Argonaute (AGO) proteins. Using this approach, we detected > 350 pre-miRNAs in human cells and > 250 pre-miRNAs in a reanalysis of a similar study in mouse cells. We uncovered widespread trimming and non-templated additions to the 3’ ends of pre- and mature miRNAs. Additionally, we created an index for microRNA precursor processing efficiency. This analysis revealed a subset of pre-miRNAs that produce low levels of mature miRNAs despite abundant precursors, including an annotated miRNA in the 5’ UTR of the DiGeorge syndrome critical region 8 mRNA transcript. This led us to search for AGO-associated stem-loops originating from other mRNA species, which identified hundreds of putative pre-miRNAs derived from human and mouse mRNAs. In summary, we provide a wealth of information on mammalian pre-miRNAs, and identify novel microRNA and microRNA-like elements localized in mRNAs.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals The Integrator complex regulates microRNA abundance through RISC loading

2021 ◽  
Author(s):  
Nina Kirstein ◽  
Sadat Dokaneheifard ◽  
Pradeep Reddy Cingaram ◽  
Monica Guiselle Valencia ◽  
Felipe Beckedorff ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Mature Mirnas ◽  
Mirna Biogenesis ◽  
Important Regulator ◽  
Rna Maturation ◽  
Nascent Transcripts ◽  
Post Transcriptional Regulation ◽  
Mirna Duplex

MicroRNA (miRNA) homeostasis is crucial for the post-transcriptional regulation of their target genes and miRNA dysregulation has been linked to multiple diseases, including cancer. The molecular mechanisms underlying miRNA biogenesis from processing of primary miRNA transcripts to formation of mature miRNA duplex are well understood. Loading of miRNA duplex into members of the Argonaute (Ago) protein family, representing the core of the RNA-induced silencing complex (RISC), is pivotal to miRNA-mediated gene silencing. The Integrator complex has been previously shown to be an important regulator of RNA maturation, RNA polymerase II pause-release, and premature transcriptional termination. Here, we report that loss of Integrator results in global diminution of mature miRNAs. By incorporating 4-Thiouridine (s4U) in nascent transcripts, we traced miRNA fate from biogenesis to stabilization and identified Integrator to be essential for proper miRNA assembly into RISC. Enhanced UV crosslinking and immunoprecipitation (eCLIP) of Integrator confirms a robust association with mature miRNAs. Indeed, Integrator potentiates Ago2-mediated cleavage of target RNAs. These findings highlight an essential role for Integrator in miRNA abundance and RISC function.

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