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49
PF: Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data
- Bioinformatics
, 2006
"... doi:10.1093/bioinformatics/btl257 ..."
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Identification and characterization of small RNAs involved in RNA silencing
- FEBS Lett
, 2005
"... Abstract Double-stranded RNA (dsRNA) is a potent trigger of sequence-specific gene silencing mechanisms known as RNA silencing or RNA interference. The recognition of the target sequences is mediated by ribonucleoprotein complexes that contain 21- to 28-nucleotide (nt) guide RNAs derived from proces ..."
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Cited by 41 (0 self)
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Abstract Double-stranded RNA (dsRNA) is a potent trigger of sequence-specific gene silencing mechanisms known as RNA silencing or RNA interference. The recognition of the target sequences is mediated by ribonucleoprotein complexes that contain 21- to 28-nucleotide (nt) guide RNAs derived from processing of the trigger dsRNA. Here, we review the experimental and bioinformatic approaches that were used to identify and characterize these small RNAs isolated from cells and tissues. The identification and characterization of small RNAs and their expression
Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier
- Bioinformatics
, 2006
"... 2003 developed a computational method using sequence Motivation: Numerous computational methodologies utilize conservation and structural similarity to predict miRNAs in the techniques based on sequence conservation and/or structural similarity for microRNA gene prediction. In this study we describe ..."
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Cited by 35 (1 self)
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2003 developed a computational method using sequence Motivation: Numerous computational methodologies utilize conservation and structural similarity to predict miRNAs in the techniques based on sequence conservation and/or structural similarity for microRNA gene prediction. In this study we describe a new technique, which is applicable across several species, for predicting microRNA genes. This technique is based on machine learning, using the Naïve Bayes classifier. This computational procedure automatically generates a model from the input or training data, which is the sequence and structure of known microRNAs from a variety of species. Results: This study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for microRNA C.elegans genome, while Lai et al, 2003 developed a different computational tool called miRseeker. These efforts have recently been reviewed by Bartel (Bartel, 2004). Others used homologue searches for revealing paralog and ortholog miRNAs
Principles and Limitations of Computational MicroRNA Gene and Target Finding
, 2007
"... In 2001 there were four PubMed entries matching the word ‘‘microRNA’’ (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of ..."
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Cited by 28 (3 self)
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In 2001 there were four PubMed entries matching the word ‘‘microRNA’’ (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of sequence data being generated, the need for analysis by computational approaches is obvious. Here, we review the general principles used in computational gene and target finding, and discuss the strengths and weaknesses of the methods. Several methods rely on detection of evolutionary conserved candidates, but recent methods have challenged this paradigm by simultaneously searching for the gene and the corresponding target(s). Whereas the early methods made predictions based on sets of hand-derived rules from precursor-miRNA structure or observed target–miRNA interactions, recent methods apply machine learning techniques. Even though these methods are already powerful, the amount of data they rely on is still limited. Since it is evident that data are continuously being generated, it must be anticipated that these methods will further improve their performance.
MaturePred: Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAs
"... Background: MicroRNAs (miRNAs) are a set of short (19,24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish re ..."
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Background: MicroRNAs (miRNAs) are a set of short (19,24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates. Methodology/Principal Findings: A miRNA:miRNA * duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA * duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA * duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with
Analysing reviews
- in the Web 2.0: Small and medium hotels in Portugal. Tourism Management
, 2012
"... Role and regulation of the forkhead transcription ..."
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Using base pairing probabilities for MiRNA recognition
- In SYNASC ’08: Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
, 2008
"... Abstract—We designed a new SVM for microRNA identification, whose novelty consist in the fact that many of its features incorporate the base-pairing probabilities provided by McCaskill’s algorithm. Comparisons with other SVMs for microRNA identification prove that our SVM obtains competitive results ..."
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Abstract—We designed a new SVM for microRNA identification, whose novelty consist in the fact that many of its features incorporate the base-pairing probabilities provided by McCaskill’s algorithm. Comparisons with other SVMs for microRNA identification prove that our SVM obtains competitive results. One of the advantages of our approach is that it makes no use of so-called normalised features which are based on sequence shuffling, which is a sensitive issue from the biological point of view. This also makes our approach much less time consuming. I.
MicroRNAs and Cancer-The Search Begins
- IEEE Transactions on Information Technology in Biomedicine
"... Abstract—For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much mor ..."
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Abstract—For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer. Index Terms—Cancer, computational prediction, microRNA genes. I.
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"... We designed a new SVM for microRNA identification, whose novelty is two-folded: firstly many of its features incorporate the base-pairing probabilities provided by Mc-Caskill’s algorithm, and secondly the classification performance is improved by using a certain similarity (“profile”based) measure b ..."
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We designed a new SVM for microRNA identification, whose novelty is two-folded: firstly many of its features incorporate the base-pairing probabilities provided by Mc-Caskill’s algorithm, and secondly the classification performance is improved by using a certain similarity (“profile”based) measure between the training and test microRNAs and a set of carefully chosen (“pivot”) RNA sequences. Comparisons with some of the best existing SVMs for microRNA identification prove that our SVM obtains truly competitive results.
