The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un type d'ARN non codants fonctionnels, les microARN (miARN), forment une classe d'ARN endogènes qui peuvent jouer un rôle régulateur important chez les animaux et les plantes en ciblant les transcrits pour la répression du clivage ou de la traduction. Des recherches sur des approches expérimentales et informatiques ont montré que les miARN sont effectivement impliqués dans le développement et la progression du cancer humain. Cependant, les miARN qui contribuent davantage à la distinction entre les échantillons (tissus) normaux et tumoraux sont encore indéterminés. Récemment, la technologie des micropuces à haut débit a été utilisée comme technique puissante pour mesurer le niveau d’expression des miARN dans les cellules. L'analyse de ces données d'expression peut nous permettre de déterminer les rôles fonctionnels des miARN dans les cellules vivantes. Dans cet article, nous présentons une méthode informatique pour (1) prédire les tissus tumoraux à l’aide de profils d’expression de miARN à haut débit ; (2) trouver les miARN informatifs qui montrent une forte distinction du niveau d'expression dans les tissus tumoraux. À cette fin, nous exécutons une méthode basée sur une machine à vecteurs de support (SVM) pour examiner en profondeur un ensemble de données d’expression de miARN récent. Les résultats expérimentaux montrent que la méthode basée sur SVM surpasse les autres méthodes d'apprentissage supervisé telles que les arbres de décision, les réseaux bayésiens et les réseaux neuronaux de rétropropagation. De plus, en utilisant les informations sur la cible des miARN et les annotations de Gene Ontology, nous avons montré que les miARN informatifs présentent des preuves solides liées à certains types de cancer humain, notamment le cancer du sein, du poumon et du côlon.
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Dang Hung TRAN, Tu Bao HO, Tho Hoan PHAM, Kenji SATOU, "MicroRNA Expression Profiles for Classification and Analysis of Tumor Samples" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 416-422, March 2011, doi: 10.1587/transinf.E94.D.416.
Abstract: One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.416/_p
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@ARTICLE{e94-d_3_416,
author={Dang Hung TRAN, Tu Bao HO, Tho Hoan PHAM, Kenji SATOU, },
journal={IEICE TRANSACTIONS on Information},
title={MicroRNA Expression Profiles for Classification and Analysis of Tumor Samples},
year={2011},
volume={E94-D},
number={3},
pages={416-422},
abstract={One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.},
keywords={},
doi={10.1587/transinf.E94.D.416},
ISSN={1745-1361},
month={March},}
Copier
TY - JOUR
TI - MicroRNA Expression Profiles for Classification and Analysis of Tumor Samples
T2 - IEICE TRANSACTIONS on Information
SP - 416
EP - 422
AU - Dang Hung TRAN
AU - Tu Bao HO
AU - Tho Hoan PHAM
AU - Kenji SATOU
PY - 2011
DO - 10.1587/transinf.E94.D.416
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.
ER -