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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Satu jenis RNA bukan pengekodan berfungsi, mikroRNA (miRNA), membentuk kelas RNA endogen yang boleh mempunyai peranan pengawalseliaan yang penting dalam haiwan dan tumbuhan dengan menyasarkan transkrip untuk penindasan pembelahan atau terjemahan. Penyelidikan mengenai kedua-dua pendekatan eksperimen dan pengiraan telah menunjukkan bahawa miRNA memang terlibat dalam perkembangan dan perkembangan kanser manusia. Walau bagaimanapun, miRNA yang menyumbang lebih banyak maklumat kepada perbezaan antara sampel normal dan tumor (tisu) masih tidak dapat ditentukan. Baru-baru ini, teknologi microarray throughput tinggi digunakan sebagai teknik yang berkuasa untuk mengukur tahap ekspresi miRNA dalam sel. Menganalisis data ungkapan ini boleh membolehkan kita menentukan peranan fungsi miRNA dalam sel hidup. Dalam makalah ini, kami membentangkan kaedah pengiraan untuk (1) meramalkan tisu tumor menggunakan profil ekspresi miRNA throughput tinggi; (2) mencari miRNA bermaklumat yang menunjukkan perbezaan tahap ekspresi yang kuat dalam tisu tumor. Untuk tujuan ini, kami melakukan kaedah berasaskan mesin vektor sokongan (SVM) untuk memeriksa secara mendalam satu set data ekspresi miRNA terkini. Keputusan eksperimen menunjukkan bahawa kaedah berasaskan SVM mengatasi kaedah pembelajaran diselia yang lain seperti pepohon keputusan, rangkaian Bayesian, dan rangkaian neural perambatan belakang. Tambahan pula, dengan menggunakan maklumat sasaran miRNA dan anotasi Gene Ontology, kami menunjukkan bahawa miRNA bermaklumat mempunyai bukti kukuh yang berkaitan dengan beberapa jenis kanser manusia termasuk kanser payudara, paru-paru dan kolon.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -