Araştırma Makalesi
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Makine Öğrenmesi Tabanlı Mikrodizi Tekniği ile MikroRNA Hedef Tahmini: Araştırma Çalışması

Yıl 2022, Sayı: 44, 39 - 45, 31.12.2022
https://doi.org/10.31590/ejosat.1220962

Öz

Biyoenformatik, biyolojik bilgilerin bilgisayar teknolojileri yardımıyla incelenmesini ve değerlendirilmesini sağlayan bir araştırma alanıdır. Çok disiplinli bu alan sayesinde tıbbi veriler üzerinde yapılan çalışmalarda hızla yol alınabilmekte, gerek hastalıkların teşhis-tedavi süreçlerinde gerek önlenmesi süreçlerinde başarılı çözümler bulunabilmektedir.

Birçok farklı organizmada görülen ve hücre üzerinde olaylarda etkili olduğu ortaya çıkan mikroRNA (miRNA, miR olarak da isimlendirilir, mikro RiboNükleik Asit’in kısaltmasıdır)’ların genler üzerindeki etkisi ile ilgili çalışmalar da biyoenformatik yöntemler yardımıyla başarılı sonuçlar vermektedir. Özellikle kanser ile yakın ilişkili olduğu düşünülen mikroRNA’ların incelenmesinde mikrodizi teknikleri sıklıkla tercih edilmektedir. Mikrodizi olarak hazırlanan veri setleri makine öğrenmesi yöntemleri ile değerlendirilerek mikroRNA hedef genlerinin belirlenmesi, mikroRNA’ya bağlı hastalık/kanserin teşhis ve tedavi süreçleri ile ilgili hızlı ve doğruluğu yüksek sonuçlar elde edilebilmektedir.

Bu araştırma çalışmasında, mikroRNA hedef gen tahmini sürecinde makine öğrenmesi tekniklerinin kullanımı incelenmiştir.

Kaynakça

  • Maziere, P., & Enright, A.J. (2007). Prediction of microRNA targets. Drug Discovery Today. 12(11712):452-458.
  • Karagün, B.Ş., Antmen, B., Şaşmaz, İ., & Kılınç, Y. (2014). Mikro RNA ve Kanser. Türk Klinik Biyokimya Dergisi. 12(1):45-56.
  • (2021, Mart 27). https://www.affymetrix.com/
  • (2021, Mart 27). https://www.illumina.com/
  • (2021, Mart 27). https://www.agilent.com/
  • (2021, Mart 27). http://www.exiqon.com/
  • Jiang, H., Wang, J., Li, M., Lan, W., Wu, F.X., & Pan, Yi. (2015). miRTRS: A Recommendation Algorithm for Predicting miRNA Targets. Journal of Latex Class Files. 14(8):1-10.
  • Kim, S., Choi, M., & Cho, K.H. (2009). Identifying the Target mRNAs of microRNAs in Colorectal Cancer. Computational Biology and Chemistry. 33(1):94-99.
  • Lu, Y., Zhou, Y., Qu, W., Deng, M., & Zhang, C. (2011). A Lasso Regression Model for the Construction of microRNA-Target Regulatory Networks. Bioinformatics. 27(17):2406-2413.
  • Sedaghat, N., Fathy, M., Modarressi, M.H., & Shojaie, A. (2018). Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(5):1594-1604.
  • Van der Auwera, I., Limame, R., Van Dam, P., Vermeulen, P., Dirix, L., & Van Laere, S. (2010). Integrated miRNA and mRNA Expression Profiling of the Inflammatory Breast Cancer Subtype. British J. Cancer. 103(4):532-541.
  • Liu, H., Brannon, A.R., Reddy, A.R., Alexe, G., Seiler, M.W., Arreola, A., Oza, J.H., Yao, M., Juan, D., Liou, L.S., Ganesan, S., Levine, A.J., Rathmell, W.K., & Bhanot, G.V. (2010). Identifiying mRNA Targets of microRNA Dysregulated in Cancer: with Application to Clear Cell Renal Cell Carcinoma. BMC Syst. Biology. 4(1):51
  • Sales, G., Coppe, A., Bisognin, A., Biasiolo, M., Bortoluzzi, S., & Romualdi, C. (2010). MAGIA, a Web-Based Tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res. 38(2):352-359.
  • Muniategui, A., Nogales-Cadenas, R., Vázquez, M., Aranguren, X. L., Agirre, X., Luttun, A., Prosper, F., Pascual-Montano, A., & Rubio, A. (2012). Quantification of miRNA-mRNA interactions. PloS one. 7(2):1-10.
  • Rabiee-Ghahfarrokhi, B., Rafiei, F., Niknafs, A. A., & Zamani, B. (2015). Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree. FEBS open bio. 5:877–884.
  • Abdelhadi Ep Souki, O., Day, L., Albrecht, A.A., & Steinhöfel, K. (2015). microRNA Target Prediction Based Upon Metastable RNA Secondary Structures. Bioinformatics and Biomedical Engineering. 2:456-467
  • SaeTrom, O. L. A., Snøve, O., & Sætrom, P. (2005). Weighted Sequence Motifs as an Improved Seeding Step in microRNA Target Prediction Algorithms. RNA. 11(7):995-1003.
  • Bandyopadhyay, S., & Mitra, R. (2009). TargetMiner: microRNA Target Prediction with Systematic Identification of Tissue-Specific Negative Examples. Bioinformatics. 25(20):2625-2631.
  • Yousef, M., Jung, S., Kossenkov, A. V., Showe, L. C., & Showe, M. K. (2007). Naïve Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics. 23(22):2987-2992.
  • Reyes-Herrera, P. H., Ficarra, E., Acquaviva, A., & Macii, E. (2011). miREE: miRNA recognition elements ensemble. Bmc Bioinformatics. 12(1):1-20.
  • Öztemur, Y., Aydos, A., & GÜR-DEDEOĞLU, B. (2014). Meme kanseri mikrodizin verilerinin biyoinformatik yöntemler ile bir araya getirilmesi-Meta-analiz yaklaşımları. Türk Hijyen ve Deneysel Biyoloji Dergisi. 72(2):155-162.
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  • (2022, Mart 8). https://www.ebi.ac.uk/arrayexpress/
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  • Marry, K.V. (2005). Small RNAs: Classification, Biogenesis, and Function. Mol Celss. 19(1):1-15. Chen, X., & Yan, G. Y. (2014). Semi-supervised learning for potential human microRNA-disease associations inference. Scientific reports. 4(1):1-10.
  • Peterson, S. M., Thompson, J. A., Ufkin, M. L., Sathyanarayana, P., Liaw, L., & Congdon, C. B. (2014). Common features of microRNA target prediction tools. Frontiers in genetics. 5:23.
  • Mathelier, A., & Carbone, A. (2010). MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics. 26(18):2226-2234.
  • Saydam, F., Değirmenci, İ., & Güneş, H. V. (2011). MikroRNA\'lar ve kanser. Dicle Tıp Dergisi. 38(1).
  • Kwak, P. B., Iwasaki, S., & Tomari, Y. (2010). The microRNA pathway and cancer. Cancer science. 101(11):2309-2315.
  • (2022, Kasım 21). https://www.cancer.gov/
  • (2022, Ocak 12). https://dcc.icgc.org/
  • (2022, Haziran 19). http://www.targetscan.org/
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  • (2022, Haziran 19). http://www.cuilab.cn/hmdd
  • Riolo, G., Cantara, S., Marzocchi, C., & Ricci, C. (2020). miRNA targets: from prediction tools to experimental validation. Methods and protocols. 4(1):1-20.
  • Sturm, M., Hackenberg, M., Langenberger, D., & Frishman, D. (2010). TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC bioinformatics. 11(1):1-17.
  • Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology. 11(8):1-14.
  • Gudyś, A., Szcześniak, M. W., Sikora, M., & Makałowska, I. (2013). HuntMi: an efficient and taxon-specific approach in pre-miRNA identification. BMC Bioinformatics. 14(1):1-10.
  • Coronnello, C., & Benos, P. V. (2013). ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Research. 41(1):159-164.
  • Mendoza, M. R., da Fonseca, G. C., Loss-Morais, G., Alves, R., Margis, R., & Bazzan, A. L. (2013). RFMirTarget: predicting human microRNA target genes with a random forest classifier. PloS One. 8(7):1-18.
  • Zou, Q., Mao, Y., Hu, L., Wu, Y., & Ji, Z. (2014). miRClassify: an advanced web server for miRNA family classification and annotation. Computers in Biology and Medicine. 45(1):157-160.
  • Holec, M., Gologuzov, V., & Kléma, J. (2014). miXGENE tool for learning from heterogeneous gene expression data using prior knowledge. IEEE 27th International Symposium on Computer-Based Medical Systems. 247-250.
  • Menor, M., Ching, T., Zhu, X., Garmire, D., & Garmire, L. X. (2014). mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biology. 15(10):1-16.
  • Wang, C. Y., Hu, L., Guo, M. Z., Liu, X. Y., & Zou, Q. (2015). imDC: an ensemble learning method for imbalanced classification with miRNA data. Genetics and Molecular Research. 14(1):123-133.
  • Bandyopadhyay, S., Ghosh, D., Mitra, R., & Zhao, Z. (2015). MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Scientific Reports. 5(1):1-12.
  • Karathanou, K., Theofilatos, K., Kleftogiannis, D., Alexakos, C., Likothanassis, S., Tsakalidis, A., & Mavroudi, S. (2015). ncRNAclass: A web platform for non-coding RNA feature calculation and microRNAs and targets prediction. International Journal on Artificial Intelligence Tools. 24(01):1-17.
  • Cui, H., Zhai, J., & Ma, C. (2015). miRLocator: machine learning-based prediction of mature microRNAs within plant pre-miRNA sequences. PLoS One. 10(11):1-15.
  • Kim, M. S., Hur, B., & Kim, S. (2016, January). RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data. BMC Genomics. 17(1):85-95.
  • Pian, C., Zhang, J., Chen, Y. Y., Chen, Z., Li, Q., Li, Q., & Zhang, L. Y. (2016). OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features. Journal of Bioinformatics and Computational Biology. 14(01):1-14.
  • Ding, J., Li, X., & Hu, H. (2016). TarPmiR: a new approach for microRNA target site prediction. Bioinformatics. 32(18):2768-2775.
  • Cheng, S., Guo, M., Wang, C., Liu, X., Liu, Y., & Wu, X. (2015). MiRTDL: a deep learning approach for miRNA target prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 13(6):1161-1169.
  • Marques, Y. B., de Paiva Oliveira, A., Ribeiro Vasconcelos, A. T., & Cerqueira, F. R. (2016). Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction. BMC Bioinformatics. 17(18):53-63.
  • Cava, C., Colaprico, A., Bertoli, G., Graudenzi, A., Silva, T. C., Olsen, C., ... & Castiglioni, I. (2017). SpidermiR: an R/bioconductor package for integrative analysis with miRNA data. International Journal of Molecular Sciences. 18(2):1-14.
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MicroRNA Target Prediction by Machine Learning-Based Microarray Technique: Research Study

Yıl 2022, Sayı: 44, 39 - 45, 31.12.2022
https://doi.org/10.31590/ejosat.1220962

Öz

Bioinformatics is a research field that enables the examination and evaluation of biological information with the help of computer technologies. With the help of this multidisciplinary field, studies on medical data can progress rapidly, and successful solutions can be found both in the diagnosis-treatment processes of diseases and in the prevention processes.

Studies on the effects of microRNAs (miRNA, also called miR, an abbreviation for micro RiboNucleic Acid) that are seen in many different organisms and are effective in events on the cell, also give successful results with the help of bioinformatics methods. Microarray techniques are frequently preferred especially in the examination of microRNAs that are thought to be closely related to cancer. By evaluating the data sets prepared as microarrays with machine learning methods, fast and high-accuracy results can be obtained regarding the determination of microRNA target genes, diagnosis and treatment processes of microRNA-related disease/cancer.,

In this research study, the use of machine learning techniques in the microRNA target gene prediction process was examined.

Kaynakça

  • Maziere, P., & Enright, A.J. (2007). Prediction of microRNA targets. Drug Discovery Today. 12(11712):452-458.
  • Karagün, B.Ş., Antmen, B., Şaşmaz, İ., & Kılınç, Y. (2014). Mikro RNA ve Kanser. Türk Klinik Biyokimya Dergisi. 12(1):45-56.
  • (2021, Mart 27). https://www.affymetrix.com/
  • (2021, Mart 27). https://www.illumina.com/
  • (2021, Mart 27). https://www.agilent.com/
  • (2021, Mart 27). http://www.exiqon.com/
  • Jiang, H., Wang, J., Li, M., Lan, W., Wu, F.X., & Pan, Yi. (2015). miRTRS: A Recommendation Algorithm for Predicting miRNA Targets. Journal of Latex Class Files. 14(8):1-10.
  • Kim, S., Choi, M., & Cho, K.H. (2009). Identifying the Target mRNAs of microRNAs in Colorectal Cancer. Computational Biology and Chemistry. 33(1):94-99.
  • Lu, Y., Zhou, Y., Qu, W., Deng, M., & Zhang, C. (2011). A Lasso Regression Model for the Construction of microRNA-Target Regulatory Networks. Bioinformatics. 27(17):2406-2413.
  • Sedaghat, N., Fathy, M., Modarressi, M.H., & Shojaie, A. (2018). Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(5):1594-1604.
  • Van der Auwera, I., Limame, R., Van Dam, P., Vermeulen, P., Dirix, L., & Van Laere, S. (2010). Integrated miRNA and mRNA Expression Profiling of the Inflammatory Breast Cancer Subtype. British J. Cancer. 103(4):532-541.
  • Liu, H., Brannon, A.R., Reddy, A.R., Alexe, G., Seiler, M.W., Arreola, A., Oza, J.H., Yao, M., Juan, D., Liou, L.S., Ganesan, S., Levine, A.J., Rathmell, W.K., & Bhanot, G.V. (2010). Identifiying mRNA Targets of microRNA Dysregulated in Cancer: with Application to Clear Cell Renal Cell Carcinoma. BMC Syst. Biology. 4(1):51
  • Sales, G., Coppe, A., Bisognin, A., Biasiolo, M., Bortoluzzi, S., & Romualdi, C. (2010). MAGIA, a Web-Based Tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res. 38(2):352-359.
  • Muniategui, A., Nogales-Cadenas, R., Vázquez, M., Aranguren, X. L., Agirre, X., Luttun, A., Prosper, F., Pascual-Montano, A., & Rubio, A. (2012). Quantification of miRNA-mRNA interactions. PloS one. 7(2):1-10.
  • Rabiee-Ghahfarrokhi, B., Rafiei, F., Niknafs, A. A., & Zamani, B. (2015). Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree. FEBS open bio. 5:877–884.
  • Abdelhadi Ep Souki, O., Day, L., Albrecht, A.A., & Steinhöfel, K. (2015). microRNA Target Prediction Based Upon Metastable RNA Secondary Structures. Bioinformatics and Biomedical Engineering. 2:456-467
  • SaeTrom, O. L. A., Snøve, O., & Sætrom, P. (2005). Weighted Sequence Motifs as an Improved Seeding Step in microRNA Target Prediction Algorithms. RNA. 11(7):995-1003.
  • Bandyopadhyay, S., & Mitra, R. (2009). TargetMiner: microRNA Target Prediction with Systematic Identification of Tissue-Specific Negative Examples. Bioinformatics. 25(20):2625-2631.
  • Yousef, M., Jung, S., Kossenkov, A. V., Showe, L. C., & Showe, M. K. (2007). Naïve Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics. 23(22):2987-2992.
  • Reyes-Herrera, P. H., Ficarra, E., Acquaviva, A., & Macii, E. (2011). miREE: miRNA recognition elements ensemble. Bmc Bioinformatics. 12(1):1-20.
  • Öztemur, Y., Aydos, A., & GÜR-DEDEOĞLU, B. (2014). Meme kanseri mikrodizin verilerinin biyoinformatik yöntemler ile bir araya getirilmesi-Meta-analiz yaklaşımları. Türk Hijyen ve Deneysel Biyoloji Dergisi. 72(2):155-162.
  • Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., ... & Vingron, M. (2001). Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nature genetics. 29(4):365-371.
  • (2022, Mart 8). https://www.ncbi.nlm.nih.gov/geo/
  • (2022, Mart 8). https://www.ebi.ac.uk/arrayexpress/
  • (2022, Mart 8). https://datamed.org/
  • Marry, K.V. (2005). Small RNAs: Classification, Biogenesis, and Function. Mol Celss. 19(1):1-15. Chen, X., & Yan, G. Y. (2014). Semi-supervised learning for potential human microRNA-disease associations inference. Scientific reports. 4(1):1-10.
  • Peterson, S. M., Thompson, J. A., Ufkin, M. L., Sathyanarayana, P., Liaw, L., & Congdon, C. B. (2014). Common features of microRNA target prediction tools. Frontiers in genetics. 5:23.
  • Mathelier, A., & Carbone, A. (2010). MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics. 26(18):2226-2234.
  • Saydam, F., Değirmenci, İ., & Güneş, H. V. (2011). MikroRNA\'lar ve kanser. Dicle Tıp Dergisi. 38(1).
  • Kwak, P. B., Iwasaki, S., & Tomari, Y. (2010). The microRNA pathway and cancer. Cancer science. 101(11):2309-2315.
  • (2022, Kasım 21). https://www.cancer.gov/
  • (2022, Ocak 12). https://dcc.icgc.org/
  • (2022, Haziran 19). http://www.targetscan.org/
  • (2022, Haziran 19). http://mirwalk.umm.uni-heidelberg.de
  • (2022, Haziran 19). http://www.mirbase.org/
  • (2022, Haziran 19). http://www.disgenet.org/
  • (2022, Haziran 19). http://www.cuilab.cn/hmdd
  • Riolo, G., Cantara, S., Marzocchi, C., & Ricci, C. (2020). miRNA targets: from prediction tools to experimental validation. Methods and protocols. 4(1):1-20.
  • Sturm, M., Hackenberg, M., Langenberger, D., & Frishman, D. (2010). TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC bioinformatics. 11(1):1-17.
  • Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology. 11(8):1-14.
  • Gudyś, A., Szcześniak, M. W., Sikora, M., & Makałowska, I. (2013). HuntMi: an efficient and taxon-specific approach in pre-miRNA identification. BMC Bioinformatics. 14(1):1-10.
  • Coronnello, C., & Benos, P. V. (2013). ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Research. 41(1):159-164.
  • Mendoza, M. R., da Fonseca, G. C., Loss-Morais, G., Alves, R., Margis, R., & Bazzan, A. L. (2013). RFMirTarget: predicting human microRNA target genes with a random forest classifier. PloS One. 8(7):1-18.
  • Zou, Q., Mao, Y., Hu, L., Wu, Y., & Ji, Z. (2014). miRClassify: an advanced web server for miRNA family classification and annotation. Computers in Biology and Medicine. 45(1):157-160.
  • Holec, M., Gologuzov, V., & Kléma, J. (2014). miXGENE tool for learning from heterogeneous gene expression data using prior knowledge. IEEE 27th International Symposium on Computer-Based Medical Systems. 247-250.
  • Menor, M., Ching, T., Zhu, X., Garmire, D., & Garmire, L. X. (2014). mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biology. 15(10):1-16.
  • Wang, C. Y., Hu, L., Guo, M. Z., Liu, X. Y., & Zou, Q. (2015). imDC: an ensemble learning method for imbalanced classification with miRNA data. Genetics and Molecular Research. 14(1):123-133.
  • Bandyopadhyay, S., Ghosh, D., Mitra, R., & Zhao, Z. (2015). MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Scientific Reports. 5(1):1-12.
  • Karathanou, K., Theofilatos, K., Kleftogiannis, D., Alexakos, C., Likothanassis, S., Tsakalidis, A., & Mavroudi, S. (2015). ncRNAclass: A web platform for non-coding RNA feature calculation and microRNAs and targets prediction. International Journal on Artificial Intelligence Tools. 24(01):1-17.
  • Cui, H., Zhai, J., & Ma, C. (2015). miRLocator: machine learning-based prediction of mature microRNAs within plant pre-miRNA sequences. PLoS One. 10(11):1-15.
  • Kim, M. S., Hur, B., & Kim, S. (2016, January). RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data. BMC Genomics. 17(1):85-95.
  • Pian, C., Zhang, J., Chen, Y. Y., Chen, Z., Li, Q., Li, Q., & Zhang, L. Y. (2016). OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features. Journal of Bioinformatics and Computational Biology. 14(01):1-14.
  • Ding, J., Li, X., & Hu, H. (2016). TarPmiR: a new approach for microRNA target site prediction. Bioinformatics. 32(18):2768-2775.
  • Cheng, S., Guo, M., Wang, C., Liu, X., Liu, Y., & Wu, X. (2015). MiRTDL: a deep learning approach for miRNA target prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 13(6):1161-1169.
  • Marques, Y. B., de Paiva Oliveira, A., Ribeiro Vasconcelos, A. T., & Cerqueira, F. R. (2016). Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction. BMC Bioinformatics. 17(18):53-63.
  • Cava, C., Colaprico, A., Bertoli, G., Graudenzi, A., Silva, T. C., Olsen, C., ... & Castiglioni, I. (2017). SpidermiR: an R/bioconductor package for integrative analysis with miRNA data. International Journal of Molecular Sciences. 18(2):1-14.
  • Thomas, J., Thomas, S., & Sael, L. (2017, February). DP-miRNA: An improved prediction of precursor microRNA using deep learning model. IEEE International Conference on Big Data and Smart Computing (BigComp). 96-99.
  • Saçar Demirci, M. D., Baumbach, J., & Allmer, J. (2017). On the performance of pre-microRNA detection algorithms. Nature Communications. 8(1):1-9.
  • Vitsios, D. M., Kentepozidou, E., Quintais, L., Benito-Gutiérrez, E., Van Dongen, S., Davis, M. P., & Enright, A. J. (2017). Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests. Nucleic Acids Research. 45(21):1-11.
  • Tseng, K. C., Chiang-Hsieh, Y. F., Pai, H., Chow, C. N., Lee, S. C., Zheng, H. Q., ... & Chang, W. C. (2018). microRPM: a microRNA prediction model based only on plant small RNA sequencing data. Bioinformatics. 34(7):1108-1115.
  • Wen, M., Cong, P., Zhang, Z., Lu, H., & Li, T. (2018). DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics. 34(22):3781-3787.
  • Ghoshal, A., Zhang, J., Roth, M. A., Xia, K. M., Grama, A. Y., & Chaterji, S. (2018). A distributed classifier for microrna target prediction with validation through tcga expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(4):1037-1051.
  • Maji, R. K., Khatua, S., & Ghosh, Z. (2018). A supervised ensemble approach for sensitive microRNA target prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(1):37-46.
  • Song, F., Cui, C., Gao, L., & Cui, Q. (2019). miES: predicting the essentiality of miRNAs with machine learning and sequence features. Bioinformatics. 35(6):1053-1054.
  • El-Manzalawy, Y., Hsieh, T. Y., Shivakumar, M., Kim, D., & Honavar, V. (2018). Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Medical Genomics. 11(3):19-31.
  • Jiang, H., Wang, J., Li, M., Lan, W., Wu, F. X., & Pan, Y. (2018). miRTRS: a recommendation algorithm for predicting miRNA targets. IEEE/ACM Ttransactions on Computational Biology and Bioinformatics. 17(3):1032-1041.
  • Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern Recognition and Machine Learning (Vol. 4, No. 4, p. 738). New York: Springer.
  • (2022, Ağustos 10). https://www.r-project.org/
  • (2022, Ağustos 10). https://www.python.org/
  • (2022, Ağustos 10). https://www.bioconductor.org/
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Zerrin Yıldız Çavdar 0000-0003-4299-5344

Tolga Ensari 0000-0003-0896-3058

Leyla Turker Sener 0000-0002-7317-9086

Ahmet Sertbaş 0000-0001-8166-1211

Erken Görünüm Tarihi 31 Aralık 2022
Yayımlanma Tarihi 31 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 44

Kaynak Göster

APA Yıldız Çavdar, Z., Ensari, T., Turker Sener, L., Sertbaş, A. (2022). Makine Öğrenmesi Tabanlı Mikrodizi Tekniği ile MikroRNA Hedef Tahmini: Araştırma Çalışması. Avrupa Bilim Ve Teknoloji Dergisi(44), 39-45. https://doi.org/10.31590/ejosat.1220962