Academic literature on the topic 'MicroRNA genes prediction'
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Journal articles on the topic "MicroRNA genes prediction"
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Jamali, Ali Akbar, Anthony Kusalik, and Fang-Xiang Wu. "MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization." Bioinformatics 36, no. 20 (2020): 5061–67. http://dx.doi.org/10.1093/bioinformatics/btaa577.
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Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.
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Zou, Quan, Jinjin Li, Qingqi Hong, et al. "Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/810514.
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MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.
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Fan, Lichao, Xiaoting Yu, Ziling Huang, et al. "Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis." Mediators of Inflammation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1804240.
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The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression.
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Bujko, Mateusz, Paulina Kober, Joanna Boresowicz, et al. "Differential microRNA Expression in USP8-Mutated and Wild-Type Corticotroph Pituitary Tumors Reflect the Difference in Protein Ubiquitination Processes." Journal of Clinical Medicine 10, no. 3 (2021): 375. http://dx.doi.org/10.3390/jcm10030375.
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Background: USP8 mutations are the most common driver changes in corticotroph pituitary tumors. They have direct effect on cells’ proteome through disturbance of ubiquitination process and also influence gene expression. The aim of this study was to compare microRNA profiles in USP8-mutated and wild-type tumors and determine the probable role of differential microRNA expression by integrative microRNA and mRNA analysis. Methods: Patients with Cushing’s disease (n = 28) and silent corticotroph tumors (n = 20) were included. USP8 mutations were identified with Sanger sequencing. MicroRNA and gene expression was determined with next-generation sequencing. Results: USP8-mutated patients with Cushing’s disease showed higher rate of clinical remission and trend towards lower tumor volume than wild-type patients. Comparison of microRNA profiles of USP8-mutated and wild-type tumors revealed 68 differentially expressed microRNAs. Their target genes were determined by in silico prediction and microRNA/mRNA correlation analysis. GeneSet Enrichment analysis of putative targets showed that the most significantly overrepresented genes are involved in protein ubiquitination-related processes. Only few microRNAs influence the expression of genes differentially expressed between USP8-mutated and wild-type tumors. Conclusions: Differences in microRNA expression in corticotropinomas stratified according to USP8 status reflect disturbed ubiquitination processes, but do not correspond to differences in gene expression between these tumors.
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Fadaka, Adewale Oluwaseun, Ashley Pretorius, and Ashwil Klein. "Functional Prediction of Candidate MicroRNAs for CRC Management Using in Silico Approach." International Journal of Molecular Sciences 20, no. 20 (2019): 5190. http://dx.doi.org/10.3390/ijms20205190.
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Approximately 30–50% of malignant growths can be prevented by avoiding risk factors and implementing evidence-based strategies. Colorectal cancer (CRC) accounted for the second most common cancer and the third most common cause of cancer death worldwide. This cancer subtype can be reduced by early detection and patients’ management. In this study, the functional roles of the identified microRNAs were determined using an in silico pipeline. Five microRNAs identified using an in silico approach alongside their seven target genes from our previous study were used as datasets in this study. Furthermore, the secondary structure and the thermodynamic energies of the microRNAs were revealed by Mfold algorithm. The triplex binding ability of the oligonucleotide with the target promoters were analyzed by Trident. Finally, evolutionary stage-specific somatic events and co-expression analysis of the target genes in CRC were analyzed by SEECancer and GeneMANIA plugin in Cytoscape. Four of the five microRNAs have the potential to form more than one secondary structure. The ranges of the observed/expected ratio of CpG dinucleotides of these genes range from 0.60 to 1.22. Three of the candidate microRNA were capable of forming multiple triplexes along with three of the target mRNAs. Four of the total targets were involved in either early or metastatic stage-specific events while three other genes were either a product of antecedent or subsequent events of the four genes implicated in CRC. The secondary structure of the candidate microRNAs can be used to explain the different degrees of genetic regulation in CRC due to their conformational role to modulate target interaction. Furthermore, due to the regulation of important genes in the CRC pathway and the enrichment of the microRNA with triplex binding sites, they may be a useful diagnostic biomarker for the disease subtype.
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Torkey, Hanaa, Lenwood S. Heath, and Mahmoud ElHefnawi. "MicroTarget: MicroRNA target gene prediction approach with application to breast cancer." Journal of Bioinformatics and Computational Biology 15, no. 04 (2017): 1750013. http://dx.doi.org/10.1142/s0219720017500135.
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MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA–gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA–gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget .
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Shan-Ying, Wang, Lin Liang-Ting, Lin Bing-Ze, et al. "A comparative study of single or dual treatment of theranostic 188Re-Liposome on microRNA expressive profiles of orthotopic human head and neck tumor model." Heighpubs Otolaryngology and Rhinology 5, no. 1 (2021): 001–12. http://dx.doi.org/10.29328/journal.hor.1001024.
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Background: 188Re-liposome has been used for evaluating the theranostic efficacy on human head and neck squamous cell carcinoma (HNSCC) at preclinical stages. Here we furthercompared the microRNA expressive profile in orthtopic HNSCC tumor model exposed to 188Re-liposome. Methods: A single dose or dual doses of 188Re-liposome was intravenously injected into tumor-bearing mice followed by the Cerenkov luminescent imaging (CLI) for monitoring the accumulation of 188Re-liposome in tumors. The microRNA expressive profile was generated using the Taqman® OpenArray® Human MicroRNA Panel followed by the DIANA mirPath analysis, KEGG signaling pathways prediction, and Kaplan-Meier survival analysis for predicting the prognostic role of 188Re-liposome affected microRNAs. Results: Dual doses of 188Re-liposome exhibited a better tumor suppression than a single dose of 188Re-liposome, including reduced tumor size, Ki-67 proliferative marker, and epithelial-mesenchymal transition (EMT) related factors. The microRNA expressive profiles showed that 22 microRNAs and 19 microRNAs were up-regulated and down-regulated by dual doses of 188Re-liposome, respectively. Concomitantly, these two groups of microRNAs were inversely regulated by a single dose of 188Re-liposome accordingly. These microRNAs influenced most downstream genes involved in cancer related signaling pathways. Further, miR-520e and miR-522-3p were down-regulated whereas miR-186-5p and miR-543 were up-regulated by dual doses of 188Re-liposome, and they separately affected most of genes involved in their corresponding pathways with high significance. Additionally, high expressions of miR-520e and miR-522-3p were associated with lower survival rate of HNSCC patients. Conclusion: MicroRNA expression could be used to evaluate the therapeutic efficacy and regarded prognostic factors using different doses of 188Re-liposome.
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Fadaka, Adewale Oluwaseun, Ashwil Klein, and Ashley Pretorius. "In silico identification of microRNAs as candidate colorectal cancer biomarkers." Tumor Biology 41, no. 11 (2019): 101042831988372. http://dx.doi.org/10.1177/1010428319883721.
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The involvement of microRNA in cancers plays a significant role in their pathogenesis. Specific expressions of these non-coding RNAs also serve as biomarkers for early colorectal cancer diagnosis, but their laboratory/molecular identification is challenging and expensive. The aim of this study was to identify potential microRNAs for colorectal cancer diagnosis using in silico approach. Sequence similarity search was employed to obtain the candidate microRNA from the datasets, and three target prediction software were employed to determine their target genes. To determine the involvement of these microRNAs in colorectal cancer, the microRNA gene list obtained was used alongside with colorectal cancer expressed genes from gbCRC and CoReCG databases for gene intersection analysis. The involvement of these genes in the cancer subtype was further strengthened with the DAVID database. KEGG and Gene Ontology were used for the pathway and functional analysis, while STRING was employed for the interactions of protein network and further visualized by Cytoscape. The cBioPortal database was used to prioritize the target genes; prognostic and expression analysis were finally performed on the candidate microRNAs and the prioritized targets. This study, therefore, identified five candidate microRNAs, two hub genes (CTNNB1 and epidermal growth factor receptor), and seven significant target genes associated with colorectal cancer. The molecular validation studies are ongoing to ascertain the biological fitness of these findings.
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Tong, Chuan-zhou, Yong-feng Jin, and Yao-zhou Zhang. "Computational prediction of microRNA genes in silkworm genome." Journal of Zhejiang University SCIENCE B 7, no. 10 (2006): 806–16. http://dx.doi.org/10.1631/jzus.2006.b0806.
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Chen, Zhi Ru, Wen Xue Hong, and Pei Pei Zhao. "An Imbalance SVM for MicroRNA Target Genes Prediction." Applied Mechanics and Materials 577 (July 2014): 1245–51. http://dx.doi.org/10.4028/www.scientific.net/amm.577.1245.
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Imbalance miRNA target sample data bring about the lower prediction accuracy of SVM(Support Vector Machine). This paper proposes an SVM algorithm to predict the target genes based on biased discriminant idea. This paper selects an optimal feature sets as input data, and constructs a kernel optimization objective function based on the biased discriminant analysis criteria in the empirical feature space. The conformal transformation of a kernel is utilized to gradually optimize the kernel matrix. Through the comparative analysis of the experimental results of human, mouse and rat, the imbalance SVM with biased discriminant has higher specificity, sensitivity and prediction accuracy, which proves that it has stronger generalization ability and better robustness.
Dissertations / Theses on the topic "MicroRNA genes prediction"
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Graça, Clara Susana Marques. "MicroRNAs and target genes involved in E. globulus xylogenesis: in silico prediction and experimental validation." Master's thesis, ISA, 2014. http://hdl.handle.net/10400.5/6788.
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Mestrado em Biologia Funcional - Instituto Superior de Agronomia<br>Portugal is one of the largest producers of pulp and paper derived from Eucalyptus globulus, which
makes this a valuable species for the country. Wood is a complex and variable material, and its posttranscriptional
regulation knowledge is only beginning. MicroRNAs (miRNA) are small size (21-24nt),
endogenous non-coding RNAs, involved in post-transcriptional regulation. MiRBase v20 database
encloses thousands of entries, however none from Eucalyptus. In this study we aim to validate E.
globulus miRNAs candidates; to predict in silico and validate experimentally the miRNAs targets; and
analyze the gene expression of validated targets.
Four miRCa-02, miRCa-04, miRCa-08 and miRCa-09 candidates were validated by Northern blot
and there in silico prediction revealed 42 target genes. Fourteen predicted target genes were tested
through the RLM 5’-RACE methodology, but only three predicted targets were validated
(Eucgr.E01509, Eucgr.C01382 and Eucgr.J02113 predicted target genes for miR171, miRCa-04 and
miRCa-08, respectively). Expression of these three target genes analyzed by RT-qPCR suggests that
the distinct expression levels found may be related with to wood formation in Eucalyptus globulus.
For the first time, four Eucalytus miRNAs and their target genes were disclosed and validated by
bioinformatic and molecular tools.
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Le, Hai-Son Phuoc. "Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/245.
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Advances in genomics allow researchers to measure the complete set of transcripts in cells. These transcripts include messenger RNAs (which encode for proteins) and microRNAs, short RNAs that play an important regulatory role in cellular networks. While this data is a great resource for reconstructing the activity of networks in cells, it also presents several computational challenges. These challenges include the data collection stage which often results in incomplete and noisy measurement, developing methods to integrate several experiments within and across species, and designing methods that can use this data to map the interactions and networks that are activated in specific conditions. Novel and efficient algorithms are required to successfully address these challenges.
In this thesis, we present probabilistic models to address the set of challenges associated with expression data. First, we present a novel probabilistic error correction method for RNA-Seq reads. RNA-Seq generates large and comprehensive datasets that have revolutionized our ability to accurately recover the set of transcripts in cells. However, sequencing reads inevitably contain errors, which affect all downstream analyses. To address these problems, we develop an efficient hidden Markov modelbased error correction method for RNA-Seq data . Second, for the analysis of expression data across species, we develop clustering and distance function learning methods for querying large expression databases. The methods use a Dirichlet Process Mixture Model with latent matchings and infer soft assignments between genes in two species to allow comparison and clustering across species. Third, we introduce new probabilistic models to integrate expression and interaction data in order to predict targets and networks regulated by microRNAs.
Combined, the methods developed in this thesis provide a solution to the pipeline of expression analysis used by experimentalists when performing expression experiments.
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Guidi, Mònica. "Micro RNA-Mediated regulation of the full-length and truncated isoforms of human neurotrophic tyrosine kinase receptor type 3 (NTRK 3)." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7114.
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Neurotrophins and their receptors are key molecules in the development of the<br/>nervous system. Neurotrophin-3 binds preferentially to its high-affinity receptor<br/>NTRK3, which exists in two major isoforms in humans, the full-length kinaseactive<br/>form (150 kDa) and a truncated non-catalytic form (50 kDa). The two<br/>variants show different 3'UTR regions, indicating that they might be differentially<br/>regulated at the post-transcriptional level. In this work we explore how<br/>microRNAs take part in the regulation of full-length and truncated NTRK3,<br/>demonstrating that the two isoforms are targeted by different sets of microRNAs.<br/>We analyze the physiological consequences of the overexpression of some of the<br/>regulating microRNAs in human neuroblastoma cells. Finally, we provide<br/>preliminary evidence for a possible involvement of miR-124 - a microRNA with no<br/>putative target site in either NTRK3 isoform - in the control of the alternative<br/>spicing of NTRK3 through the downregulation of the splicing repressor PTBP1.<br>Las neurotrofinas y sus receptores constituyen una familia de factores cruciales<br/>para el desarrollo del sistema nervioso. La neurotrofina 3 ejerce su función<br/>principalmente a través de una unión de gran afinidad al receptor NTRK3, del cual<br/>se conocen dos isoformas principales, una larga de 150KDa con actividad de tipo<br/>tirosina kinasa y una truncada de 50KDa sin dicha actividad. Estas dos isoformas<br/>no comparten la misma región 3'UTR, lo que sugiere la existencia de una<br/>regulación postranscripcional diferente. En el presente trabajo se ha explorado<br/>como los microRNAs intervienen en la regulación de NTRK3, demostrando que las<br/>dos isoformas son reguladas por diferentes miRNAs. Se han analizado las<br/>consecuencias fisiológicas de la sobrexpresión de dichos microRNAs utilizando<br/>células de neuroblastoma. Finalmente, se ha estudiado la posible implicación del<br/>microRNA miR-124 en el control del splicing alternativo de NTRK3 a través de la<br/>regulación de represor de splicing PTBP1.
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Lee, Chia-En, and 李嘉恩. "Prediction of microRNA Genes near the HOX Gene family." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/57558315965749636931.
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碩士<br>亞洲大學<br>生物資訊學系碩士班<br>95<br>The Hox gene (homeobox) is known to be related to the human embryo development. In the past few years, research reports suggested that overexpression of multiple HOX genes could cause cancer. MicroRNA is a small non-coding RNA with 18–22 nucleotides long that mediate posttranscriptional silencing of genes. Some researches reported that miRNA could target the HOX mRNA and lead to its down-regulation.
A total of six pre-miRNAs are predicted around the HOXA10 and HOXD4 regions by using a miRNA gene prediction protocol proposed in this thesis. These six putative pre-miRNAs have a sequence identity of 50% to 70% when compared with certain known mature miRNAs. Among the six pre-miRNAs only one of them is near a CpG island. None of the putative pre-miRNA is found to has significant sequence identity with known pre-miRNA, this could be due to the following two reasons: (i) the mean free energy is chosen to be -30 Kcal/mole instead of -25 Kcal/mole, and (ii) only the 1000 bp upstream and downstream regions of the HOX gene are chosen for miRNA gene prediction analysis, where regions of 6000 bp long upstream and downstream will be a better choice in future analysis.
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Chen, Ching-Yi, and 陳敬詒. "Prediction of microRNA target genes using hierarchical classification system." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/01670770444043072031.
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碩士<br>國立陽明大學<br>生物醫學資訊研究所<br>100<br>Many studies have implicated miRNAs and its associated target mRNAs in numerous human diseases. Recently, the next generation sequencing technology (NGS) has led to the discovery of an enormous number of novel miRNAs from small RNA sequencing. However, until now there are no high-throughput experimental techniques that can be used to determine the miRNA targets. Hence, how to accurately identify miRNA targets has become an important issue, given the huge increase in novel miRNAs continuously being discovered. Based on known miRNAs and associated validated target mRNAs, and despite the limited data available, it is imperative that computational approaches can be developed to speed up production of reliable and testable prediction of miRNA targets.
In this study, we have developed two hierarchical classification systems to perform the prediction of mammalian miRNA targets: hierarchical fuzzy system (HFS) and Hybrid system. First we apply Fuzzy System Feature Attenuating Gates (FS-FAG) approaches to select a set of useful features, which represent important characteristics for the determination of the interaction between microRNA and its target binding mRNA sequence. Next, these selected features are used in two hierarchical classifiers, hierarchical fuzzy system (HFS) and Hybrid (Fuzzy rule based system and Random Forests), to perform prediction of microRNA target gene.
At the first level of the hierarchy, a fuzzy classifier is designed to predict miRNA binding sites, where 16 features relative to characteristics of miRNA-targeting sites are used. Then in the second level, we use another set of useful features as well as the estimated output of the previous (first) level as inputs to construct another fuzzy classifier for production of final prediction of miRNA targets. In addition, in the first level we have further performed feature selection process to select a set of useful features, which represent important characteristics for the determination of the interaction between miRNA and its target binding mRNA sequence. The performance of the hierarchical system is evaluated by a 5-fold cross-validation and an independent testing experiment. Here our results have shown that fuzzy system is comparable to other approaches. Furthermore, since fuzzy rule based systems have better interpretability; our constructed hierarchical fuzzy system can play a significant role in further explaining the relationships between features as well as that between features and the tool.
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Lee, Chien-Yueh, and 李建樂. "Prediction of microRNA Target Genes Using a Hidden Markov Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/27144805620534916409.
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碩士<br>國立臺灣大學<br>生醫電子與資訊學研究所<br>97<br>MicroRNAs (miRNAs) are short non-coding RNAs about 22 nucleotides that play important regulatory roles in animals for translational repression. Nevertheless, it is a difficult challenge to predict targets in animals because of their much more imperfect complementarity between microRNAs and mRNAs. In order to further improve the prediction performance, we propose a novel microRNA target-gene prediction algorithm which combines several conventional prediction models such as the sequence complementary searching for calculating alignment scores and thermodynamic stability approaches for assigning folding free energy to each microRNA-target interactions. Besides, it includes a Hidden Markov Model (HMM), which is a famous machine learning approach, to help the prediction decision. However, due to its innate limitation, HMM can’t consider all the global information of the sequences. Hence, in order to overcome this limitation, forward and backward HMMs are simultaneously utilized in the proposed algorithm. As a result, it can make any element information of microRNA-target interactions able to pass to any other element by bi-directions.
In this thesis, the author calculates the highest sensitivity, specificity, and overall accuracy in the different combination of the proposed models. And it also uses the predicted genes from existing prediction algorithms and down-regulated genes from microarray data to demonstrate the correctness of the proposed algorithm. According to the simulation result, the corresponding sensitivity, specificity, and overall accuracy are 84.25%, 96.78%, and 96.67%, respectively in the complete prediction models. And it is determined that 52.42% and 70.37% overlap rates predicted by the proposed algorithm also can be estimated in other existing prediction algorithms and the down-regulated results of microarray data, respectively.
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Chuan-FangChiang and 姜權芳. "Integrating multiple microRNA prediction databases to identify specific target genes co-regulated by a set of microRNAs." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/79h54v.
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碩士<br>國立成功大學<br>生物資訊與訊息傳遞研究所<br>102<br>MicroRNA (miRNA) is a class of noncoding small RNAs about 22 nt in length which bind to the 3’ untranslated region (UTR) of target genes. They have been found to regulate genes involved in diverse biological functions. MiRNAs also prevent protein synthesis by inhibiting translation or inducing target degradation. In the past, the process of validating a potential miRNA target in the laboratory is time consuming and costly. Therefore, computational prediction of miRNA targets is a critical initial step in identifying interactions of miRNA and mRNA target for experimental validation. Till now, several useful prediction tools for miRNA target genes have been developed. For example, miRanda and TargetScan both predict miRNA target genes by the rules of seed match and 3’ UTR pairing, DIANA-microT develops a dynamic programming algorithm to calculate scores based on the affinity of the interactions between miRNAs and gene targets ,and miRDB makes miRNA target predictions using SVM and features extracted from a large microarray training dataset. Therefore, we integrate these prediction databases to find the real target genes. And we not only provide kinds of databases for users to choose, but also supply user data to predict target genes in our website. In addition to finding important miRNA and mRNA interaction, we also find each target gene that co-regulated by several miRNAs through data from users. Moreover, we provide researchers with combining the mRNA expression data and miRNA and analyzing their correlation. In summary, our web site is easy and friendly to use for researchers.
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Κλεφτογιάννης, Δημήτριος. "Πρόβλεψη microRNA γονιδίων : σχεδιασμός και ανάπτυξη ολοκληρωμένου δικτυακού εργαλείου εξαγωγής χαρακτηριστικών και ταξινόμησης με χρήση καινοτόμων τεχνικών υπολογιστικής νοημοσύνης". Thesis, 2011. http://hdl.handle.net/10889/5008.
Full textAbstract:
Η ανακάλυψη των microRNA γονιδίων το 1993 έφερε επανάσταση στα όσα γνωρίζαμε από το κεντρικό δόγμα της Βιολογίας για τη σύνθεση των πρωτεϊνών και τη ρύθμιση της πρωτεϊνικής έκφρασης. Αυτό γιατί τα microRNA γονίδια δεν κωδικοποιούν κάποια πρωτεΐνη αλλά αντί αυτού παράγουν μικρά μόρια μεγέθους περίπου 22 νουκλεοτιδιών. Τα μόρια αυτά αλληλεπιδρούν με το mRNA και συγκεκριμένα βρίσκουν στόχους στις 3 UTR περιοχές άλλων γονιδίων και αλληλεπιδρούν με βάση την αρχή της συμπληρωματικότητας Watson-Crick. Αποτέλεσμα αυτής της πρόσδεσης είναι η καταστολή της πρωτεϊνικής έκφρασης του γονιδίου στόχου μέσω αναστολής της μετάφρασης ή μέσω της υποβάθμισης του mRNA. Μετά την ανακάλυψη των microRNA και του τρόπου αλληλεπίδρασης τους ήταν φυσικό να ακολουθήσουν εκτεταμένες έρευνες για τις κυτταρικές διεργασίες τις οποίες ρυθμίζουν αλλά και σε ποιών γονίδιων τη ρύθμιση λαμβάνουν μέρος. Ως αποτέλεσμα προέκυψε ότι σε πολλές γνωστές ασθένειες παρουσιάζεται άμεση συσχέτιση με ρύθμιση από microRNA. Τέτοια περίπτωση αποτελεί και η ασθένεια του καρκίνου η οποία σε συνδυασμό με την «λεπτή» ρύθμιση που προκαλούν τα microRNA δίνει ελπίδες για νέες μοριακές θεραπείες. Αυτά είναι μερικά παραδείγματα από τα οποία μπορούμε να καταλάβουμε τη μεγάλη σημασία των microRNA και την ανάγκη για αποδοτική πρόβλεψη τους.
Εξαιτίας του μικρού τους μεγέθους αλλά και του μικρού τους ποσοστού σε σχέση με το μέγεθος του γονιδιώματος (περίπου 3%) ο πειραματικός τους εντοπισμός χαρακτηρίζεται εξαιρετικά δύσκολος. Για το λόγο αυτό έχουν επιστρατευτεί αρκετές μέθοδοι Υπολογιστικής Νοημοσύνης και Αλγόριθμοι οι οποίοι μπορούν να «ξεχωρίσουν» τα πραγματικά γονίδια από τα ψεύτικα γονίδια δημιουργώντας έτσι μοντέλα πρόβλεψης. Μέχρι στιγμής έχουν χρησιμοποιηθεί σαν μέθοδοι ταξινόμησης, Support Vector Machine, δίκτυα Bayes και άλλες πιθανοτικές μέθοδοι όπως τα Hidden Markov μοντέλα .Όλες οι μέθοδοι αυτού του είδους βασίζονται στον υπολογισμό αρκετών χαρακτηριστικών για τα microRNA που σχετίζονται με το ακολουθιακό περιεχόμενο, με τη δομή τους στο χώρο αλλά και με θερμοδυναμικά στοιχεία των μορίων. Σε μεγάλο βαθμό η αποδοτικότητα πρόβλεψης βασίζεται στην επιλογή των πιο αντιπροσωπευτικών χαρακτηριστικών και στη βελτιστοποίηση των παραμέτρων της υπολογιστικής μεθόδου που χρησιμοποιείται χωρίς μέχρι τώρα να έχει βρεθεί μια αξιόπιστη λύση.
Στην παρούσα διπλωματική εργασία αρχικά προτείναμε μια νέα υβριδική μεθοδολογία για ταυτόχρονη ταξινόμηση microRNA γονιδίων, εξαγωγή χαρακτηριστικών και υπολογισμό παραμέτρων. Η προτεινόμενη μεθοδολογία χρησιμοποιεί τις καινοτόμες τεχνικές του Εξελικτικού Προγραμματισμού και συγκεκριμένα τους Γενετικούς Αλγορίθμους για την εξαγωγή χαρακτηριστικών και βελτιστοποίηση παραμέτρων καθώς και Support Vector Machine για ταξινόμηση. Απώτερος στόχος της παρούσας εργασίας ήταν να αναπτυχθεί μια ολοκληρωμένη διαδικτυακή πλατφόρμα η οποία επιτρέπει αρχικά υπολογισμό χαρακτηριστικών για τις γονιδιακές ακολουθίες που θα εισάγει ο χρήστης και κατά δεύτερον δίνει την πρόβλεψη του ταξινομητή για το αν είναι ή όχι microRNA γονίδια μέσω ενός φιλικού περιβάλλοντος. Επιπλέον, ενσωματώθηκαν τα περισσότερα microRNA χαρακτηριστικά που έχουν προταθεί στη βιβλιογραφία και η αποδοτικότητα πρόβλεψης του ταξινομητή βελτιώθηκε μέσω της προτεινόμενης υβριδικής μεθόδου. Ενσωματώσαμε διάφορα πακέτα για τον υπολογισμό των χαρακτηριστικών όπως το Vienna RNA Package και το UnaFold και αυτό το κομμάτι της εργασίας σχετίζεται με την έρευνα για τη θερμοδυναμική και φυσικοχημική συμπεριφορά των μικρών RNA μορίων. Καινοτόμο στοιχείο αποτελεί το γεγονός ότι ο υπολογισμός των χαρακτηριστικών γίνεται με τέτοιο τρόπο ώστε να ευνοείται στη συνέχεια η εφαρμογή μεθόδων Υπολογιστικής Νοημοσύνης. Επιπλέον στο σύστημα ενσωματώσαμε βάση δεδομένων η οποία αποθηκεύει τις επεξεργασμένες ακολουθίες και τα υπολογισμένα χαρακτηριστικά δίνοντας ώθηση για την κατασκευή νέων συνόλων δεδομένων. Τέλος αυτή η δυνατότητα ανάκτησης πληροφορίας κάνει το σύστημα μας μια ολοκληρωμένη πλατφόρμα μελέτης μικρών RNA μορίων και πρόβλεψης microRNA γονιδίων.<br>The discovery of microRNA genes in 1993 challenged the view of the Central Dogma of Biology and introduced a new layer of complexity in which RNA is not only a carrier of gene information but also a mediator of gene expression and protein synthesis. Typically, microRNA genes are short non-coding (~20-22 nt) RNAs that do not encode proteins, but instead they produce small RNA molecules. These molecules can regulate mRNA target genes by binding to the 3’ UTR of mRNA in accordance to Watson-Crick complementarity for cleavage or translational repression. MicroRNAs have been a major object of study as they have been found to be involved in some basic biological processes. These observations underline the importance of normal microRNA homeostasis on functions such as development, differentiation, apoptosis and proliferation. Dysregulation of miRNA is fundamental to the pathogenesis of many diseases and it has been long suspected that miRNA expression can be deregulated in cancer and abnormal miRNA activity may lead to tumorgenesis. From all the above, it is obvious that the better understanding of miRNA mechanism and function may lead to new and more sophisticated design of clinical therapies. Consequently, the identification of novel microRNA genes is a challenging bioinformatics problem.
The experimental identification has some important drawbacks: the small size of microRNA genes in comparison to the size of the genome, cost and time, and low sensitivity. To overcome these technical problems a lot of computational methods have been proposed and several Artificial Intelligence techniques have been applied to distinguish real pre-miRNAs from pseudo hairpins. Support Vector Machines, Naïve Bayes and other probabilistic algorithms such as Hidden Markov Models have been successfully developed as classification methods. All these computational methods rely on the computation of several sequential, structural and thermodynamical microRNA features. In addition, the prediction efficiency is related to the extraction of the most discriminative features and to the optimization of the parameters.
At first, this thesis presents a hybrid approach for simultaneous microRNA classification, feature extraction and parameters optimization. We took advantage of the innovative techniques of Evolutionary Programming and we introduced an embedded classification method, which combines the efficiency and robustness of Support Vector Machines with Genetic Algorithms for feature selection and parameters optimization. Secondly, objective of this thesis was the development of an online platform for effective microRNA genes prediction. The prediction model is based on our classification model and we used a significant feature subset that the proposed methodology revealed to be consistent. Also, we provided the opportunity to calculate all the microRNA features that have been proposed in the literature. We incorporated different packages such as the Vienna RNA package and UnaFold package and we introduced some new features with high discriminative power. This work is related to biophysics and thermodynamics of small RNA molecules and it can be applied to other categories of regulatory molecules.
Also, the provided service in accordance to the feature calculation will encourage the application of other Artificial Intelligence Techniques in identifying microRNAs. We hope that the construction of new datasets will contribute to the Development of new Machine Learning methodologies.
Furthermore, our tool maintains a Database about predicted microRNA sequences plus some informating metadata about them. Finally it can act as an integraded system and the usage of metadata enables information retrieval.
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Radfar, Hossein. "Computational Prediction of Target Genes of MicroRNAs." Thesis, 2014. http://hdl.handle.net/1807/44130.
Full textAbstract:
MicroRNAs (miRNAs) are a class of short (21-25 nt) non-coding endogenous RNAs that mediate the
expression of their direct target genes post-transcriptionally. The goal of this thesis is to identify the
target genes of miRNAs using computational methods. The most popular computational target prediction
methods rely on sequence based determinants to predict targets. However, these determinants are neither
sufficient nor necessary to identify functional target sites,
and commonly ignore the cellular conditions in which miRNAs interact with their targets \emph{in vivo}.
Since miRNAs activity reduces the steady-state
abundance of mRNA targets, the main goal of this thesis is to augment large scale gene expression profiles as
a supplement to sequence-based computational miRNA target prediction techniques.
We develop two computational miRNA target prediction methods: InMiR and BayMiR; in addition, we study the interaction between miRNAs and lncRNAs using long RNA expression data.
InMiR is a computational method that predicts the targets of intronic miRNAs based on the expression profiles of their host
genes across a large number of datasets. InMiR can also predict
which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host
genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3 UTRs
and are significantly more likely to have predicted Pol II-III promoters in their introns.
We also develop BayMiR that scores miRNA-mRNA pairs based on the endogenous footprint of miRNAs on gene expression in a genome-wide scale. BayMiR provides an ``endogenous target repression" index, that identifies the contribution of each miRNA in repressing a target gene in presence of other targeting miRNAs.
This thesis also addresses the interactions between miRNAs and lncRNAs. Our analysis on expression abundance of long RNA transcripts (mRNA and lncRNA) shows that the lncRNA target set of some miRNAs have relatively low abundance in the tissues that these miRNAs are highly active. We also found lncRNAs and mRNAs that shared many targeting miRNAs are significantly positively correlated, indicating that these set of highly expressed lncRNAs may act as miRNA sponges to promote mRNA regulation.
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Lekprasert, Parawee. "MicroRNA Target Prediction via Duplex Formation Features and Direct Binding Evidence." Diss., 2012. http://hdl.handle.net/10161/6175.
Full textAbstract:
<p>MicroRNAs (miRNAs) are small RNAs that have important roles in post-transcriptional gene regulation in a wide range of species. This regulation is controlled by having miRNAs directly bind to a target messenger RNA (mRNA), causing it to be destabilized and degraded, or translationally repressed. Identifying miRNA targets has been a large area of focus for study; however, a lack of generally high-throughput experiments to validate direct miRNA targeting has been a limiting factor. To overcome these limitations, computational methods have become crucial for understanding and predicting miRNA-gene target interactions.</p><p>While a variety of computational tools exist for predicting miRNA targets, many of them are focused on a similar feature set for their prediction. These commonly used features are complementarity to 5'seed of miRNAs and evolutionary conservation. Unfortunately, not all miRNA target sites are conserved or adhere to canonical seed complementarity. Seeking to address these limitations, several studies have included energy features of mRNA:miRNA duplex formation as alternative features. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.</p><p>We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide interaction sites to Argonaute (AGO) protein family members, which are essential parts of the miRNA machinery complex. This trend is confirmed on data sets that assay the effect of miRNAs on mRNA and protein expression changes, where a statistically significant change in expression is noted when compared to the control. Furthermore, our method also allows for prediction of strictly imperfect sites, as well as non-conserved targets.</p><p>Recently, new methods for identifying direct miRNA binding have been developed, which provides us with additional sources of information for miRNA target prediction. While some computational target predictions tools have begun to incorporate this information, they still rely on the presence of a seed match in the AGO-bound windows without accounting for the possibility of variations. </p><p>We investigate the usefulness of the site level direct binding evidence in miRNA target identification and propose a model that incorporates multiple different features along with the AGO-interaction data. Our method outperforms both an ad hoc strategy of seed match searches as well as an existing target prediction tool, while still allowing for predictions of sites other than a long perfect seed match. Additionally, we show supporting evidence for a class of non-canonical sites as bound targets. Our model can be extended to predict additional types of imperfect sites, and can also be readily modified to include additional features that may produce additional improvements.</p><br>Dissertation
Books on the topic "MicroRNA genes prediction"
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(Editor), Wolfgang Nellen, and Christian Hammann (Editor), eds. Small RNAs:: Analysis and Regulatory Functions (Nucleic Acids and Molecular Biology). Springer, 2005.
Find full textBook chapters on the topic "MicroRNA genes prediction"
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Hertel, Jana, David Langenberger, and Peter F. Stadler. "Computational Prediction of MicroRNA Genes." In Methods in Molecular Biology. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-709-9_20.
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Roberts, Justin T., and Glen M. Borchert. "Computational Prediction of MicroRNA Target Genes, Target Prediction Databases, and Web Resources." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_8.
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Mendes, Nuno D. "MicroRNA, Gene Prediction." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1361.
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Masulli, Francesco, Stefano Rovetta, and Giuseppe Russo. "Predicting microRNA Prostate Cancer Target Genes." In Computational Intelligence and Pattern Analysis in Biological Informatics. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470872352.ch5.
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Saçar, Müşerref Duygu, and Jens Allmer. "Machine Learning Methods for MicroRNA Gene Prediction." In miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-748-8_10.
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Allmer, Jens. "Computational and Bioinformatics Methods for MicroRNA Gene Prediction." In miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-748-8_9.
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Karagur, Ege Riza, Sakir Akgun, and Hakan Akca. "Computational and Bioinformatics Methods for MicroRNA Gene Prediction." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1170-8_17.
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Krawczyk, Anna, and Joanna Polanska. "Comparative Analysis of MicroRNA-Target Gene Interaction Prediction Algorithms Based on Integrated P-Value Calculation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67792-7_14.
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Krawczyk, Anna, and Joanna Polańska. "Comparative Analysis of microRNA-Target Gene Interaction Prediction Algorithms - The Attempt to Compare the Results of Three Algorithms." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31744-1_10.
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Sharma, Aman, and Rinkle Rani. "Machine Learning Perspective in Cancer Research." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch008.
Full textAbstract:
Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this chapter, the authors present a review of the machine learning perspective in cancer research.
Conference papers on the topic "MicroRNA genes prediction"
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Wei, Zhen-lin, Chun-zhen Jiao, Zhi-huan Tian, and Ling Dong. "Computational Prediction of UV-responsible MicroRNA Genes in Vitis vinifera Genome." In 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.159.
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Theofilatos, Konstantinos A., Dimitrios A. Kleftogiannis, Maria Anna V. Rapsomaniki, et al. "A novel pre-miRNA classification approach for the prediction of microRNA genes." In 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010). IEEE, 2010. http://dx.doi.org/10.1109/itab.2010.5687799.
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Yang, Jincai, Chunjie Guo, Xingpeng Jiang, Xiaohua Hu, and Xianjun Shen. "Systematic characterization and prediction of tumor-associated genes in mouse using microrna." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217856.
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Radfar, M. Hossein, Willy Wong, and Quaid D. Morris. "Predicting the target genes of intronic microRNAs using large-scale gene expression data." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626505.
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Sacar, Muserref Duygu, and Jens Allmer. "Data mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene prediction." In 2013 8th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2013. http://dx.doi.org/10.1109/hibit.2013.6661685.
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Nitaya, Tan, Prathan Phumphuang, Pitukpong Pomjalren, and Nilubon Kurubanjerdjit. "MicroRNA-Gene Signatures Prediction for cancers with Drug Discovery." In 2019 4th International Conference on Information Technology (InCIT). IEEE, 2019. http://dx.doi.org/10.1109/incit.2019.8912061.
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Batuwita, Rukshan, and Vasile Palade. "An improved non-comparative classification method for human microRNA gene prediction." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696724.
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