Relevant bibliographies by topics / Bioinformatics, microRNA

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bioinformatics, microRNA'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Bioinformatics, microRNA"

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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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Gaffo, Enrico, Michele Bortolomeazzi, Andrea Bisognin, et al. "MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data." International Journal of Molecular Sciences 21, no. 5 (2020): 1754. http://dx.doi.org/10.3390/ijms21051754.

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MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&moRe2 were in agreement with previous research findings. Moreover, we observed that moRNAs and new microRNAs predicted by miR&moRe2 were downregulated upon the silencing of the microRNA-biogenesis pathway. Further, in a sizeable dataset of human blood cell populations, tens of novel miRNAs and moRNAs were discovered, some of them with significantly varied expression levels among the cell types. Results demonstrate that miR&moRe2 is a valid tool for a comprehensive study of small RNAs generated from microRNA precursors and could help to investigate their biogenesis and function.
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Miskiewicz, J., K. Tomczyk, A. Mickiewicz, J. Sarzynska, and M. Szachniuk. "Bioinformatics Study of Structural Patterns in Plant MicroRNA Precursors." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/6783010.

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According to the RNA world theory, RNAs which stored genetic information and catalyzed chemical reactions had their contribution in the formation of current living organisms. In recent years, researchers studied this molecule diversity, i.a. focusing on small non-coding regulatory RNAs. Among them, of particular interest is evolutionarily ancient, 19–24 nt molecule of microRNA (miRNA). It has been already recognized as a regulator of gene expression in eukaryotes. In plants, miRNA plays a key role in the response to stress conditions and it participates in the process of growth and development. MicroRNAs originate from primary transcripts (pri-miRNA) encoded in the nuclear genome. They are processed from single-stranded stem-loop RNA precursors containing hairpin structures. While the mechanism of mature miRNA production in animals is better understood, its biogenesis in plants remains less clear. Herein, we present the results of bioinformatics analysis aimed at discovering how plant microRNAs are recognized within their precursors (pre-miRNAs). The study has been focused on sequential and structural motif identification in the neighbourhood of microRNA.
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Vrahatis, Aristidis G., Konstantina Dimitrakopoulou, Panos Balomenos, Athanasios K. Tsakalidis, and Anastasios Bezerianos. "CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis." Bioinformatics 32, no. 6 (2015): 884–92. http://dx.doi.org/10.1093/bioinformatics/btv673.

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Abstract Motivation: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific ‘active parts’ of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical ‘themes’—in the form of enriched biologically relevant microRNA-mediated subpathways—that determine the functionality of signaling networks across time. Results: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. Availability and implementation: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/. Contact: tassos.bezerianos@nus.edu.sg. Supplementary information: Supplementary data are available at Bioinformatics online.
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Zhu, Yujie, Yuxin Lin, Wenying Yan, et al. "Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4618323.

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Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.
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Moore, Alyssa C., Jonathan S. Winkjer, and Tsai-Tien Tseng. "Bioinformatics Resources for MicroRNA Discovery." Biomarker Insights 10s4 (January 2015): BMI.S29513. http://dx.doi.org/10.4137/bmi.s29513.

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Biomarker identification is often associated with the diagnosis and evaluation of various diseases. Recently, the role of microRNA (miRNA) has been implicated in the development of diseases, particularly cancer. With the advent of next-generation sequencing, the amount of data on miRNA has increased tremendously in the last decade, requiring new bioinformatics approaches for processing and storing new information. New strategies have been developed in mining these sequencing datasets to allow better understanding toward the actions of miRNAs. As a result, many databases have also been established to disseminate these findings. This review focuses on several curated databases of miRNAs and their targets from both predicted and validated sources.
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Jiang, Limin, Jingjun Zhang, Ping Xuan, and Quan Zou. "BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9565689.

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MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.
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Phuong, Ho Thi Bich, Vien Ngoc Thach, Luong Hoang Ngan, and Le Thi Truc Linh. "Using Bioinformatics to predict potential targets of Microrna-144 in osteoarthritis." ENGINEERING AND TECHNOLOGY 8, no. 1 (2020): 43–52. http://dx.doi.org/10.46223/hcmcoujs.tech.en.8.1.335.2018.

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MicroRNAs are short endogenous non-coding RNA molecules, typically 19-25 nucleotides in length, which negatively regulate gene expression through binding to 3’UTR of target mRNAs, leading to repression of protein translation or target mRNA degradation. MicroRNA-144 (miR-144) was found as an abnormal expression in various diseases, including osteoarthritis (OA). We have identified increased microRNA-144 expression in early phase and
 end stage of OA. However, the molecular mechanism of this increase has not been yet to be determined yet. Using bioinformatics tools, we found more than 4,000 mRNAs that are predicted to be potential direct targets of miR-144,
 including mRNAs involved in the critical signaling pathways in OA e.g. TGFβ/Smad2/3 and WNT/β-catenin. Results from this research provide information for future ex periments to validate miR-144 potential targets.
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Huntley, Rachael P., Barbara Kramarz, Tony Sawford, et al. "Expanding the horizons of microRNA bioinformatics." RNA 24, no. 8 (2018): 1005–17. http://dx.doi.org/10.1261/rna.065565.118.

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Ghosh, Zhumur, Jayprokas Chakrabarti, and Bibekanand Mallick. "miRNomics—The bioinformatics of microRNA genes." Biochemical and Biophysical Research Communications 363, no. 1 (2007): 6–11. http://dx.doi.org/10.1016/j.bbrc.2007.08.030.

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Dissertations / Theses on the topic "Bioinformatics, microRNA"

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Pomyen, Yotsawat. "Exploring microRNA biology using integrative bioinformatics." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24774.

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Deregulation of energy metabolism is one of the emerging hallmarks of cancer required for proliferation and metastasis. MicroRNAs are small RNA molecules that have crucial roles in the regulation of biological processes in organisms, including metabolism. Due to recent discovery of miRNAs in humans, roles of miRNAs in metabolism of tumour cells, and effects these have on cancer patients, are still obscure and in need of expansion. Currently, experimental and computational data on the miRNAs are being analysed by a wide range of statistical methods; however, these methods in their original forms posses many limitations. Therefore, new ways of utilising these statistical methods are needed in order to unravel the roles of miRNAs in cancer metabolism. In this thesis, the roles of a specific miRNA, miR-22, and the three metabolic target genes were investigated through the use of classical statistical methods, revealed that miR-22, the metabolic target genes, and the interactions between them, were beneficial to survival outcome of breast cancer patients. Furthermore, novel combinations of the conventional statistical methods were invented in order to investigate the global miRNA regulations on metabolic target genes. These new procedures were demonstrated by using publicly available data sets. In one analysis, it was found that miRNAs could be divided into six clusters according to the metabolic target genes through a novel combination of statistical methods. A new statistical method was also invented to provide a generalised means to test for clustering based on sets of correlations.
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Kaimal, Vivek. "Computational approaches to study microRNA networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298041682.

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Adai, Alex Tamas. "Uncovering microRNA function through data integration." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3311333.

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Deo, Ameya. "Normalization of microRNA expression levels in Quantitative RT-PCR arrays." Thesis, University of Skövde, School of Life Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4133.

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<p><strong>Background:</strong> Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study.</p><p><strong>Results:</strong> In this study, different normalization methods were tested, which are available in the R packages <em>Affy</em> and <em>qpcrNorm</em> for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that <em>qpcrNorm</em> Quantile normalization method performs best for all methods tested.</p><p><strong>Conclusions:</strong> The <em>qpcrNorm</em> Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.</p>
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Zichner, Thomas. "Building graph models of oncogenesis by using microRNA expression data." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1167.

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<p>MicroRNAs (miRNAs) are a class of small non-coding RNAs that control gene expression by targeting mRNAs and triggering either translation repression or RNA degradation. Several groups pointed out that miRNAs play a major role in several diseases, including cancer. This is assumed since the expression level of several miRNAs differs between normal and cancerous cells. Further, it has been shown that miRNAs are involved in cell proliferation and cell death.</p><p>Because of this role it is suspected that miRNAs could serve as biomarkers to improve tumor classification, therapy selection, or prediction of survival. In this context, it is questioned, among other things, whether miRNA deregulations in cancer cells occur according to some pattern or in a rather random order. With this work we contribute to answering this question by adapting two approaches (Beerenwinkel et al. (J Comput Biol, 2005) and Höglund et al. (Gene Chromosome Canc, 2001)), developed to derive graph models of oncogenesis for chromosomal imbalances, to miRNA expression data and applying them to a breast cancer data set. Further, we evaluated the results by comparing them to results derived from randomly altered versions of the used data set.</p><p>We could show that miRNA deregulations most likely follow a rough temporal order, i.e. some deregulations occur early and some occur late in cancer progression. Thus, it seems to be possible that the expression level of some miRNAs can be used as indicator for the stage of a tumor. Further, our results suggest that the over expression of mir-21 as well as mir-102 are initial events in breast cancer oncogenesis.</p><p>Additionally, we identified a set of miRNAs showing a cluster-like behavior, i.e. their deregulations often occur together in a tumor, but other deregulations are less frequently present. These miRNAs are let-7d, mir-10b, mir-125a, mir-125b, mir-145, mir-206, and mir-210.</p><p>Further, we could confirm the strong relationship between the expression of mir-125a and mir-125b.</p>
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Howe, Eleanor Arden. "MicroRNA expression and activity in high-grade serous ovarian cancer." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:9d17590c-550b-4ae9-ac8d-15387cf70e5f.

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miRNAs are critical modulators in the development and progression of cancer. Emerging evidence suggests that they are drivers of ovarian cancer. A better understanding of the molecular underpinnings of the development, progression and chemoresistance of the disease is critical for the development of new, more effective therapies. Here we explore the expression patterns of miRNAs as they relate to gene expression, as they differ across molecular subtypes of the disease. We examine the correlation structure of miRNA expression with mRNA expression in two distinct genomic datasets and report on patterns in correlation structure in several subsets of the data. We find that the datasets show consistency in their correlation structure, and in the specific miRNA-mRNA pairs that are either highly positively or negatively correlated. The data include a larger number of strong positive and strong negative correlations than would be expected by chance, indicating that biological relationships between the types of data are detectable in these datasets. We further find an enrichment for positively-correlated miRNA-mRNA pairs in which the miRNA is encoded in close proximity to the mRNA. The correlation of miRNA and mRNA is apparently unaffected by miRNA and mRNA expression level; similarly the two molecular subtypes do not contain differences in their correlation. We find that the recently described poorer prognosis, or angiogenic, subtype has a generally lower miRNA activity than the second, non-angiogenic, subtype. The subtypes are characterized by a consistent pattern of differential miRNA expression. We also report on a switch-like relationship between the expression levels of certain miRNAs and the genes that are anticorrelated with them. We propose these miRNAs drive many of the differences in the subtypes both directly, by RISC-mediated repression of target messages and indirectly, by repressing transcription factors that regulate expression in the cell. We build models of patient survival and time-to-relapse based on these miRNA expression data and inferred miRNA activity scores, using several types of univariate and variable selection models. We find essentially no survival-predictive information provided by the RE score data. While the direct miRNA expression measurements may contain some predictive power, we find that a larger dataset and the segretation of that dataset into distinct molecular phenotypes is likely to be necessary to produce a useful model of survival in ovarian cancer.
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Leung, Wing-sze. "Filtering of false positive microRNA candidates by a clustering-based approach." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B41633908.

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Leung, Wing-sze, and 梁穎思. "Filtering of false positive microRNA candidates by a clustering-based approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41633908.

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Hatem, Ayat. "Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398949621.

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Rose, Jarod. "An Investigation and Visualization of MicroRNA Targets and Gene Expressions and Their Use in Classifying Cancer Samples." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302303717.

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Books on the topic "Bioinformatics, microRNA"

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Huang, Jingshan, Glen M. Borchert, Dejing Dou, et al., eds. Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9.

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Schmitz, Ulf. MicroRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools. Springer Netherlands, 2013.

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Bioinformatics in MicroRNA Research. Humana, 2018.

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Microrna Profiling In Cancer A Bioinformatics Perspective. Pan Stanford Publishing, 2009.

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Microrna Cancer Regulation Advanced Concepts Bioinformatics And Systems Biology Tools. Springer, 2012.

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Vera, Julio, Ulf Schmitz, and Olaf Wolkenhauer. MicroRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools. Springer, 2016.

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Book chapters on the topic "Bioinformatics, microRNA"

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Kuo, Chih-Hao, Mark D. Goldberg, Shi-Lung Lin, Shao-Yao Ying, and Jiang F. Zhong. "Identify Intronic MicroRNA with Bioinformatics." In MicroRNA Protocols. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-083-0_6.

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Cardin, Sara-Elizabeth, and Glen M. Borchert. "Viral MicroRNAs, Host MicroRNAs Regulating Viruses, and Bacterial MicroRNA-Like RNAs." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_3.

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Langenberger, David, Sebastian Bartschat, Jana Hertel, Steve Hoffmann, Hakim Tafer, and Peter F. Stadler. "MicroRNA or Not MicroRNA?" In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22825-4_1.

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King, Valeria M., and Glen M. Borchert. "MicroRNA Expression: Protein Participants in MicroRNA Regulation." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_2.

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Can, Tolga. "Introduction to Bioinformatics." In miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-748-8_4.

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Loganantharaj, Rasiah, and Thomas A. Randall. "The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_10.

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Xue, Min, Ying Zhuo, and Bin Shan. "MicroRNAs, Long Noncoding RNAs, and Their Functions in Human Disease." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_1.

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Liu, Feng. "Genomic Regulation of MicroRNA Expression in Disease Development." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_11.

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Hu, Yue, Wenjun Lan, and Daniel Miller. "Next-Generation Sequencing for MicroRNA Expression Profile." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_12.

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Hu, Yue, Wenjun Lan, and Daniel Miller. "Handling High-Dimension (High-Feature) MicroRNA Data." In Bioinformatics in MicroRNA Research. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7046-9_13.

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Conference papers on the topic "Bioinformatics, microRNA"

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YANG, LIANG HUAI, WYNNE HSU, MONG LI LEE, and LIMSOON WONG. "IDENTIFICATION OF MICRORNA PRECURSORS VIA SVM." In 4th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2005. http://dx.doi.org/10.1142/9781860947292_0030.

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Narcı, Kübra, Hasan Oğul, and Mahinur Akkaya. "Sequence-based MicroRNA Clustering." In 7th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005552901070116.

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Hu, Wei, Lu Li, Yingyu Meng, Ting Li, Yuhua Tian, and Mu Li. "Preparation of MicroRNA chip from deer antler tip tissue and screening of differential expression MicroRNAs." In International Conference on Medical Engineering and Bioinformatics. WIT Press, 2014. http://dx.doi.org/10.2495/meb140501.

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Tran, Nhat, Vinay Abhyankar, KyTai Nguyen, Ishfaq Ahmad, Jon Weidanz, and Jean Gao. "MicroRNA dysregulational synergistic network: Learning context-specific MicroRNA dysregulations in lung cancer subtypes." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217640.

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Hassani, Mohsen Sheikh, and James R. Green. "Active Learning for microRNA Prediction." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621144.

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"MicroRNA Prioritization based on Target Profile Similarities." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004925502780285.

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Yue, Dong, Yidong Chen, Shou-Jiang Gao, and Yufei Huang. "Computational prediction of MicroRNA regulatory pathways." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703833.

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Guo, Li, and Zuhong Lu. "MicroRNA Locus Expression Analysis with Phylogenetic Relationship Based on MicroRNA Control from High Throughput Sequencing Data." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516330.

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"MicroRNA content of horse and human milk exosomes." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-062.

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"An architecture-independent algorithm for microRNA target prediction." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-094.

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