Relevant bibliographies by topics / MicroRNA analysis

Contents

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

Academic literature on the topic 'MicroRNA analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'MicroRNA analysis.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "MicroRNA analysis"

1

Qin, Li-Xuan. "An Integrative Analysis of microRNA and mRNA Expression–-A Case Study." Cancer Informatics 6 (January 2008): CIN.S633. http://dx.doi.org/10.4137/cin.s633.

Full text
Abstract:
Background MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression. Results We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression. Discussion Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure–the co-clustering frequency–to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.
APA, Harvard, Vancouver, ISO, and other styles
2

Zou, Quan, Jinjin Li, Qingqi Hong, Ziyu Lin, Yun Wu, Hua Shi, and Ying Ju. "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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

Varga, Zoltán V., Ágnes Zvara, Nóra Faragó, Gabriella F. Kocsis, Márton Pipicz, Renáta Gáspár, Péter Bencsik, et al. "MicroRNAs associated with ischemia-reperfusion injury and cardioprotection by ischemic pre- and postconditioning: protectomiRs." American Journal of Physiology-Heart and Circulatory Physiology 307, no. 2 (July 15, 2014): H216—H227. http://dx.doi.org/10.1152/ajpheart.00812.2013.

Full text
Abstract:
We aimed to characterize early changes in microRNA expression in acute cardioprotection by ischemic pre- and postconditioning in rat hearts. Hearts isolated from male Wistar rats were subjected to 1) time-matched nonischemic perfusion, 2) ischemia-reperfusion (30 min of coronary occlusion and 120 min of reperfusion), 3) preconditioning (3 × 5 min of coronary occlusion) followed by ischemia-reperfusion, or 4) ischemia-reperfusion with postconditioning (6 × 10 s of global ischemia-reperfusion at the onset of reperfusion). Infarct size was significantly reduced by both interventions. Of 350 different microRNAs assessed by microarray analysis, 147–160 microRNAs showed detectable expression levels. Compared with microRNA alterations induced by ischemia-reperfusion versus time-matched nonischemic controls, five microRNAs were significantly affected by both pre- and postconditioning (microRNA-125b*, microRNA-139-3p, microRNA-320, microRNA-532-3p, and microRNA-188), four microRNAs were significantly affected by preconditioning (microRNA-487b, microRNA-139-5p, microRNA-192, and microRNA-212), and nine microRNAs were significantly affected by postconditioning (microRNA-1, microRNA let-7i, microRNA let-7e, microRNA let-7b, microRNA-181a, microRNA-208, microRNA-328, microRNA-335, and microRNA-503). Expression of randomly selected microRNAs was validated by quantitative real-time PCR. By a systematic comparison of the direction of microRNA expression changes in all groups, we identified microRNAs, specific mimics, or antagomiRs that may have pre- and postconditioning-like cardioprotective effects (protectomiRs). Transfection of selected protectomiRs (mimics of microRNA-139-5p, microRNA-125b*, microRNA let-7b, and inhibitor of microRNA-487b) into cardiac myocytes subjected to simulated ischemia-reperfusion showed a significant cytoprotective effect. This is the first demonstration that the ischemia-reperfusion-induced microRNA expression profile is significantly influenced by both pre- and postconditioning, which shows the involvement of microRNAs in cardioprotective signaling. Moreover, by analysis of microRNA expression patterns in cardioprotection by pre- and postconditioning, specific protectomiRs can be revealed as potential therapeutic tools for the treatment of ischemia-reperfusion injury.
APA, Harvard, Vancouver, ISO, and other styles
4

Martinez-Fierro, Margarita L., and Idalia Garza-Veloz. "Analysis of Circulating microRNA Signatures and Preeclampsia Development." Cells 10, no. 5 (April 24, 2021): 1003. http://dx.doi.org/10.3390/cells10051003.

Full text
Abstract:
microRNAs are important regulators of cell processes and have been proposed as potential preeclampsia biomarkers. We evaluated serum microRNA expression profiling to identify microRNAs involved in preeclampsia development. Serum microRNA expression profiling was evaluated at 12, 16, and 20 weeks of gestation (WG), and at the time of preeclampsia diagnosis. Two groups were evaluated using TaqMan low-density array plates: a control group with 18 normotensive pregnant women and a case group with 16 patients who developed preeclampsia during the follow-up period. Fifty-three circulating microRNAs were differentially expressed between groups (p < 0.05). Compared with controls, hsa-miR-628-3p showed the highest relative quantity values (at 12 WG = 7.7 and at 20 WG = 3.45) and the hsa-miRs -151a-3p and -573 remained differentially expressed from 16 to 20 WG (p < 0.05). Signaling pathways including cancer-related, axon guidance, Neurotrophin, GnRH, VEGF, and B/T cell receptor, were most commonly altered. Further target gene prediction revealed that nuclear factor of activated T-cells 5 gene was included among the transcriptional targets of preeclampsia-modulated microRNAs. Specific microRNAs including hsa-miRs -628-3p, -151a-3p, and -573 were differentially expressed in serum of pregnant women before they developed preeclampsia compared with controls and their participation in the preeclampsia development should be considered.
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Wei, Dequang Zhou, Xiaolong Shi, Chao Tang, Xueying Xie, Jing Tu, Qinyu Ge, and Zuhong Lu. "Global Egr1-miRNAs Binding Analysis in PMA-Induced K562 Cells Using ChIP-Seq." Journal of Biomedicine and Biotechnology 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/867517.

Full text
Abstract:
Although much is known about microRNAs' regulation in gene expression and their contributions in cell fate, to date, globally lineage-(cell-) specific identification of the binding events between a transcription factor and its targeting microRNA genes is still waiting for elucidation. In this paper, we performed a ChIP-Seq experiment to find the targeting microRNA genes of a transcription factor, Egr1, in human erythroleukemia cell line K562. We found Egr1 binding sites near the promoters of 124 distinct microRNA genes, accounting for about 42% of the miRNAs which have high-confidence predicted promoters (294). We also found EGR1 bind to another 63 pre-miRNAs. We chose 12 of the 187 microRNAs with Egr1 binding sites to perform ChIP-PCR assays and the positive binding signal from ChIP-PCR confirmed the ChIP-Seq results. Our experiments provide the first global binding profile between Egr1 and its targeting microRNA genes in PMA-treated K562 cells, which may facilitate the understanding of pathways controlling microRNA biology in this specific cell line.
APA, Harvard, Vancouver, ISO, and other styles
6

Alexiou, Panagiotis, Manolis Maragkakis, and Artemis G. Hatzigeorgiou. "Online resources for microRNA analysis." Journal of Nucleic Acids Investigation 2, no. 1 (February 25, 2011): 4. http://dx.doi.org/10.4081/jnai.2011.2161.

Full text
Abstract:
The use of online tools for bioinformatics analyses is becoming increasingly widespread. Resources specific to the field of microRNAs are available, varying in scope and usability. Online tools are the most useful for casual as well as power users since they need no installation, are hardware independent and are used mostly through graphic user interfaces and links to external sources. Here, we present an overview of useful online resources that have to do with microRNA genomics, gene finding, target prediction and functional analysis.
APA, Harvard, Vancouver, ISO, and other styles
7

Fan, Lichao, Xiaoting Yu, Ziling Huang, Shaoqiang Zheng, Yongxin Zhou, Hanjing Lv, Yu Zeng, Jin-Fu Xu, Xuyou Zhu, and Xianghua Yi. "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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

Jongen-Lavrencic, Mojca, Su Ming Sun, Menno K. Dijkstra, Peter J. M. Valk, and Bob Löwenberg. "MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia." Blood 111, no. 10 (May 15, 2008): 5078–85. http://dx.doi.org/10.1182/blood-2008-01-133355.

Full text
Abstract:
Abstract Acute myeloid leukemia (AML) is a highly diverse disease characterized by various cytogenetic and molecular abnormalities. MicroRNAs are small noncoding RNAs that show variable expression during myeloid differentiation. MicroRNA expression in marrow blasts in 215 cases of newly diagnosed and (cyto)genetically defined AML was assessed using quantitative reverse-transcription–polymerase chain reaction (RT-PCR) for 260 human microRNAs. In the same series, mRNA gene expression profiles were established, allowing a direct comparison between microRNA and mRNA expression. We show that microRNA expression profiling following unsupervised analysis reveals distinctive microRNA signatures that correlate with cytogenetic and molecular subtypes of AML (ie, AMLs with t(8;21), t(15;17), inv(16), NPM1, and CEBPA mutations). Significantly differentially expressed microRNAs for genetic subtypes of AML were identified. Specific microRNAs with established oncogenic and tumor suppressor functions, such as microRNA-155, microRNA-21, and let-7, appear to be associated with particular subtypes. Combinations of selected sets of microRNAs could predict cytogenetically normal AML with mutations in the genes of NPM1 and CEBPA and FLT3-ITD with similar accuracy as mRNA probe set combinations defined by gene expression profiling. MicroRNA expression apparently bears specific relationships to the heterogeneous pathobiology of AML. Distinctive microRNA signatures appear of potential value in the clinical diagnosis of AML.
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Jinfeng, Fengyuan Che, and Jinling Zhang. "Cell-free microRNAs as non-invasive biomarkers in glioma: a diagnostic meta-analysis." International Journal of Biological Markers 34, no. 3 (April 10, 2019): 232–42. http://dx.doi.org/10.1177/1724600819840033.

Full text
Abstract:
Objective: Since the diagnostic value of microRNAs for detecting glioma is contentious, we aimed to carry out a meta-analysis to synthetically evaluate the diagnostic significance of cell-free microRNAs in cerebrospinal fluid and blood in the detection of glioma. Methods: A systematic document retrieval of public databases was performed to obtain eligible studies. Specificity was applied to draw the summary receiver operator characteristic (SROC) curve against sensitivity, and the pooled diagnostic efficiency was assessed by generating the area under the SROC curve. Meta-regression and subgroup analyses were utilized to explore the latent sources of heterogeneity. STATA 12.0, RevMan 5.3 and Meta-DiSc 1.4 were used to conduct all statistical analyses. Results: A total of 47 studies from 20 articles comprising 2262 glioma patients and 1986 controls were included in our meta-analysis. Cell-free microRNAs exhibited relatively good diagnostic efficiency in glioma detection, with a sensitivity of 0.83, a specificity of 0.87, and an area under the curve of 0.91. Cell-free miR-21 performed best with pooled area under the curve of 0.88, followed by miR-125 and miR-222. Subgroup analyses and meta-regression indicated that there was substantial heterogeneity existing among the studies, which was in part caused by sample size, World Health Organization grade, reference gene, microRNA origin (extracellular vesicles or non-extracellular vesicle-based-microRNA), microRNA profiling (single- or multiple-microRNA), specimen types, and ethnicity. Conclusions: Cell-free microRNAs in cerebrospinal fluid and blood may play an important role as promising non-invasive biomarkers in the early diagnosis of glioma. Further comprehensive forward-looking research is required to validate their clinical significance in glioma diagnosis.
APA, Harvard, Vancouver, ISO, and other styles
10

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 (November 14, 2015): 884–92. http://dx.doi.org/10.1093/bioinformatics/btv673.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "MicroRNA analysis"

1

Weinstein, Earl G. 1974. "MicroRNA cloning and bioinformatic analysis." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8390.

Full text
Abstract:
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Biology, 2002.
Includes bibliographical references.
Part I. Two gene-regulatory noncoding RNAs (ncRNAs), let-7 RNA and lin-4 RNA, were previously discovered in the C. elegans genome. The let-7 gene is conserved across a wide range of genomes, suggesting that these ncRNAs represent a wider class of gene-regulatory RNAs. Both lin-4 and let-7 RNAs are generated from stem-loop precursor RNAs, and share a common biochemical signature, namely 5'-terminal phosphate and 3'-terminal hydroxyl groups. We refer to ncRNAs that share the characteristic size, biochemical signature, and precursor structures of let-7 and lin-4 as microRNAs (miRNAs). The size of this class of genes, and its prevalence in other genomes, are unknown. Therefore, we developed an experimental and bioinformatics strategy to identify novel miRNA genes. We discovered a total of 75 miRNA genes in the C. elegans genome, and orthologues for a majority of these were computationally identified in the C. briggsae, D. melanogaster or H. sapiens genomes. Northern analysis was used to confirm and analyze the expression of these miRNAs. The data set has implications for understanding miRNA gene regulation, miRNA processing, and regulation of miRNA genes. Part II. Directed molecular evolution has previously been applied to generate RNAs with novel structures and functions. This method works because nucleic acids can be selected, randomized, amplified and characterized using polymerase chain reaction (PCR)-based methods. Here we present a novel method for extending directed molecular evolution to the realm of peptide selections by linking a peptide to its encoding mRNA.
(cont.) A proof of principle selection for two different peptides indicates that this tRNA should prove useful in discovering more complex protein molecules using directed molecular evolution.
by Earl G. Weinstein.
Ph.D.
APA, Harvard, Vancouver, ISO, and other styles
2

Wang, Qi. "Using Imputed Microrna Regulation Based on Weighted Ranked Expression and Putative Microrna Targets and Analysis of Variance to Select Micrornas for Predicting Prostate Cancer Recurrence." Thesis, North Dakota State University, 2014. https://hdl.handle.net/10365/27341.

Full text
Abstract:
Imputed microRNA regulation based on weighted ranked expression and putative microRNA targets (IMRE) is a method to predict microRNA regulation from genome-wide gene expression. A false discovery rate (FDR) for each microRNA is calculated using the expression of the microRNA putative targets to analyze the regulation between different conditions. FDR is calculated to identify the differences of gene expression. The dataset used in this research is the microarray gene expression of 596 patients with prostate cancer. This dataset includes three different phenotypes: PSA (Prostate-Specific Antigen recurrence), Systemic (Systemic Disease Progression) and NED (No Evidence of Disease). We used the IMRE and ANOVA methods to analyze the dataset and identified several microRNA candidates that can be used to predict PSA recurrence and systemic disease progression in prostate cancer patients.
APA, Harvard, Vancouver, ISO, and other styles
3

Goldstein, L. D. "Statistical analysis of microRNA expression and related data." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599479.

Full text
Abstract:
The first part of this thesis is concerned with the analysis of miRNA expression data obtained by bead-based flow cytometric profiling. Based on data obtained from 93 human breast cancer samples, we assess the association of individual miRNAs with clinical factors and molecular tumour subtype. We investigate potential mechanisms of miRNA deregulation by analysing matched data on DNA copy number and mRNA expression. We describe an analysis of miRNA and mRNA expression during normal postnatal mouse mammary gland development, a model system for the study of human breast cancer. In the second part of this thesis we are concerned with the analysis of mRNA expression data with a focus on miRNA targets. We develop a statistical method to assess whether predicted miRNA targets show expression levels that are different compared to those of suitably chosen control genes. We find that, across human breast cancers and during mouse mammary gland development, the targets of most miRNAs do not show systematic changes in their expression. In cases where targets are differentially expressed, changes in expression are mostly consistent with miRNA-mediated regulation. We characterize the molecular function of the miRNA miR-124 in the nematode Caenorhabditis elegans. Many targets of miR-124 are coexpressed with and actively repressed by miR-124. Reduced expression levels of miR-124 targets in cells that express the miRNA compared to the rest of the animal are mostly due to direct miRNA-mediated repression in the case of evolutionary conserved targets and due to both direct repression and other regulatory mechanisms in the case of nonconserved targets.
APA, Harvard, Vancouver, ISO, and other styles
4

Alvarez-Saavedra, Ezequiel (Ezequiel Andrès). "Functional analysis of the microRNA genes of C. elegans." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/42948.

Full text
Abstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 231-252).
MicroRNAs (miRNAs) were discovered in C. elegans during studies of the control of developmental timing. MicroRNAs are a large class of short non-coding RNAs found in many viruses, plants and animals that regulate gene expression through sequence-specific base-pairing with target mRNAs. Initial studies since the identification of many miRNAs only six years ago, have revealed their very diverse roles in biology. Yet, few miRNAs have been studied using loss-of-function mutations. We have generated deletion mutations in 87 miRNA genes in C. elegans, and performed an initial characterization of the 95 miRNA mutants available (86% of known C. elegans miRNAs). We found that the majority of miRNAs are not essential for the viability or development of C. elegans, and mutations in most miRNA genes do not result in grossly abnormal phenotypes. Within species, many miRNAs can be grouped into families according to their sequence similarities. We generated a collection of 12 multiply mutant C. elegans strains that each lacks an entire miRNA family. We found that at least four families display synthetic abnormalities, indicating that miRNAs within a family can have redundant functions. While single mutants are superficially wild-type, mutants deleted for all members of the mir-35 or the mir-51 families show embryonic or early larval lethality, mutants deleted for all members of the mir-58 family show an egglaying defect, and mutants deleted for some members of the let-7 family show defects in developmental timing. We developed a microarray technology suitable for detecting microRNAs and used this microarray to determine the profile of microRNAs expressed in the developing mouse brain. We observed a temporal wave of expression of microRNAs, suggesting that microRNAs play important roles in the development of the mammalian brain.
(cont.) We also performed a systematic expression analysis of 334 samples covering diverse human cancers, using a bead-based flow cytometric miRNA expression profiling method we developed. The miRNA profiles reflect the developmental lineage and differentiation state of the tumors, and reveal a general down-regulation of miRNAs in tumors compared to normal tissues.
by Ezequiel Alvarez-Saavedra.
Ph.D.
APA, Harvard, Vancouver, ISO, and other styles
5

Bexon, Kimerley Jane. "Forensic microRNA analysis of body fluids relating to sexual assaults." Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/34347/.

Full text
Abstract:
DNA profiling has become a universal technique for identifying individuals for evidential use in courts of law. In more complex cases such as sexual assaults, identifying the origin of a stain or sample offers valuable information as to the events that occurred. Currently, many ‘in service’ body fluid identification (BFID) techniques are presumptive, require significant sample volumes and generate false positives. As such, the development of a highly specific and reliable BFID technique would be highly beneficial to forensic scientists in provide more informative and reliable evidence. MicroRNAs (miRNA) are short, stable, non-coding RNA’s which modulate gene expression. Expression of some of these miRNA are body fluid specific, making them a potentially robust tool for BFID. The possibility for the integration of a robust, miRNA based BFID technology for forensic casework employing stem-loop reverse transcription and qPCR analysis was the theme of the research presented here. To be incorporated into the workflow of current forensic laboratories, the protocol must be able to be carried out alongside current techniques with limited addition of cost, equipment, analysts and time. A range of custom designed miRNA markers were analysed on vaginal material, menstrual blood, saliva, venous blood, semen, seminal fluid and skin. Screening indicated specificity of hsa mir-124 to vaginal material, hsa-mir-10a, 135a and 888 to semen, hsa-mir-412 and 507 to menstrual blood, hsa-mir-144-3, 144-5, 142 and 451 to blood and although highly expressed in saliva, hsa-mir-205 was also observed in vaginal material. Universal expression was observed in hsa-mir-93, 508, 1260b and SNORD 47 providing a means of normalisation through the designation of these markers as endogenous controls. A combined panel of markers are presented which were capable of identifying all body fluids, excluding skin from single stains. The panel was successful at identifying certain mixtures such as semen within vaginal material but was unable to confirm saliva presence within vaginal material. Screening of hsa-mir 205 within vaginal material uncovered the observation that hsa-mir-205 was impacted by the use of female contraception. Once a full BFID panel was generated the robustness of the markers was further analysed across the menstrual cycle. No significant difference (p > 0.001) was seen in markers highly expressed in vaginal material during screening (hsa-mir-124, 203a, 205). Expression of non specific markers highlighted the importance of the optimisation of input miRNA. Differential extraction of genetic material was found to be detrimental to miRNA sample integrity. As such, total DNA extraction was employed for vaginal swabs obtained from volunteers following unprotected sexual intercourse, markers hsa mir-10a, 135a and 888 were able to successfully detect semen presence for up to 96 hours. The data generated to date has highlighted a number of miRNA markers that provide a platform for BFID. The developed protocol is reliable and robust; requiring minimal optimisation and is capable of integration with current laboratory workflow with minimum implications to time and cost. The markers identified for BFID have been implemented within studies that are representative of real case scenarios, and have demonstrated their ability to be stable, specific and successful in the identification of certain body fluids. Overall, this research showcases a reliable and body fluid specific protocol capable of being performed alongside DNA profiling.
APA, Harvard, Vancouver, ISO, and other styles
6

Lehrbach, Nicolas John. "Genetic analysis of microRNA mechanisms and functions in C. elegans." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609195.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Moriarty, Charlotte M. Harwood. "Functional Analysis of MicroRNA-10b in Breast Carcinoma: A Dissertation." eScholarship@UMMS, 2009. https://escholarship.umassmed.edu/gsbs_diss/426.

Full text
Abstract:
MicroRNAs (miRNAs) represent a class of small noncoding RNAs that regulate gene expression. Recent studies have shown that miRNAs are mis-expressed in various human cancers and that some miRNAs have the potential to act as tumor suppressors or oncogenes. MiR-10b is one miRNA that has been shown to be deregulated in breast cancer. However, current findings regarding miR-10b’s role in breast cancer are controversial. MiR-10b was originally reported to be downregulated in breast cancer compared to normal breast tissue. Subsequently, miR-10b was argued to be upregulated in metastatic breast cancer cell lines, acting as a potent pro-metastatic agent via regulation of HOXD10. This report was soon challenged by another group who reported that miR-10b expression in a large patient cohort correlated inversely and significantly with tumor size, grade, and vascular invasion, but did not correlate with development of distant metastases or survival. These latter data suggest that miR-10b may impede specific functions associated with breast cancer progression. In this thesis, I present my analysis of miR-10b function in breast carcinoma cells, which revealed that it suppresses their migration and invasion. To define a mechanism that accounts for this suppressive function, I identified T-lymphoma invasion and metastasis 1 (TIAM1), a guanine nucleotide exchange factor for Rac1, as a miR-10b target and demonstrated that miR-10b inhibits TIAM1-dependent Rac1 activation, migration, and invasion. In addition, I identified the VEGF receptor fms-related tyrosine kinase 1 (FLT-1) as a second target of miR-10b and discovered a novel function for FLT-1 in promoting breast carcinoma cell migration and invasion. My results show, for the first time, that Rac activation can be regulated by a specific miRNA and provide a novel mechanism for the regulation of TIAM1 and FLT-1 in breast cancer. These data support the conclusion from clinical data that miR-10b expression correlates inversely with breast cancer progression, and suggest that miR-10b functions to impede breast carcinoma progression by regulating key target genes involved in cell motility.
APA, Harvard, Vancouver, ISO, and other styles
8

Woodcock, M. Ryan. "Network Analysis and Comparative Phylogenomics of MicroRNAs and their Respective Messenger RNA Targets Using Twelve Drosophila species." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/155.

Full text
Abstract:
MicroRNAs represent a special class of small (~21–25 nucleotides) non-coding RNA molecules which exert powerful post-transcriptional control over gene expression in eukaryotes. Indeed microRNAs likely represent the most abundant class of regulators in animal gene regulatory networks. This study describes the recovery and network analyses of a suite of homologous microRNA targets recovered through two different prediction methods for whole gene regions across twelve Drosophila species. Phylogenetic criteria under an accepted tree topology were used as a reference frame to 1) make inference into microRNA-target predictions, 2) study mathematical properties of microRNA-gene regulatory networks, 3) and conduct novel phylogenetic analyses using character data derived from weighted edges of the microRNA-target networks. This study investigates the evidences of natural selection and phylogenetic signatures inherent within the microRNA regulatory networks and quantifies time and mutation necessary to rewire a microRNA regulatory network. Selective factors that appear to operate upon seed aptamers include cooperativity (redundancy) of interactions and transcript length. Topological analyses of microRNA regulatory networks recovered significant enrichment for a motif possessing a redundant link in all twelve species sampled. This would suggest that optimization of the whole interactome topology itself has been historically subject to natural selection where resilience to attack have offered selective advantage. It seems that only a modest number of microRNA–mRNA interactions exhibit conservation over Drosophila cladogenesis. The decrease in conserved microRNA-target interactions with increasing phylogenetic distance exhibited a cure typical of a saturation phenomena. Scale free properties of a network intersection of microRNA target predictions methods were found to transect taxonomic hierarchy.
APA, Harvard, Vancouver, ISO, and other styles
9

Bracht, John Russell. "Analysis of lin-4 microRNA biogenesis and function in C. elegans." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3378519.

Full text
Abstract:
Thesis (Ph. D.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed Oct. 21, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 139-150).
APA, Harvard, Vancouver, ISO, and other styles
10

Lopes, Ivani de Oliveira Negrão. "Analysis of microRNA precursors in multiple species by data mining techniques." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19092014-155038/.

Full text
Abstract:
RNA Sequencing has recently emerged as a breakthrough technology for microRNA (miRNA) discovery. This technology has allowed the discovery of thousands of miRNAs in a large number of species. However, despite the benefits of this technology, it also carries its own limitations, including the need for sequencing read libraries and of the genome. Differently, ab initio computational methods need only the genome as input to search for genonic locus likely to give rise to novel miRNAs. In the core of most of these methods, there are predictive models induced by using data mining techniques able to distinguish between real (positive) and pseudo (negative) miRNA precursors (pre-miRNA). Nevertheless, the applicability of current literature ab initio methods have been compromised by high false detection rates and/or by other computational difficulties. In this work, we investigated how the main aspects involved in the induction of predictive models for pre-miRNA affect the predictive performance. Particularly, we evaluate the discriminant power of feature sets proposed in the literature, whose computational costs and composition vary widely. The computational experiments were carried out using sequence data from 45 species, which covered species from eight phyla. The predictive performance of the classification models induced using large training set sizes (≥ 1; 608) composed of instances extracted from real and pseudo human pre-miRNA sequences did not differ significantly among the feature sets that lead to the maximal accuracies. Moreover, the differences in the predictive performances obtained by these models, due to the learning algorithms, were neglectable. Inspired by these results, we obtained a feature set which can be computed 34 times faster than the less costly among those feature sets, producing the maximal accuracies, albeit the proposed feature set has achieved accuracy within 0.1% of the maximal accuracies. When classification models using the elements previously discussed were induced using small training sets (120) from 45 species, we showed that the feature sets that produced the highest accuracies in the classification of human sequences were also more likely to produce higher accuracies for other species. Nevertheless, we showed that the learning complexity of pre-miRNAs vary strongly among species, even among those from the same phylum. These results showed that the existence of specie specific features indicated in previous studies may be correlated with the learning complexity. As a consequence, the predictive accuracies of models induced with different species and same features and instances spaces vary largely. In our results, we show that the use of training examples from species phylogenetically more complex may increase the predictive performances for less complex species. Finally, by using ensembles of computationally less costly feature sets, we showed alternative ways to increase the predictive performance for many species while keeping the computational costs of the analysis lower than those using the feature sets from the literature. Since in miRNA discovery the number of putative miRNA loci is in the order of millions, the analysis of putative miRNAs using a computationally expensive feature set and or inaccurate models would be wasteful or even unfeasible for large genomes. In this work, we explore most of the learning aspects implemented in current ab initio pre-miRNA prediction tools, which may lead to the development of new efficient ab initio pre-miRNA discovery tools
O sequenciamento de pequenos RNAs surgiu recentemente como uma tecnologia inovadora na descoberta de microRNAs (miRNA). Essa tecnologia tem facilitado a descoberta de milhares de miRNAs em um grande número de espécies. No entanto, apesar dos benefícios dessa tecnologia, ela apresenta desafios, como a necessidade de construir uma biblioteca de pequenos RNAs, além do genoma. Diferentemente, métodos computacionais ab initio buscam diretamente no genoma regiões prováveis de conter miRNAs. A maioria desses métodos usam modelos preditivos capazes de distinguir entre os verdadeiros (positivos) e pseudo precursores de miRNA - pre-miRNA - (negativos), os quais são induzidos utilizando técnicas de mineração de dados. No entanto, a aplicabilidade de métodos ab initio da literatura atual é limitada pelas altas taxas de falsos positivos e/ou por outras dificuldades computacionais, como o elevado tempo necessário para calcular um conjunto de atributos. Neste trabalho, investigamos como os principais aspectos envolvidos na indução de modelos preditivos de pre-miRNA afetam o desempenho preditivo. Particularmente, avaliamos a capacidade discriminatória de conjuntos de atributos propostos na literatura, cujos custos computacionais e a composição variam amplamente. Os experimentos computacionais foram realizados utilizando dados de sequências positivas e negativas de 45 espécies, cobrindo espécies de oito filos. Os resultados mostraram que o desempenho preditivo de classificadores induzidos utilizando conjuntos de treinamento com 1608 ou mais vetores de atributos calculados de sequências humanas não diferiram significativamente, entre os conjuntos de atributos que produziram as maiores acurácias. Além disso, as diferenças entre os desempenhos preditivos de classificadores induzidos por diferentes algoritmos de aprendizado, utilizando um mesmo conjunto de atributos, foram pequenas ou não significantes. Esses resultados inspiraram a obtenção de um conjunto de atributos menor e que pode ser calculado até 34 vezes mais rapidamente do que o conjunto de atributos menos custoso produzindo máxima acurácia, embora a acurácia produzida pelo conjunto proposto não difere em mais de 0.1% das acurácias máximas. Quando esses experimentos foram executados utilizando vetores de atributos calculados de sequências de outras 44 espécies, os resultados mostraram que os conjuntos de atributos que produziram modelos com as maiores acurácias utilizando vetores calculados de sequências humanas também produziram as maiores acurácias quando pequenos conjuntos de treinamento (120) calculados de exemplos de outras espécies foram utilizadas. No entanto, a análise destes modelos mostrou que a complexidade de aprendizado varia amplamente entre as espécies, mesmo entre aquelas pertencentes a um mesmo filo. Esses resultados mostram que a existência de características espécificas em pre-miRNAs de certas espécies sugerida em estudos anteriores pode estar correlacionada com a complexidade de aprendizado. Consequentemente, a acurácia de modelos induzidos utilizando um mesmo conjunto de atributos e um mesmo algoritmo de aprendizado varia amplamente entre as espécies. i Os resultados também mostraram que o uso de exemplos de espécies filogeneticamente mais complexas pode aumentar o desempenho preditivo de espécies menos complexas. Por último, experimentos computacionais utilizando técnicas de ensemble mostraram estratégias alternativas para o desenvolvimento de novos modelos para predição de pre-miRNA com maior probabilidade de obter maior desempenho preditivo do que estratégias atuais, embora o custo computacional dos atributos seja inferior. Uma vez que a descoberta de miRNAs envolve a análise de milhares de regiões genômicas, a aplicação prática de modelos preditivos de baixa acurácia e/ou que dependem de atributos computacionalmente custosos pode ser inviável em análises de grandes genomas. Neste trabalho, apresentamos e discutimos os resultados de experimentos computacionais investigando o potencial de diversas estratégias utilizadas na indução de modelos preditivos para predição ab initio de pre-miRNAs, que podem levar ao desenvolvimento de ferramentas ab initio de maior aplicabilidade prática
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "MicroRNA analysis"

1

Yousef, Malik, and Jens Allmer, eds. miRNomics: MicroRNA Biology and Computational Analysis. Totowa, NJ: Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-748-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

RNAi and microRNA-mediated gene regulation in stem cells: Methods, protocols, and applications. New York: Humana Press, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Zhiguo. MicroRNA expression detection methods. Heidelberg: Springer, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chen, Pinhong. Static crosstalk-noise analysis: For deep sub-micron digital designs. Norwell, MA: Kluwer Academic Publishers, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Mirnomics Microrna Biology And Computational Analysis. Humana Press Inc., 2013.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Dahiya, Neetu. MicroRNA Let-7: Role in Human Diseases and Drug Discovery. Nova Science Publishers, Incorporated, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Singh, Shree Ram, and Pranela Rameshwar. MicroRNA in Development and in the Progression of Cancer. Springer, 2016.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Singh, Shree Ram, and Pranela Rameshwar. MicroRNA in Development and in the Progression of Cancer. Springer, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Center, Ames Research, ed. High resolution surface analysis by Microarea Auger analysis: Computerization and characterization. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Pinhong. Static Crosstalk-Noise Analysis: For Deep Sub-Micron Digital Designs. Springer, 2013.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "MicroRNA analysis"

1

Xu, Jianzhen, and Chi-Wai Wong. "Enrichment Analysis of miRNA Targets." In MicroRNA Protocols, 91–103. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-083-0_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Muiwo, Pamchui, Priyatama Pandey, and Alok Bhattacharya. "Computational Analysis of miRNAs, Their Target Sequences and Their Role in Gene Regulatory Networks." In MicroRNA, 21–38. Boca Raton : Taylor & Francis, 2018. | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: CRC Press, 2018. http://dx.doi.org/10.1201/b22195-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kim, Ju Han. "MicroRNA Data Analysis." In Genome Data Analysis, 159–72. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Megraw, Molly, and Artemis G. Hatzigeorgiou. "MicroRNA Promoter Analysis." In Methods in Molecular Biology, 149–61. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-005-2_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Isik, Meltem, and Eugene Berezikov. "Expression Pattern Analysis of MicroRNAs in Caenorhabditis elegans." In MicroRNA Protocols, 129–41. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-083-0_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Prévot, Pierre-Paul, Marie-Laure Volvert, Alexander Deiters, and Laurent Nguyen. "Functional Analysis of Cortical Neuron Migration Using miRNA Silencing." In MicroRNA Technologies, 73–88. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/7657_2016_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kim, Hak Hee, Monichan Phay, and Soonmoon Yoo. "Isolation and Quantitative Analysis of Axonal Small Noncoding RNAs." In MicroRNA Technologies, 147–59. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/7657_2016_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Da Silveira, Juliano, Gabriella M. Andrade, Felipe Perecin, Flávio Vieira Meireles, Quinton A. Winger, and Gerrit J. Bouma. "Isolation and Analysis of Exosomal MicroRNAs from Ovarian Follicular Fluid." In MicroRNA Protocols, 53–63. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7601-0_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Starega-Roslan, Julia, and Wlodzimierz J. Krzyzosiak. "Analysis of MicroRNA Length Variety Generated by Recombinant Human Dicer." In MicroRNA Protocols, 21–34. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-083-0_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wu, Wei. "MicroRNA Sequencing Data Analysis Toolkits." In MicroRNA and Cancer, 211–15. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7435-1_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "MicroRNA analysis"

1

Igdeli, Muratcan, Atif Yilmaz, and Hasan Ogul. "Sequence analysis to predict microRNA chemotherapy resistance." In 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737427.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kim, Sun, Soo-Jin Kim, and Byoung-Tak Zhang. "Evolving hypernetwork classifiers for microRNA expression profile analysis." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424487.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Jin, Huawei, Zhenhua Yu, Xiaodan Wang, Weian Chen, Shaolei Guo, Mohan Vamsi Kasukurthi, Glen M. Borchert, and Jingshan Huang. "Computational analysis to discover microRNA biomarkers in glioblastoma." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217841.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sungroh Yoon and G. De Micheli. "Prediction and Analysis of Human microRNA Regulatory Modules." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615545.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Pu, Heng-Ying, Da Fu, Mei-Yin Zhang, Yin-Lian Cha, Hai-Shan Peng, Lan-Jun Zhang, Wei-Hua Jia, et al. "Abstract 5223: Prognostic value of microRNA signature in non-small-cell lung cancer: A microRNA expression analysis." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-5223.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Suman, Shikha, Ashutosh Mishra, and Anurag Kulshrestha. "Integrated analysis of microRNA regulation of genes in HSIL." In 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2016. http://dx.doi.org/10.1109/cibcb.2016.7758132.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Meyenhofer, Felix, Olivier Schaad, Patrick Descombes, and Michel Kocher. "Automatic analysis of microRNA Microarray images using Mathematical Morphology." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353780.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zeng, Zhaolei, Jiahua Lu, Zhixiang Zuo, Qi Zhao, and Ruihua Xu. "Abstract 5407: MicroRNA expression analysis of advanced colorectal cancer reveals a microRNA signature with prognostic and predictive value." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-5407.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zeng, Zhaolei, Jiahuan Lu, Zhixiang Zuo, and Ruihua Xu. "IDDF2018-ABS-0122 MICRORNA expression analysis of advanced colorectal cancer reveals a microrna signature with prognostic and predictive value." In International Digestive Disease Forum (IDDF) 2018, Hong Kong, 9–10 June 2018. BMJ Publishing Group Ltd and British Society of Gastroenterology, 2018. http://dx.doi.org/10.1136/gutjnl-2018-iddfabstracts.14.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "MicroRNA analysis"

1

Liu, Wenfeng, and Keshu Hu. Prognostic significance of microRNA-221 in liver cancer: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ji, Kun, Xiaohua Wang, Anqi Zhang, and Hongwei Wen. Prognostic value of microRNA-21 in epithelial ovarian carcinoma: a systematic review and meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0064.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Qiao, Feng. Association between microRNA 21 expression in serum and lung cancer: a protocol of systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review Protocols, April 2020. http://dx.doi.org/10.37766/inplasy2020.4.0055.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Qiao, Feng. Association between microRNA 25 expression in serum and lung cancer: a protocol of systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review Protocols, April 2020. http://dx.doi.org/10.37766/inplasy2020.4.0056.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Song, Pingping Yu, Zhen Meng, and Lin Feng. The value of microRNA-203 as a biomarker for the prognosis of esophageal cancer: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0022.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Johnson, Curtis E., Stephen Fallis, Thomas J. Groshens, Kelvin T. Higa, and Ismail M. Ismail. Characterization of Nanometer- to Micron-Sized Aluminum Powders by Thermogravimetric Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada409796.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Green, Jeffrey E., and Kristin K. Deeb. Cross Species Identification and Functional Analysis of MicroRNAs in Mammary Tumorigenesis: Potential Targets for Detection, Diagnosis and Therapy. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada473885.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Foscolos, A. E. Mass Transfer of Elements in Middle Triassic Shale / Sandstone Sequences, Sverdrup Basin, Arctic Islands, Part 2: Mineralogy, Clay Mineralogy, Thermogravimetric Analysis and Chemistry of the Greater Than .2 Micron Fraction and Sem Studies On Thin Sections, East Drake L-06 and Sky Battle Bay M-11 Cores. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1989. http://dx.doi.org/10.4095/130812.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'MicroRNA analysis' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography