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MicroRNA Protocols

Overview of attention for book
Cover of 'MicroRNA Protocols'

Table of Contents

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    Book Overview
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    Chapter 1 The MicroRNA
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    Chapter 2 Target mRNA-Driven Biogenesis of Cognate MicroRNAs In Vitro
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    Chapter 3 Isolation of Viral-Infected Brain Regions for miRNA Profiling from Formalin-Fixed Paraffin-Embedded Tissues by Laser Capture Microdissection
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    Chapter 4 Isolation and Analysis of Exosomal MicroRNAs from Ovarian Follicular Fluid
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    Chapter 5 Profiling of MicroRNAs in the Biofluids of Livestock Species
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    Chapter 6 Exosomal MicroRNAs as Potential Biomarkers in Neuropsychiatric Disorders
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    Chapter 7 Identification and Validation of Potential Differential miRNA Regulation via Alternative Polyadenylation
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    Chapter 8 How to Explore the Function and Importance of MicroRNAs: MicroRNAs Expression Profile and Their Target/Pathway Prediction in Bovine Ovarian Cells
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    Chapter 9 Gene Silencing In Vitro and In Vivo Using Intronic MicroRNAs
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    Chapter 10 Mining Exosomal MicroRNAs from Human-Induced Pluripotent Stem Cells-Derived Cardiomyocytes for Cardiac Regeneration
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    Chapter 11 Quantitative Analysis of Precursors MicroRNAs and Their Respective Mature MicroRNAs in Cancer Exosomes Overtime
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    Chapter 12 Quantum Language of MicroRNA: Application for New Cancer Therapeutic Targets
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    Chapter 13 In Vitro Methods for Analyzing miRNA Roles in Cancer Cell Proliferation, Invasion, and Metastasis
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    Chapter 14 Isolation and Identification of Gene-Specific MicroRNAs
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    Chapter 15 Comprehensive Measurement of Gene Silencing Involving Endogenous MicroRNAs in Mammalian Cells
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    Chapter 16 Screening miRNA for Functional Significance by 3D Cell Culture System
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    Chapter 17 Neonatal Rat Cardiomyocytes Isolation, Culture, and Determination of MicroRNAs’ Effects in Proliferation
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    Chapter 18 Gene Manipulation with Micro RNAs at Single-Human Cancer Cell
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    Chapter 19 Laser Capture Microdissection of Epithelium from a Wound Healing Model for MicroRNA Analysis
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    Chapter 20 Transgene-Like Animal Models Using Intronic MicroRNAs
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    Chapter 21 Application of TALE-Based Approach for Dissecting Functional MicroRNA-302/367 in Cellular Reprogramming
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    Chapter 22 Mechanism and Method for Generating Tumor-Free iPS Cells Using Intronic MicroRNA miR-302 Induction
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    Chapter 23 The miR-302-Mediated Induction of Pluripotent Stem Cells (iPSC): Multiple Synergistic Reprogramming Mechanisms
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    Chapter 24 Identification and Isolation of Novel Sugar-Like RNA Protecting Materials: Glycylglycerins from Pluripotent Stem Cells
Attention for Chapter 12: Quantum Language of MicroRNA: Application for New Cancer Therapeutic Targets
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Chapter title
Quantum Language of MicroRNA: Application for New Cancer Therapeutic Targets
Chapter number 12
Book title
MicroRNA Protocols
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7601-0_12
Pubmed ID
Book ISBNs
978-1-4939-7600-3, 978-1-4939-7601-0
Authors

Yoichi Robertus Fujii, Fujii, Yoichi Robertus

Abstract

MicroRNA (miRNA) is the noncoding gene: therefore, the miRNA gene inheritably controls protein gene expression through transcriptional and post-transcriptional levels. Aberrant expression of miRNA genes causes various human diseases, especially cancers. Although cancer is a complex disease, cancer/miRNA implication has yet been grasped from the perspective of miRNA profile in bed side. Since miRNA is the mobile genetic element, the clinical verification of miRNA in microvesicle of blood is too much straggle to predict potential cancer/miRNA associations without bioinformatical computing. Further, experimental investigation of miRNA/cancer pathways is expensive and time-consuming. While the accumulated data (big data) of miRNA profiles has been on line as the databases in cancers, using the database algorithms for miRNA target prediction have reduced required time for conventional experiments and have cut the cost. Computational prediction of miRNA/target mRNA has shown numerous significant outcomes that are unobtainable only by experimental approaches. However, ID of miRNA in the annotation is an arbitrary number and the ID is not related with miRNA its functions. Therefore, it has not been physicochemically shown why multiple miRNAs in blood or tissues are useful for diagnosis and porgnosis of human diseases or why function of single miRNA in cancer is rendered to oncomir or tumopr suppressor. In addition, it is less cleared why environmental factors, such as temperature, radiation, therapeutic anti-cancer immune or chemical agents can alter the expression of miRNAs in the cell. The ceRNA theory would not be enough for the investigation of such subjects. Given miRNA/target prediction tools, to elucidate such issues with computer simulation we have previously introduced the quantum miRNA/miRNA interaction as a new scoring using big database. The quantum score was implicated in miRNA synergisms in cancer and participated in the miRNA/target interaction on human diseases. On the other hand, ribosomal RNA (rRNA) is the dominant RNA species of the cells. It is well known that ribosomopathies, such as Diamond-Blackfan anemia, dyskeratiosis congenital, Shwachman-Diamond syndrome, 5q-myelodysplastic syndrome, Treacher Collins syndrome, cartilage-hair hypoplasia, North American Indian childhood cirrhosis, isolated congenital asplenia, Bowen-Conradi syndrome and cancer are caused by altered expression of ribosomal proteins or rRNA genes. We have proposed the hypothesis that the interaction among miRNAs from rRNA and/or other cellular miRNAs would be involved into cancer as the ribosomopathy. Subsequently, we found rRNA-derived miRNAs (rmiRNAs) by using the sequence homology search (miPS) with miRNA database (miRBase). Further, the pathway related with cancer between rmiRNA/target protein gene was predicted by miRNA entangling target sorting (METS) algorithm. In this chapter, we describe about the usage of in silico miRNA identification program, miRNA/target prediction search through the database and quantum language of miRNA by the METS, and the ontology analysis. In particular, the METS algorithm according to the quantum value would be useful simulator to discover a new therapeutic target aganist cancer. It may also partly contribute to the elucidation of complex mechanisms and development of agents of anti-cancer.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 21%
Student > Master 4 14%
Researcher 4 14%
Unspecified 2 7%
Student > Ph. D. Student 2 7%
Other 4 14%
Unknown 7 24%
Readers by discipline Count As %
Medicine and Dentistry 8 28%
Biochemistry, Genetics and Molecular Biology 3 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Unspecified 2 7%
Psychology 2 7%
Other 4 14%
Unknown 8 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 September 2019.
All research outputs
#16,722,520
of 25,386,051 outputs
Outputs from Methods in molecular biology
#5,310
of 14,201 outputs
Outputs of similar age
#271,588
of 456,336 outputs
Outputs of similar age from Methods in molecular biology
#509
of 1,487 outputs
Altmetric has tracked 25,386,051 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,201 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 456,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,487 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.