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Bioinformatics in MicroRNA Research

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Cover of 'Bioinformatics in MicroRNA Research'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 MicroRNAs, Long Noncoding RNAs, and Their Functions in Human Disease
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    Chapter 2 MicroRNA Expression: Protein Participants in MicroRNA Regulation
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    Chapter 3 Viral MicroRNAs, Host MicroRNAs Regulating Viruses, and Bacterial MicroRNA-Like RNAs
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    Chapter 4 MicroRNAs: Biomarkers, Diagnostics, and Therapeutics
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    Chapter 5 Relational Databases and Biomedical Big Data
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    Chapter 6 Semantic Technologies and Bio-Ontologies
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    Chapter 7 Genome-Wide Analysis of MicroRNA-Regulated Transcripts
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    Chapter 8 Computational Prediction of MicroRNA Target Genes, Target Prediction Databases, and Web Resources
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    Chapter 9 Exploring MicroRNA::Target Regulatory Interactions by Computing Technologies
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    Chapter 10 The Limitations of Existing Approaches in Improving MicroRNA Target Prediction Accuracy.
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    Chapter 11 Genomic Regulation of MicroRNA Expression in Disease Development
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    Chapter 12 Next-Generation Sequencing for MicroRNA Expression Profile
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    Chapter 13 Handling High-Dimension (High-Feature) MicroRNA Data.
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    Chapter 14 Effective Removal of Noisy Data Via Batch Effect Processing
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    Chapter 15 Logical Reasoning (Inferencing) on MicroRNA Data
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    Chapter 16 Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions
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    Chapter 17 Involvement of MicroRNAs in Diabetes and Its Complications
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    Chapter 18 MicroRNA Regulatory Networks as Biomarkers in Obesity: The Emerging Role.
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    Chapter 19 Expression of MicroRNAs in Thyroid Carcinoma.
Attention for Chapter 13: Handling High-Dimension (High-Feature) MicroRNA Data.
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Chapter title
Handling High-Dimension (High-Feature) MicroRNA Data.
Chapter number 13
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7046-9_13
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

Yue Hu, Wenjun Lan, Daniel Miller

Editors

Jingshan Huang, Glen M. Borchert, Dejing Dou, Jun (Luke) Huan, Wenjun Lan, Ming Tan, Bin Wu

Abstract

High-dimensional data, or high-feature variables, are often used to describe the characteristics of microRNA sequence and microarray data. As a consequence, the curse of high dimension often becomes a problem. High-dimension variables lead to many difficulties in processing and can be hard to understand. On the other aspect, as the sample size rather limited, the more variables, the more statistical error would be produced in the data processing. For the purpose of decreasing the dimension of variables, a degenerated k-mer method was suggested. To enhance the statistical robustness, the gapped k-mer method was introduced. In the last part of this chapter, some traditional supervised and unsupervised mathematical methods that used to decrease the dimensionality of the data are also described.

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Chemical Engineering 1 33%
Business, Management and Accounting 1 33%
Unknown 1 33%