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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 14: Effective Removal of Noisy Data Via Batch Effect Processing
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Chapter title
Effective Removal of Noisy Data Via Batch Effect Processing
Chapter number 14
Book title
Bioinformatics in MicroRNA Research
Published in
Methods in molecular biology, May 2017
DOI 10.1007/978-1-4939-7046-9_14
Pubmed ID
Book ISBNs
978-1-4939-7044-5, 978-1-4939-7046-9
Authors

Ryan G. Benton Ph.D., Ryan G. Benton

Editors

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

Abstract

In order to have faith in the analysis of data, a key factor is to have confidence that the data is reliable. In the case of microRNA, reliability includes understanding the collection methods, ensuring that the analysis is appropriate, and ensuring that the data itself is accurate. A key element in ensuring data accuracy is the removal of noise. While there can be several sources of noise, a common source of noise is the batch effect, which can be defined as systematic variability in the data caused by non-biological factors. This chapter will present various techniques designed to remove variability caused by batch effects and the potential effectiveness.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Other 1 25%
Student > Postgraduate 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Chemical Engineering 1 25%
Biochemistry, Genetics and Molecular Biology 1 25%
Physics and Astronomy 1 25%
Unknown 1 25%