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Translational Biomedical Informatics

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Cover of 'Translational Biomedical Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 NGS for Sequence Variants.
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    Chapter 2 RNA Bioinformatics for Precision Medicine.
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    Chapter 3 Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.
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    Chapter 4 Computational Methods in Mass Spectrometry-Based Proteomics.
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    Chapter 5 Informatics for Metabolomics.
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    Chapter 6 Metagenomics and Single-Cell Omics Data Analysis for Human Microbiome Research.
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    Chapter 7 Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.
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    Chapter 8 Medical Imaging Informatics.
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    Chapter 9 LIMS and Clinical Data Management.
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    Chapter 10 Biobanks and Their Clinical Application and Informatics Challenges.
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    Chapter 11 XML, Ontologies, and Their Clinical Applications.
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    Chapter 12 Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.
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    Chapter 13 Network-Based Biomedical Data Analysis.
Attention for Chapter 9: LIMS and Clinical Data Management.
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Citations

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Chapter title
LIMS and Clinical Data Management.
Chapter number 9
Book title
Translational Biomedical Informatics
Published in
Advances in experimental medicine and biology, November 2016
DOI 10.1007/978-981-10-1503-8_9
Pubmed ID
27807749
Book ISBNs
978-9-81-101502-1, 978-9-81-101503-8
Authors

Yalan Chen, Yuxin Lin, Xuye Yuan, Bairong Shen

Editors

Bairong Shen, Haixu Tang, Xiaoqian Jiang

Abstract

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Master 6 16%
Student > Ph. D. Student 5 13%
Researcher 3 8%
Professor 2 5%
Other 6 16%
Unknown 9 24%
Readers by discipline Count As %
Computer Science 7 18%
Medicine and Dentistry 6 16%
Biochemistry, Genetics and Molecular Biology 6 16%
Engineering 3 8%
Immunology and Microbiology 2 5%
Other 5 13%
Unknown 9 24%

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