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Data Science for Asthma Study

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Genomic Approach to Asthma

Part of the book series: Translational Bioinformatics ((TRBIO,volume 12))

Abstract

To obtain information from quantitative data, we need to develop various analysis methods, which can be drawn from diverse fields, such as computer science, information theory and statistics. This chapter discusses methods for analysing datasets generated in asthma study for personalized medicine. Personalized medicine is the future of medicine, aiming at providing tailor-made medical decisions, practices and products to individual patients. Medical decisions and treatments are being tailored to individual patient based on the context of patient’s various profiles such as Genomics, Proteomics, Lipidomics and Metabolomics content. High throughput instruments are used to generate large scale datasets. To succeed in personalized medicine, analysis methods, including those dedicated to specific data types and those shared among various data, should be well developed. In this chapter, we first discuss the need of using data from molecular level to pathway level. Then we introduce analysis methods in typical analysis steps, which are batch effect detection and removal, statistical analysis, feature selection and classification, and unsupervised way of pattern recognition.

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Abbreviations

ANOVA:

Analysis of variance

CS:

Corticosteroids

CV:

Cross-validation

DNA:

Deoxyribonucleic acid

DRAMI:

Drift, Retention time, Accurate Mass, Intensity

DWD:

Distance weighted discrimination

GR:

Glucocorticoid receptor

GWAS:

Genome-wide association study

LASSO:

least absolute shrinkage and selection operator

LC-IMS/MSE :

Ion mobility supported lipid chromatography and mass spectrometry instrument

LOOCV:

Leave-one-out cross-validation

LPS:

Lipopolysaccharide

MAPK:

Mitogen Activated Protein Kinase

MKP-1:

MAPK phosphatase-1

MS:

Mass spectrometry

PCA:

Principle component analysis

PLGS:

ProteinLynx Global Server

ROC:

Receiver operating characteristic

SNP:

Single-nucleotide polymorphisms

SVA:

Surrogate variable analysis

SVD:

Singular value decomposition

SVM:

Support vector machine

TAK1:

GFβ kinase-1

TDA:

Topological data analysis

UBIOPRED:

Unbiased BIOmarkers in PREDiction of respiratory disease outcomes

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Authors and Affiliations

  1. Data Science Institute, Imperial College London, London, UK

    Xian Yang & Yike Guo

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  1. Xian Yang
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  1. Zhongshan Hospital Institute of Clinical Science, Shanghai Medical School Fudan University, Shanghai, China

    Xiangdong Wang

  2. Shanghai Respiratory Research Institute, Respiratory Division of Zhongshan Hospital, Fudan University, Shanghai, China

    Zhihong Chen

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Yang, X., Guo, Y. (2018). Data Science for Asthma Study. In: Wang, X., Chen, Z. (eds) Genomic Approach to Asthma. Translational Bioinformatics, vol 12. Springer, Singapore. https://doi.org/10.1007/978-981-10-8764-6_13

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