Bioinformatics tools for cancer metabolomics

Grigoriy Blekherman; Reinhard Laubenbacher; Diego F. Cortes; Pedro Mendes; Frank M. Torti; Steven Akman; Suzy V. Torti; Vladimir Shulaev

doi:10.1007/s11306-010-0270-3

Bioinformatics tools for cancer metabolomics

Grigoriy Blekherman
, Reinhard Laubenbacher
, Diego F. Cortes
, Pedro Mendes
, Frank M. Torti
, Steven Akman
, Suzy V. Torti
, Vladimir Shulaev

Research output: Contribution to journal › Article › peer-review

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Abstract

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages. © 2011 The Author(s).

Original language	English
Pages (from-to)	329-343
Number of pages	14
Journal	Metabolomics
Volume	7
Issue number	3
DOIs	https://doi.org/10.1007/s11306-010-0270-3
Publication status	Published - Sept 2011

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Cancer
Mass spectrometry
Metabolite profiling
Metabolomics
NMR

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10.1007/s11306-010-0270-3

POST-PEER-REVIEW-PUBLISHERS.PDFFinal published version, 253 KB

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title = "Bioinformatics tools for cancer metabolomics",

abstract = "It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages. {\textcopyright} 2011 The Author(s).",

keywords = "Bioinformatics, Cancer, Mass spectrometry, Metabolite profiling, Metabolomics, NMR",

author = "Grigoriy Blekherman and Reinhard Laubenbacher and Cortes, \{Diego F.\} and Pedro Mendes and Torti, \{Frank M.\} and Steven Akman and Torti, \{Suzy V.\} and Vladimir Shulaev",

note = "The research in the authors{\textquoteright} lab is financially supported by the NIH (National Institutes of Health) NCI (National Cancer Institute) grant R01CA120170. FMT and SVT were supported in part by NIH NIDDK (National Institutes of Diabetes and Digestive and Kidney Diseases) grant R37 DK42412.",

year = "2011",

month = sep,

doi = "10.1007/s11306-010-0270-3",

language = "English",

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T1 - Bioinformatics tools for cancer metabolomics

AU - Blekherman, Grigoriy

AU - Laubenbacher, Reinhard

AU - Cortes, Diego F.

AU - Mendes, Pedro

AU - Torti, Frank M.

AU - Akman, Steven

AU - Torti, Suzy V.

AU - Shulaev, Vladimir

N1 - The research in the authors’ lab is financially supported by the NIH (National Institutes of Health) NCI (National Cancer Institute) grant R01CA120170. FMT and SVT were supported in part by NIH NIDDK (National Institutes of Diabetes and Digestive and Kidney Diseases) grant R37 DK42412.

PY - 2011/9

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N2 - It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages. © 2011 The Author(s).

AB - It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages. © 2011 The Author(s).

KW - Bioinformatics

KW - Cancer

KW - Mass spectrometry

KW - Metabolite profiling

KW - Metabolomics

KW - NMR

U2 - 10.1007/s11306-010-0270-3

DO - 10.1007/s11306-010-0270-3

M3 - Article

SN - 1573-3882

VL - 7

SP - 329

EP - 343

JO - Metabolomics

JF - Metabolomics

IS - 3

ER -

Bioinformatics tools for cancer metabolomics

Abstract

UN SDGs

Keywords

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