Author
Masato Kawakami
Bio: Masato Kawakami is an academic researcher from Keio University. The author has an hindex of 1, co-authored 1 publications receiving 256 citations.
Papers
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TL;DR: A state-of-the-art overview of the data processing tools available is provided, with their advantages and disadvantages, and comparisons are made to guide the reader.
Abstract: Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.
285 citations
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TL;DR: This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA.
606 citations
TL;DR: Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data.
402 citations
University of Geneva1, University of Helsinki2, Katholieke Universiteit Leuven3, University of Washington4, University of Wisconsin-Madison5, Heidelberg University6, German Cancer Research Center7, University of Nottingham8, VU University Amsterdam9, University of California, Irvine10, University of California, San Francisco11, French Institute of Health and Medical Research12, Harvard University13, Maine Medical Center14, University of Turin15, National Institutes of Health16, University of Texas Southwestern Medical Center17, University of Lausanne18, University of Missouri19, École Polytechnique Fédérale de Lausanne20, Uppsala University21, University of Arkansas for Medical Sciences22, University of Bern23, University of Virginia24, Imperial College London25, University of California, San Diego26, University College London27, Flanders Institute for Biotechnology28, University of Oxford29, University of California, Los Angeles30, University of Texas MD Anderson Cancer Center31, University of Toronto32, University of Illinois at Chicago33, University of Amsterdam34, George Washington University35, University of Liège36, University of Eastern Finland37, Ludwig Institute for Cancer Research38, Indiana University39, University of Edinburgh40, Maastricht University41, Loyola University Medical Center42, National Scientific and Technical Research Council43, University of Bari44, University of Fribourg45, Ohio State University46, University of Melbourne47, University of Antwerp48, University of Münster49
TL;DR: In vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis are described and critical aspects that are relevant for their execution and proper interpretation are highlighted.
Abstract: The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference.
397 citations
287 citations
TL;DR: The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization as discussed by the authors.
Abstract: Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.
217 citations