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Vol. 19, Suppl. 1, 2012
Issue release date: Januar 2012
Forsch Komplementmed 2012;19(suppl 1):42–48
(DOI:10.1159/000335190)

Analyzing Heterogeneous Complexity in Complementary and Alternative Medicine Research: A Systems Biology Solution via Parsimony Phylogenetics

Abu-Asab M.a · Koithan M.b,c · Shaver J.b · Amri H.d
aLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, bCollege of Nursing, cDepartment of Family and Community Medicine, The University of Arizona, Tucson, AZ, dDepartment of Biochemistry, Cellular and Molecular Biology, School of Medicine, Georgetown University, Washington, DC, USA
email Corresponding Author

Abstract

Systems biology offers cutting-edge tools for the study of complementary and alternative medicine (CAM). The advent of ‘omics’ techniques and the resulting avalanche of scientific data have introduced an unprecedented level of complexity and heterogeneous data to biomedical research, leading to the development of novel research approaches. Statistical averaging has its limitations and is unsuitable for the analysis of heterogeneity, as it masks diversity by homogenizing otherwise heterogeneous populations. Unfortunately, most researchers are unaware of alternative methods of analysis capable of accounting for individual variability. This paper describes a systems biology solution to data complexity through the application of parsimony phylogenetic analysis. Maximum parsimony (MP) provides a data-based modeling paradigm that will permit a priori stratification of the study cohort(s), better assessment of early diagnosis, prognosis, and treatment efficacy within each stratum, and a method that could be used to explore, identify and describe complex human patterning.


 Outline


 goto top of outline Keywords

  • Heterogeneity
  • Parsimony
  • Phylogenetics
  • Synapomorphies
  • Systems biology
  • Clinical trial design
  • Complementary and alternative medicine

 goto top of outline Summary

Systems biology offers cutting-edge tools for the study of complementary and alternative medicine (CAM). The advent of ‘omics’ techniques and the resulting avalanche of scientific data have introduced an unprecedented level of complexity and heterogeneous data to biomedical research, leading to the development of novel research approaches. Statistical averaging has its limitations and is unsuitable for the analysis of heterogeneity, as it masks diversity by homogenizing otherwise heterogeneous populations. Unfortunately, most researchers are unaware of alternative methods of analysis capable of accounting for individual variability. This paper describes a systems biology solution to data complexity through the application of parsimony phylogenetic analysis. Maximum parsimony (MP) provides a data-based modeling paradigm that will permit a priori stratification of the study cohort(s), better assessment of early diagnosis, prognosis, and treatment efficacy within each stratum, and a method that could be used to explore, identify and describe complex human patterning.

Copyright © 2012 S. Karger AG, Basel


 goto top of outline Author Contacts

Dr. Hakima Amri, Department of Biochemistry, Cellular and Molecular Biology, School of Medicine, Georgetown University, Washington, DC 20007, USA, amrih@georgetown.edu


 goto top of outline Article Information

Published online: January 20, 2012
Number of Print Pages : 7


 goto top of outline Publication Details

Forschende Komplementärmedizin / Research in Complementary Medicine (Research Practice Perspectives - Wissenschaft Praxis Perspektiven)

Vol. 19, No. Suppl. 1, Year 2012 (Cover Date: Januar 2012)

Journal Editor: Walach H. (Frankfurt/O.)
ISSN: 1661-4119 (Print), eISSN: 1661-4127 (Online)

For additional information: http://www.karger.com/FOK


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Systems biology offers cutting-edge tools for the study of complementary and alternative medicine (CAM). The advent of ‘omics’ techniques and the resulting avalanche of scientific data have introduced an unprecedented level of complexity and heterogeneous data to biomedical research, leading to the development of novel research approaches. Statistical averaging has its limitations and is unsuitable for the analysis of heterogeneity, as it masks diversity by homogenizing otherwise heterogeneous populations. Unfortunately, most researchers are unaware of alternative methods of analysis capable of accounting for individual variability. This paper describes a systems biology solution to data complexity through the application of parsimony phylogenetic analysis. Maximum parsimony (MP) provides a data-based modeling paradigm that will permit a priori stratification of the study cohort(s), better assessment of early diagnosis, prognosis, and treatment efficacy within each stratum, and a method that could be used to explore, identify and describe complex human patterning.



 goto top of outline Author Contacts

Dr. Hakima Amri, Department of Biochemistry, Cellular and Molecular Biology, School of Medicine, Georgetown University, Washington, DC 20007, USA, amrih@georgetown.edu


 goto top of outline Article Information

Published online: January 20, 2012
Number of Print Pages : 7


 goto top of outline Publication Details

Forschende Komplementärmedizin / Research in Complementary Medicine (Research Practice Perspectives - Wissenschaft Praxis Perspektiven)

Vol. 19, No. Suppl. 1, Year 2012 (Cover Date: Januar 2012)

Journal Editor: Walach H. (Frankfurt/O.)
ISSN: 1661-4119 (Print), eISSN: 1661-4127 (Online)

For additional information: http://www.karger.com/FOK


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.