Document Detail


Ensemble methods for classification of patients for personalized medicine with high-dimensional data.
MedLine Citation:
PMID:  17719213     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: Personalized medicine is defined by the use of genomic signatures of patients in a target population for assignment of more effective therapies as well as better diagnosis and earlier interventions that might prevent or delay disease. An objective is to find a novel classification algorithm that can be used for prediction of response to therapy in order to help individualize clinical assignment of treatment. METHODS AND MATERIALS: Classification algorithms are required to be highly accurate for optimal treatment on each patient. Typically, there are numerous genomic and clinical variables over a relatively small number of patients, which presents challenges for most traditional classification algorithms to avoid over-fitting the data. We developed a robust classification algorithm for high-dimensional data based on ensembles of classifiers built from the optimal number of random partitions of the feature space. The software is available on request from the authors. RESULTS: The proposed algorithm is applied to genomic data sets on lymphoma patients and lung cancer patients to distinguish disease subtypes for optimal treatment and to genomic data on breast cancer patients to identify patients most likely to benefit from adjuvant chemotherapy after surgery. The performance of the proposed algorithm is consistently ranked highly compared to the other classification algorithms. CONCLUSION: The statistical classification method for individualized treatment of diseases developed in this study is expected to play a critical role in developing safer and more effective therapies that replace one-size-fits-all drugs with treatments that focus on specific patient needs.
Authors:
Hojin Moon; Hongshik Ahn; Ralph L Kodell; Songjoon Baek; Chien-Ju Lin; James J Chen
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2007-08-23
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  41     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:  2007 Nov 
Date Detail:
Created Date:  2007-11-05     Completed Date:  2008-02-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  197-207     Citation Subset:  IM    
Affiliation:
Department of Mathematics and Statistics, California State University-Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, USA. hmoon623@yahoo.com
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MeSH Terms
Descriptor/Qualifier:
Adenocarcinoma / diagnosis,  genetics,  therapy
Algorithms*
Breast Neoplasms / diagnosis,  genetics,  therapy
Chemotherapy, Adjuvant
Diagnosis, Computer-Assisted*
Female
Gene Expression Regulation, Neoplastic*
Humans
Lung Neoplasms / diagnosis,  genetics,  therapy
Lymphoma, Large B-Cell, Diffuse / diagnosis,  genetics,  therapy
Male
Mesothelioma / diagnosis,  genetics,  therapy
Models, Statistical
Neoplasms / diagnosis*,  drug therapy,  genetics,  surgery,  therapy*
Patient Selection*
Pleural Neoplasms / diagnosis,  genetics,  therapy
Reproducibility of Results
Software
Treatment Outcome

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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