Computational and Mathematical Methods in Medicine

Statistical Analysis of Biomarkers for Personalized Medicine


Publishing date
31 May 2013
Status
Published
Submission deadline
11 Jan 2013

Lead Editor

1Department of Mathematical Analysis and Statistical Inference, Institute Statistical Mathematics, Tokyo, Japan

2Department of Data Science, Institute Statistical Mathematics, Tokyo, Japan

3Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

4Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan


Statistical Analysis of Biomarkers for Personalized Medicine

Description

Recently, enhanced and advanced biomedical technology, such as high-throughput microarrays and molecular imaging to monitor SNPs and gene and protein expressions, has been available to provide exhaustive medical information on a single person. In principle, we could obtain much more information about the biological and medical status of an individual from such data sets, which are viewed as biomarkers in a wide sense, for use in identification, association, and prediction studies regarding phenotypes such as disease subtypes, prognosis, treatment responsiveness, and adverse reactions as part of personalized medicine. However, it is frequently difficult to extract only the informative part in such data sets that include a garbage heap of uninformative observations. In particular, we cannot confirm statistical evidence because of the small sample size relative to the dimension of the data when attempting the practical use of such statistical applications. For example, if we implement machine learning methods for the prediction of treatment effects, then we are typically faced with difficulties in trying to confirm reproducibility and robustness as performance measures. At the same time, an effective study design is crucial for developing and validating biomarkers. We focus in this special issue on the difficulties of the statistical analysis of biomarkers, including statistical design, estimation, and prediction. Potential topics include, but are not limited to:

  • Kernel machine learning methods for predicting phenotypes
  • Matrix and tensor data learning
  • Sparse learning and boosting
  • Multiple testing/ranking and selection in biomarker screening
  • Hierarchical and/or mixture modeling of interindividual heterogeneity
  • Design of clinical studies with biomarkers

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/cmmm/bpm/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2013
  • - Article ID 467420
  • - Editorial

Statistical Analysis of Biomarkers for Personalized Medicine

Shinto Eguchi | Shigeyuki Matsui | ... | Chuhsing Kate Hsiao
  • Special Issue
  • - Volume 2013
  • - Article ID 179761
  • - Research Article

The Number of Candidate Variants in Exome Sequencing for Mendelian Disease under No Genetic Heterogeneity

Jo Nishino | Shuhei Mano
  • Special Issue
  • - Volume 2013
  • - Article ID 591032
  • - Research Article

Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data

Hisako Yoshida | Atsushi Kawaguchi | Kazuhiko Tsuruya
  • Special Issue
  • - Volume 2013
  • - Article ID 235825
  • - Research Article

Power Analysis of C-TDT for Small Sample Size Genome-Wide Association Studies by the Joint Use of Case-Parent Trios and Pairs

Farid Rajabli | Gul Inan | Ozlem Ilk
  • Special Issue
  • - Volume 2013
  • - Article ID 865980
  • - Review Article

Genomic Biomarkers for Personalized Medicine: Development and Validation in Clinical Studies

Shigeyuki Matsui
  • Special Issue
  • - Volume 2013
  • - Article ID 798189
  • - Research Article

Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

Osamu Komori | Mari Pritchard | Shinto Eguchi
  • Special Issue
  • - Volume 2013
  • - Article ID 568480
  • - Research Article

An Empirical Bayes Optimal Discovery Procedure Based on Semiparametric Hierarchical Mixture Models

Hisashi Noma | Shigeyuki Matsui
  • Special Issue
  • - Volume 2013
  • - Article ID 693901
  • - Research Article

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

Keita Mori | Tomonori Oura | ... | Shigeyuki Matsui
  • Special Issue
  • - Volume 2013
  • - Article ID 860673
  • - Research Article

A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies

Jia-Rou Liu | Po-Hsiu Kuo | Hung Hung
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate38%
Submission to final decision61 days
Acceptance to publication39 days
CiteScore1.840
Impact Factor1.563
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