How to cite the R package msma

Kawaguchi, A., & Yamashita, F. (2017). Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics (Oxford, England), 18(4), 651–665.

1.

Kawaguchi A, Yamashita F. Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics. 2017 Oct 1;18(4):651–65.

@ARTICLE{Kawaguchi2017-aj,
  title     = "Supervised multiblock sparse multivariable analysis with
               application to multimodal brain imaging genetics",
  author    = "Kawaguchi, Atsushi and Yamashita, Fumio",
  abstract  = "SUMMARYThis article proposes a procedure for describing the
               relationship between high-dimensional data sets, such as
               multimodal brain images and genetic data. We propose a
               supervised technique to incorporate the clinical outcome to
               determine a score, which is a linear combination of variables
               with hieratical structures to multimodalities. This approach is
               expected to obtain interpretable and predictive scores. The
               proposed method was applied to a study of Alzheimer's disease
               (AD). We propose a diagnostic method for AD that involves using
               whole-brain magnetic resonance imaging (MRI) and positron
               emission tomography (PET), and we select effective brain regions
               for the diagnostic probability and investigate the genome-wide
               association with the regions using single nucleotide
               polymorphisms (SNPs). The two-step dimension reduction method,
               which we previously introduced, was considered applicable to
               such a study and allows us to partially incorporate the proposed
               method. We show that the proposed method offers classification
               functions with feasibility and reasonable prediction accuracy
               based on the receiver operating characteristic (ROC) analysis
               and reasonable regions of the brain and genomes. Our simulation
               study based on the synthetic structured data set showed that the
               proposed method outperformed the original method and provided
               the characteristic for the supervised feature.",
  journal   = "Biostatistics",
  publisher = "Oxford University Press (OUP)",
  volume    =  18,
  number    =  4,
  pages     = "651--665",
  month     =  oct,
  year      =  2017,
  url       = "http://dx.doi.org/10.1093/biostatistics/kxx011",
  language  = "en",
  issn      = "1465-4644, 1468-4357",
  doi       = "10.1093/biostatistics/kxx011"
}

TY  - JOUR
AU  - Kawaguchi, Atsushi
AU  - Yamashita, Fumio
AD  - Center for Comprehensive Community Medicine, Faculty of Medicine, Saga
      University, 5-1-1 Nabeshima, Saga 849-8501, Japan akawa@cc.saga-u.ac.jp;
      Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate
      Medical University, Yahaba, Iwate 028-3694, Japan for the Alzheimer’s
      Disease Neuroimaging Initiative
TI  - Supervised multiblock sparse multivariable analysis with application to
      multimodal brain imaging genetics
T2  - Biostatistics
VL  - 18
IS  - 4
SP  - 651-665
PY  - 2017
DA  - 2017/10/1
PB  - Oxford University Press (OUP)
AB  - SUMMARYThis article proposes a procedure for describing the relationship
      between high-dimensional data sets, such as multimodal brain images and
      genetic data. We propose a supervised technique to incorporate the
      clinical outcome to determine a score, which is a linear combination of
      variables with hieratical structures to multimodalities. This approach is
      expected to obtain interpretable and predictive scores. The proposed
      method was applied to a study of Alzheimer’s disease (AD). We propose a
      diagnostic method for AD that involves using whole-brain magnetic
      resonance imaging (MRI) and positron emission tomography (PET), and we
      select effective brain regions for the diagnostic probability and
      investigate the genome-wide association with the regions using single
      nucleotide polymorphisms (SNPs). The two-step dimension reduction method,
      which we previously introduced, was considered applicable to such a study
      and allows us to partially incorporate the proposed method. We show that
      the proposed method offers classification functions with feasibility and
      reasonable prediction accuracy based on the receiver operating
      characteristic (ROC) analysis and reasonable regions of the brain and
      genomes. Our simulation study based on the synthetic structured data set
      showed that the proposed method outperformed the original method and
      provided the characteristic for the supervised feature.
SN  - 1465-4644
DO  - 10.1093/biostatistics/kxx011
UR  - http://dx.doi.org/10.1093/biostatistics/kxx011
ER  -