소장자료

Computational methods in biomedical research

  • Khattree, Ravindra. , Naik, Dayanand N.
  • Taylor & Francis
  • 2008
Computational methods in biomedical research
  • 자료유형
    단행본
  • 서명/저자사항
    Computational methods in biomedical research / editors, Ravindra Khattree and Dayanand Naik.
  • 발행사항
    Boca Raton : Taylor & Francis, 2008.
  • 개인저자
    Khattree, Ravindra., Naik, Dayanand N.
  • 형태사항
    xvii, 408 p.: ill. ; 25 cm.
  • 총서명
    Biostatistics series ; 24
  • 일반주기
    "A CRC title."
  • 서지주기
    Includes bibliographical references and index.
  • 내용주기
    Microarray Data Analysis - Machine Learning Techniques for Bioinformatics: Fundamentals and Applications - Machine Learning Methods for Cancer Diagnosis and Prognostication - Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges - Predicting US Cancer Mortality Counts Using State Space Models - Analyzing Multiple Failure Time Data Using SAS® Software - Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data - Bayesian Computational Methods in Biomedical Research - Sequential Monitoring of Randomization Tests - Proportional Hazards Mixed-Effects Models and Applications - Classification Rules for Repeated Measures Data from Biomedical Research - Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research
  • 일반주제명
    Medicine - Research - Data processing
    Biology - Research - Data processing
    Medicine - Research - Statistical methods
    Biology - Research - Statistical methods
    Computational biology
    Computational Biology - methods
    Biomedical Research - methods
    Data Interpretation, Statistical
  • ISBN
    9781584885771 (alk. paper)
  • 언어
    영어

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목차

1. Microarray Data Analysis
2. Machine Learning Techniques for Bioinformatics: Fundamentals and Applications
3. Machine Learning Methods for Cancer Diagnosis and Prognostication
4. Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges
5. Predicting US Cancer Mortality Counts Using State Space Models
6. Analyzing Multiple Failure Time Data Using SAS® Software
7. Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data
8. Bayesian Computational Methods in Biomedical Research
9. Sequential Monitoring of Randomization Tests
10. Proportional Hazards Mixed-Effects Models and Applications
11. Classification Rules for Repeated Measures Data from Biomedical Research
12. Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research