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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.1
- 수록면
- 50 - 57 (8page)
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In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.
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참고문헌 (26)
참고문헌 신청
[학술지(정
Alizadeh AA / 2000 / Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling / Nature 403 : 503 ~ 511
Chen SH / 2008 / A support vector machine approach for detecting gene-gene interaction / Genet Epidemiol 32 : 152 ~ 167
Friedman N / 2000 / Using Bayesian networks to analyze expression data / J Comput Biol 7 : 601 ~ 620
Getz G / 2000 / Coupled two-way clustering analysis of gene microarray data / Proc Natl Acad Sci 97 : 12079 ~ 12084
Alizadeh AA / 2000 / Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling / Nature 403 : 503 ~ 511
Chen SH / 2008 / A support vector machine approach for detecting gene-gene interaction / Genet Epidemiol 32 : 152 ~ 167
Friedman N / 2000 / Using Bayesian networks to analyze expression data / J Comput Biol 7 : 601 ~ 620
Getz G / 2000 / Coupled two-way clustering analysis of gene microarray data / Proc Natl Acad Sci 97 : 12079 ~ 12084
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