Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction

Ko, Yi An; Mukherjee, Bhramar; Smith, Jennifer A.; Kardia, Sharon L.R.; Allison, Matthew A.; Roux, Ana V. Diez

doi:10.1097/ede.0000000000000548

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…the researches of article [9] explored the application of classification and clustering approaches for pattern recognition and failure forecasting on mining shovels. The article [10] illustrated the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model. The article [11] present Monte Carlo simulations that evaluate size and power of the proposed tests under different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

2019

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Botnet detection, vulnerability mining and confrontation bring many challenges to network security, and become the main dangerous source threatening economic development and causing significant economic losses. To solve these problems, we combine machine learning with classification mining, and propose a botnet vulnerability mining and countermeasure algorithm and its system. Firstly, a botnet model based on machine learning and its construction method are designed according to the characteristics of Botnet such as group network attack, computer group controlled and distributed denial of service attack. Then, in order to achieve the global optimal evaluation and screening of Botnet detection by network vulnerability mining system, a network vulnerability combination classification mining algorithm and machine learning instruction set are designed. Then, based on the analysis of the vulnerability form and the vulnerability diffusion mode of botnet, a vulnerability mining and confrontation algorithm for botnet is proposed. Finally, through simulation experiments, the advantages of the proposed algorithm are proved in vulnerability identification rate, vulnerability clearance performance and botnet connectivity. For example, the average vulnerability detection rate is more than 87%, the vulnerability repair rate is higher than 86%, and the pass rate is still higher than 89%.

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Section: Introductionmentioning

confidence: 99%

Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

2019

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Gene–Environment Interaction: A Variable Selection Perspective

Zhou

Ren

et al. 2021

Methods in Molecular Biology

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Gene-environment interactions have important implications to elucidate the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in majority of published reviews on genetic interaction studies. In this article, we first survey existing overviews on both gene-environment and gene-gene interactions. Then, after a brief introduction on the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms respectively under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies have also been provided.

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Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

Coker¹,

Liverani

Su³

et al. 2018

Curr Envir Health Rpt

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One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

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Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction

Cited by 9 publications

References 34 publications

Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

Gene–Environment Interaction: A Variable Selection Perspective

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

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