Angeline S. Andrew

发表

Incorporating prior expert knowledge in learning Bayesian networks from genetic epidemiological data

Mark E. Borsuk, Chengwei Su, Angeline S. Andrew, 2014, 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology.

Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting

Angeline S. Andrew, Margaret R. Karagas, Meifang Li, 2018, Ann. GIS.

Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2015, BioData Mining.

Using Bayesian networks to discover relations between genes, environment, and disease

Mark E. Borsuk, Chengwei Su, Angeline S. Andrew, 2013, BioData Mining.

Detecting gene-gene interactions using a permutation-based random forest method

Jing Li, Jason H. Moore, James D. Malley, 2016, BioData Mining.

Bladder cancer specific pathway interaction networks

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2013, ECAL.

Pacific Symposium on Biocomputing 15:327-336(2010) ENABLING PERSONAL GENOMICS WITH AN EXPLICIT TEST OF EPISTASIS

Jason H. Moore, Casey S. Greene, Angeline S. Andrew, 2022 .

Supervising Random Forest Using Attribute Interaction Networks

Ting Hu, Jason H. Moore, James D. Malley, 2013, EvoBIO.

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

Jason H. Moore, Ryan J. Urbanowicz, Angeline S. Andrew, 2013, J. Am. Medical Informatics Assoc..

Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2014, BioData Mining.

Statistical Epistasis Networks Reduce the Computational Complexity of Searching Three-Locus Genetic Models

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2012, Pacific Symposium on Biocomputing.

Characterizing genetic interactions in human disease association studies using statistical epistasis networks

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2011, BMC Bioinformatics.

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

Jason H. Moore, Angeline S. Andrew, Peter C. Andrews, 2016, BioData Mining.

DNA Repair Polymorphisms Modify Bladder Cancer Risk: A Multi-factor Analytic Strategy

Paolo Vineis, Angeline S. Andrew, Margaret R. Karagas, 2007, Human Heredity.

A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis

Angeline S. Andrew, Margaret R. Karagas, Jiang Gui, 2010, Human Genetics.

Bladder cancer SNP panel predicts susceptibility and survival

Arthur C. Sanderson, Angeline S. Andrew, Margaret R. Karagas, 2009, Human Genetics.

HSD3B and Gene-Gene Interactions in a Pathway-Based Analysis of Genetic Susceptibility to Bladder Cancer

Jian Gu, Ting Hu, Angeline S. Andrew, 2012, PloS one.

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

Angeline S. Andrew, Margaret R. Karagas, Jiang Gui, 2010, Human Heredity.

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes

William S. Bush, Bin Zhu, Angeline S. Andrew, 2017, Nature Genetics.