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Journal of the American Statistical Association Volume 114, 2019 - Issue 525
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Applications and Case Studies

Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

Yang NiDepartment of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX
,
Francesco C. StingoDepartment of Statistics, Computer Science, Applications “G. Parenti”, The University of Florence, Florence, ItalyORCID Iconhttp://orcid.org/0000-0001-9150-8552
ORCID Icon,
Min Jin HaDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
,
Rehan AkbaniDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
&
Veerabhadran BaladandayuthapaniDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
Pages 48-60 | Received 01 May 2017, Published online: 15 Aug 2018

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Rui Vitorino, Manisha Choudhury, Sofia Guedes, Rita Ferreira, Visith Thongboonkerd, Lakshya Sharma, Francisco Amado & Sanjeeva Srivastava. (2021) Peptidomics and proteogenomics: background, challenges and future needs. Expert Review of Proteomics 18:8, pages 643-659.
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Rui Vitorino, Sofia Guedes, Fabio Trindade, Inês Correia, Gabriela Moura, Paulo Carvalho, Manuel A. S. Santos & Francisco Amado. (2020) De novo sequencing of proteins by mass spectrometry. Expert Review of Proteomics 17:7-8, pages 595-607.
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Fangting Zhou, Kejun He & Yang Ni. (2023) Individualized Causal Discovery with Latent Trajectory Embedded Bayesian Networks. Biometrics 79:4, pages 3191-3202.
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Priyam Das, Christine B. Peterson, Yang Ni, Alexandre Reuben, Jiexin Zhang, Jianjun Zhang, Kim-Anh Do & Veerabhadran Baladandayuthapani. (2023) Bayesian Hierarchical Quantile Regression with Application to Characterizing the Immune Architecture of Lung Cancer. Biometrics 79:3, pages 2474-2488.
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Matteo Pedone, Amedeo Amedei & Francesco C. Stingo. (2023) Subject-specific Dirichlet-multinomial regression for multi-district microbiota data analysis. The Annals of Applied Statistics 17:1.
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Mingrui Liang, Matthew D. Koslovsky, Emily T. Hébert, Michael S. Businelle & Marina Vannucci. (2023) Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors. Bayesian Analysis -1:-1.
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Oliver M. Crook, Chun-wa Chung & Charlotte M. Deane. (2022) Challenges and Opportunities for Bayesian Statistics in Proteomics. Journal of Proteome Research 21:4, pages 849-864.
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Magnus M. Münch, Mark A. van de Wiel, Sylvia Richardson & Gwenaël G. R. Leday. (2020) Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data. Biometrical Journal 63:2, pages 289-304.
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Benjamin Heuclin, Frédéric Mortier, Catherine Trottier & Marie Denis. (2021) Bayesian Varying Coefficient Model with Selection: An Application to Functional Mapping. Journal of the Royal Statistical Society Series C: Applied Statistics 70:1, pages 24-50.
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Ani Eloyan, Mun Sang Yue & Davit Khachatryan. (2020) Tumor heterogeneity estimation for radiomics in cancer. Statistics in Medicine 39:30, pages 4704-4723.
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