Bayesian data integration and variable selection for pan‐cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/1/biom13132_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/2/biom13132.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/3/biom13132-sup-0003-supmat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/4/biom13132-sup-0002-supplementary-v6-22Jul2019.pd

Randomization‐based statistical inference: A resampling and simulation infrastructure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143752/1/test12156_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143752/2/test12156.pd

The Effect of Student-Directed Transition Planning With a Computer-Based Reading Support Program on the Self-Determination of Students With Disabilities

The purpose of this study was to investigate the impact of student-directed transition planning instruction (Whose Future Is It Anyway? curriculum) with a computer-based reading support program (Rocket Reader) on the self-determination, self-efficacy and outcome expectancy, and transition planning knowledge of students with disabilities. This study employed a pre- and postmeasure design with 168 middle school students with disabilities who were assigned to an experimental group (n = 86) and control group (n = 82). The results of the study demonstrated that self-determination, self-efficacy, and outcome expectancy for education planning improved through the application of Rocket Reader . Avenues are discussed for promoting middle school students’ self-determination in their transition planning, as are implications for future research.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Contribuição da heterogeneidade de linhas de regressão para a recomendação de novas cultivares

O objetivo deste trabalho foi evidenciar a contribuição da análise da heterogeneidade das linhas de regressão para a recomendação de novas cultivares, pela metodologia de Eberhart & Russell. Um experimento com dez genótipos de arroz, avaliados em oito ambientes, foi utilizado para ilustração da metodologia. Utilizou-se o delineamento de blocos ao acaso com três repetições. A soma dos quadrados da interação genótipo x ambiente (GxA) foi decomposta para avaliação da heterogeneidade das linhas de regressão e dos desvios acumulados da linearidade. A heterogeneidade das linhas de regressão foi analisada com o uso do teste t sobre os coeficientes de regressão linear dos genótipos. Os dois componentes ortogonais da interação GxA foram significativos. A análise da heterogeneidade das linhas de regressão permitiu detectar incoerências na adaptabilidade dos genótipos, o que diminui as chances de recomendações equivocadas de cultivares. Os genótipos foram classificados de acordo com a eficiência da metodologia de Eberhart & Russell em explicar a natureza do desempenho genotípico diante das mudanças nos ambientes. A avaliação da heterogeneidade das linhas de regressão contribui para a recomendação mais efetiva de novas cultivares com a metodologia de Eberhart & Russell