Bayesian model-based approaches with MCMC computation to some bioinformatics problems

Bioinformatics applications can address the transfer of information at several stages of the central dogma of molecular biology, including transcription and translation. This dissertation focuses on using Bayesian models to interpret biological data in bioinformatics, using Markov chain Monte Carlo (MCMC) for the inference method. First, we use our approach to interpret data at the transcription level. We propose a two-level hierarchical Bayesian model for variable selection on cDNA Microarray data. cDNA Microarray quantifies mRNA levels of a gene simultaneously so has thousands of genes in one sample. By observing the expression patterns of genes under various treatment conditions, important clues about gene function can be obtained. We consider a multivariate Bayesian regression model and assign priors that favor sparseness in terms of number of variables (genes) used. We introduce the use of different priors to promote different degrees of sparseness using a unified two-level hierarchical Bayesian model. Second, we apply our method to a problem related to the translation level. We develop hidden Markov models to model linker/non-linker sequence regions in a protein sequence. We use a linker index to exploit differences in amino acid composition between regions from sequence information alone. A goal of protein structure prediction is to take an amino acid sequence (represented as a sequence of letters) and predict its tertiary structure. The identification of linker regions in a protein sequence is valuable in predicting the three-dimensional structure. Because of the complexities of both models encountered in practice, we employ the Markov chain Monte Carlo method (MCMC), particularly Gibbs sampling (Gelfand and Smith, 1990) for the inference of the parameter estimation

Impact of biochemical failure classification on clinical outcome: A secondary analysis of Radiation Therapy Oncology Group 9202 and 9413

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110839/1/cncr29146.pd

Determinants of change in prostate-specific antigen over time and its association with recurrence after external beam radiation therapy for prostate cancer in five large cohorts.: Determinants of change of prostate-specific antigen over time

International audiencePURPOSE: To assess the relationship between prognostic factors, postradiation prostate-specific antigen (PSA) dynamics, and clinical failure after prostate cancer radiation therapy using contemporary statistical models. METHODS AND MATERIALS: Data from 4,247 patients with 40,324 PSA measurements treated with external beam radiation monotherapy in five cohorts were analyzed. Temporal change of PSA after treatment completion was described by a specially developed linear mixed model that included standard prognostic factors. These factors, along with predicted PSA evolution, were incorporated into a Cox model to establish their predictive value for the risk of clinical recurrence over time. RESULTS: Consistent relationships were found across cohorts. The initial PSA decline after radiation therapy was associated with baseline PSA and T-stage (p < 0.001). The long-term PSA rise was associated with baseline PSA, T-stage, and Gleason score (p < 0.001). The risk of clinical recurrence increased with current level (p < 0.001) and current slope of PSA (p < 0.001). In a pooled analysis, higher doses of radiation were associated with a lower long-term PSA rise (p < 0.001) but not with the risk of recurrence after adjusting for PSA trajectory (p = 0.63). Conversely, after adjusting for other factors, increased age at diagnosis was not associated with long-term PSA rise (p = 0.85) but was directly associated with decreased risk of recurrence (p < 0.001). CONCLUSIONS: We conclude that a linear mixed model can be reliably used to construct typical patient PSA profiles after prostate cancer radiation therapy. Pretreatment factors along with PSA evolution and the associated risk of recurrence provide an efficient and quantitative way to assess the impact of risk factors on disease progression