Browsing by Subject "Bayes Theorem"

Now showing 1 - 8 of 8
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    Conventional, Bayesian, and Modified Prony's methods for characterizing fast and slow waves in equine cancellous bone
    (AIP Publishing, 2015-08) Groopman, Amber M.; Katz, Jonathan I.; Fujita, Fuminori; Matsukawa, Mami; Mizuno, Katsunori; Wear, Keith A.; Miller, James G.; Department of Radiology and Imaging Sciences, IU School of Medicine
    Conventional, Bayesian, and the modified least-squares Prony's plus curve-fitting (MLSP + CF) methods were applied to data acquired using 1 MHz center frequency, broadband transducers on a single equine cancellous bone specimen that was systematically shortened from 11.8 mm down to 0.5 mm for a total of 24 sample thicknesses. Due to overlapping fast and slow waves, conventional analysis methods were restricted to data from sample thicknesses ranging from 11.8 mm to 6.0 mm. In contrast, Bayesian and MLSP + CF methods successfully separated fast and slow waves and provided reliable estimates of the ultrasonic properties of fast and slow waves for sample thicknesses ranging from 11.8 mm down to 3.5 mm. Comparisons of the three methods were carried out for phase velocity at the center frequency and the slope of the attenuation coefficient for the fast and slow waves. Good agreement among the three methods was also observed for average signal loss at the center frequency. The Bayesian and MLSP + CF approaches were able to separate the fast and slow waves and provide good estimates of the fast and slow wave properties even when the two wave modes overlapped in both time and frequency domains making conventional analysis methods unreliable.
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    Correction to: Selective kappa-opioid antagonism ameliorates anhedonic behavior: evidence from the Fast-fail Trial in Mood and Anxiety Spectrum Disorders (FAST-MAS)
    (Springer Nature, 2021) Pizzagalli, Diego A.; Smoski, Moria; Ang, Yuen-Siang; Whitton, Alexis E.; Sanacora, Gerard; Mathew, Sanjay J.; Nurnberger, John, Jr.; Lisanby, Sarah H.; Iosifescu, Dan V.; Murrough, James W.; Yang, Hongqiu; Weiner, Richard D.; Calabrese, Joseph R.; Goodman, Wayne; Potter, William Z.; Krystal, Andrew D.; Psychiatry, School of Medicine
    Correction to: Neuropsychopharmacology 10.1038/s41386-020-0738-4, published online 16 June 2020 In this article a conflict of interest was missing. The co-author Sanjay J. Mathew served as a consultant to Alkermes. The original article has been corrected. The original article can be found online at 10.1038/s41386-020-0738-4.
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    CWL: A conditional weighted likelihood method to account for the delayed joint toxicity-efficacy outcomes for phase I/II clinical trials
    (Sage, 2021-03) Zhang, Yifei; Zang, Yong; Biostatistics, School of Public Health
    The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity–efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient’s actual follow-up time. The CWL method makes no parametric model assumption on either the dose–response curve or the toxicity–efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.
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    Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning
    (IEEE, 2019-11) Hao, Xiaoke; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Yu, Jintai; Wang, Huifu; Tan, Lan; Shen, Li; Zhang, Daoqiang; Radiology and Imaging Sciences, School of Medicine
    Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More recently, to address the problem of multiple comparison and weak detection, multivariate analysis methods such as the least absolute shrinkage and selection operator (Lasso) are often used to select the most relevant SNPs associated with QTs. However, one problem of Lasso, as well as many other feature selection methods for imaging genetics, is that some useful prior information, e.g., the hierarchical structure among SNPs, are rarely used for designing a more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the complex hierarchical structure among the SNPs in the objective function for feature selection. Specifically, motivated by the biological knowledge, the hierarchical structures involving gene groups and linkage disequilibrium (LD) blocks as well as individual SNPs are imposed as a tree-guided regularization term in our TGSL model. Experimental studies on simulation data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data show that our method not only achieves better predictions than competing methods on the MRI-derived measures of AD-related region of interests (ROIs) (i.e., hippocampus, parahippocampal gyrus, and precuneus), but also identifies sparse SNP patterns at the block level to better guide the biological interpretation.
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    Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning
    (Institute of Electrical and Electronics Engineers, 2014-07) Wan, Jing; Zhang, Zhilin; Rao, Bhaskar D.; Fang, Shiaofen; Yan, Jingwen; Saykin, Andrew J.; Shen, Li; Department of Medicine, IU School of Medicine
    Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.
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    Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization
    (Public Library of Science, 2015) Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami; Department of Pediatrics, IU School of Medicine
    6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.
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    A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer.
    (BioMed Central, 2011-05-09) Shen, Changyu; Huang, Yiwen; Liu, Yunlong; Wang, Guohua; Zhao, Yuming; Wang, Zhiping; Teng, Mingxiang; Wang, Yadong; Flockhart, David A.; Skaar, Todd C.; Yan, Pearlly; Nephew, Kenneth P.; Huang, Tim Hm; Li, Lang
    BACKGROUND: Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. RESULTS: We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ERα target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ERα regulatory network was unresponsive to 17β-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations. CONCLUSIONS: We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.
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    Polygenic risk score penetrance & recurrence risk in familial Alzheimer disease
    (Wiley, 2023) Qiao, Min; Lee, Annie J.; Reyes-Dumeyer, Dolly; Tosto, Giuseppe; Faber, Kelley; Goate, Alison; Renton, Alan; Chao, Michael; Boeve, Brad; Cruchaga, Carlos; Pericak-Vance, Margaret; Haines, Jonathan L.; Rosenberg, Roger; Tsuang, Debby; Sweet, Robert A.; Bennett, David A.; Wilson, Robert S.; Foroud, Tatiana; Mayeux, Richard; Vardarajan, Badri N.; Medical and Molecular Genetics, School of Medicine
    Objective: To compute penetrance and recurrence risk using a genome-wide PRS (including and excluding the APOE region) in families with Alzheimer's disease. Methods: Genotypes from the National Institute on Aging Late-Onset Alzheimer's Disease Family-Based Study and a study of familial Alzheimer's disease in Caribbean Hispanics were used to compute PRS with and without variants in the 2 MB region flanking APOE. PRS was calculated in using clumping/thresholding and Bayesian methods and was assessed for association with Alzheimer's disease and age at onset. Penetrance and recurrence risk for carriers in highest and lowest PRS quintiles were compared separately within APOE-ε4 carriers and non-carriers. Results: PRS excluding the APOE region was strongly associated with clinical and neuropathological diagnosis of AD. PRS association with AD was similar in participants who did not carry an APOE-ε4 allele (OR = 1.74 [1.53-1.91]) compared with APOE-ε4 carriers (1.53 [1.4-1.68]). Compared to the lowest quintile, the highest PRS quintile had a 10% higher penetrance at age 70 (p = 0.0006) and a 20% higher penetrance at age 80 (p < 10e-05). Stratifying by APOE-ε4 allele, PRS in the highest quintile was significantly more penetrant than the lowest quintile, both, within APOE-ε4 carriers (14.5% higher at age 80, p = 0.002) and non-carriers (26% higher at 80, p < 10e-05). Recurrence risk for siblings conferred by a co-sibling in the highest PRS quintile increased from 4% between the ages of 65-74 years to 39% at age 85 and older. Interpretation: PRS can be used to estimate penetrance and recurrence risk in familial Alzheimer's disease among carriers and non-carries of APOE-ε4.