Browsing by Author "Guo, Yi"

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    Potentially functional genetic variants in the TNF/TNFR signaling pathway genes predict survival of patients with non-small cell lung cancer in the PLCO cancer screening trial.
    (Molecular carcinogenesis, 2019-04-15) Guo, Yi; Feng, Yun; Liu, Hongliang; Luo, Sheng; Clarke, Jeffrey W; Moorman, Patricia G; Su, Li; Shen, Sipeng; Christiani, David C; Wei, Qingyi
    The tumor necrosis factor (TNF)/TNF receptor (TNFR) pathway is known to influence survival of patients with cancer. We hypothesize that single nucleotide polymorphisms (SNPs) in the TNF/TNFR pathway genes related to apoptosis are associated with survival of patients with non-small cell lung cancer (NSCLC). We used 1185 patients with NSCLC in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 984 patients with NSCLC in the Harvard Lung Cancer Susceptibility Study as the discovery and validation datasets, respectively. We selected 6788 SNPs in 71 genes in the TNF/TNFR signaling pathway and extracted their genotyping data from the PLCO genowide-association study (GWAS) dataset. We performed Cox proportional hazards regression analysis to evaluate associations between the identified SNPs and survival and validated the significant SNPs, which were further analyzed for their functional relevance. We found that genotypes of two validated SNPs, IKBKAP rs4978754 CT + TT and TNFRSF1B rs677844 TC + CC, as well as their combined genotypes predicted a better overall survival (P = 0.004, 0.002 and <0.001, respectively). These two validated SNPs were predicted by the RegulomeDB score to be potentially functional. In addition, IKBKAP mRNA expression levels were significantly higher, while TNFRSF1B mRNA expression levels were significantly lower in lung cancer tissues than in adjacent normal tissues (P < 0.001). The Cancer Genome Atlas (TCGA)-based expression quantitative trait loci analysis showed that IKBKAP rs4978754 and TNFRSF1B rs677844 genotypes were significantly associated with their corresponding mRNA expression levels in lung cancer tissues in a recessive model (P = 0.035 and 0.045, respectively). Therefore, we identified two potentially functional SNPs (IKBKAP rs4978754 C > T and TNFRSF1B rs677844 T > C) to be associated with survival of patients with NSCLC.
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    Problems in Computational Advertising
    (2021) Guo, Yi

    Computational advertising is a multi-billion-dollar industry, yet it has gotten little attention from academic statisticians. Despite this, the performance of this collection of pricing models, keyword auctions, A/B testing, and recommender systems is largely reliant on statistical technique in almost every element of its design and implementation.

    Online ad auctions and e-commercial logistics are two of the major components of computational advertising. In a real-time bidding scenario, the objective for the former is to maximize expected utilities. The latter is concerned with the development of statistical modeling for dynamic continuous flows. In turn, this leads to a range of various issues, three of which are discussed in this thesis.

    Chapter 1 briefly introduces the topics of online advertising and computational advertising. Chapter 2 proposes a new method, the Backwards Indifference Derivation (BID) algorithm, to numerically approximate the pure strategy Nash equilibrium (PSNE) bidding functions in asymmetric first-price auctions. The classic PSNE solution assumes that all parties agree on the type distribution for each participant, and all know that this information is held in common. This common knowledge assumption is strong and often unrealistic. Chapter 3 addresses that gap by providing two alternative solutions, each based upon an adversarial risk analysis (ARA) perspective. Chapter 4 extends the previous methodology for Bayesian dynamic flow models of discrete data to real-valued and positive flows. Finally, Chapter 5 presents some concluding remarks and briefly discusses other problems in computational advertising.