Matching Items (28)
Description
Random forest (RF) is a popular and powerful technique nowadays. It can be used for classification, regression and unsupervised clustering. In its original form introduced by Leo Breiman, RF is used as a predictive model to generate predictions for new observations. Recent researches have proposed several methods based on RF for feature selection and for generating prediction intervals. However, they are limited in their applicability and accuracy. In this dissertation, RF is applied to build a predictive model for a complex dataset, and used as the basis for two novel methods for biomarker discovery and generating prediction interval.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
ContributorsGuan, Xin (Author) / Liu, Li (Thesis advisor) / Runger, George C. (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Physical activity, sedentary behaviors, and sleep are often associated with cardiometabolic biomarkers commonly found in metabolic syndrome. These relationships are well studied, and yet there are still questions on how each activity may affect cardiometabolic biomarkers. The objective of this study was to examine data from the BeWell24 studies to evaluate the relationship between objectively measured physical activity and sedentary behaviors and cardiometabolic biomarkers in middle age adults, while also determining if sleep quality and duration mediates this relationship. A group of inactive participants (N = 29, age = 52.1 ± 8.1 years, 38% female) with increased risk for cardiometabolic disease were recruited to participate in BeWell24, a trial testing the impact of a lifestyle-based, multicomponent smartphone application targeting sleep, sedentary, and more active behaviors. During baseline, interim (4 weeks), and posttest visits (8 weeks), biomarker measurements were collected for weight (kg), waist circumference (cm), glucose (mg/dl), insulin (uU/ml), lipids (mg/dl), diastolic and systolic blood pressures (mm Hg), and C reactive protein (mg/L). Participants wore validated wrist and thigh sensors for one week intervals at each time point to measure sedentary behavior, physical activity, and sleep outcomes. Long bouts of sitting time (>30 min) significantly affected triglycerides (beta = .15 (±.07), p<.03); however, no significant mediation effects for sleep quality or duration were present. No other direct effects were observed between physical activity measurements and cardiometabolic biomarkers. The findings of this study suggest that reductions in long bouts of sitting time may support reductions in triglycerides, yet these effects were not mediated by sleep-related improvements.
ContributorsLanich, Boyd (Author) / Buman, Matthew (Thesis advisor) / Ainsworth, Barbara (Committee member) / Huberty, Jennifer (Committee member) / Arizona State University (Publisher)
Created2017
Description
ABSTRACT
Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation (C-reactive protein) and an indicator of oxidative stress (TBARS) when compared to the control group receiving a placebo capsule (white flour) in healthy, sedentary adults between the ages of 18-40 in Phoenix, Arizona.
Design: This study was designed as secondary analyses of blood samples originally collected to study the effects of coconut oil supplementation on blood lipids and body composition. The original study consisted of 32 healthy, adult volunteers recruited from the Arizona State University campus in Phoenix, Arizona. Participants followed no food restrictions or special diets, exercised less than 150 minutes per week, had no diagnoses of chronic disease, were not taking statin medications, were non-smokers, and no female participants were pregnant. Participants were randomized into either the Coconut Oil group (CO) or the Placebo group (PL) at week 0, and baseline blood samples and anthropometric measurements were obtained. Each participant completed an 8-week protocol consisting of two supplement capsules daily (coconut oil or placebo). Final fasting blood samples and anthropometric measurements were taken at week 8. This study analyzed the blood samples for measurements of C-reactive protein (CRP) and thiobarbituric reactive substance (TBARS).
Results: Eight weeks of 2 grams per day coconut oil supplementation, in comparison to placebo treatment, did not significantly reduce serum CRP ( -13% and +51% respectively, p=0.183) but did significantly increase TBARS ( +16% and -27% respectively, p=0.049).
Conclusions: Coconut oil supplementation (2 g/day) may impact lipid peroxidation as indicated by an increase in plasma TBARS concentration. Future trials are necessary to corroborate these results using other indices of fatty peroxide formation.
Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation (C-reactive protein) and an indicator of oxidative stress (TBARS) when compared to the control group receiving a placebo capsule (white flour) in healthy, sedentary adults between the ages of 18-40 in Phoenix, Arizona.
Design: This study was designed as secondary analyses of blood samples originally collected to study the effects of coconut oil supplementation on blood lipids and body composition. The original study consisted of 32 healthy, adult volunteers recruited from the Arizona State University campus in Phoenix, Arizona. Participants followed no food restrictions or special diets, exercised less than 150 minutes per week, had no diagnoses of chronic disease, were not taking statin medications, were non-smokers, and no female participants were pregnant. Participants were randomized into either the Coconut Oil group (CO) or the Placebo group (PL) at week 0, and baseline blood samples and anthropometric measurements were obtained. Each participant completed an 8-week protocol consisting of two supplement capsules daily (coconut oil or placebo). Final fasting blood samples and anthropometric measurements were taken at week 8. This study analyzed the blood samples for measurements of C-reactive protein (CRP) and thiobarbituric reactive substance (TBARS).
Results: Eight weeks of 2 grams per day coconut oil supplementation, in comparison to placebo treatment, did not significantly reduce serum CRP ( -13% and +51% respectively, p=0.183) but did significantly increase TBARS ( +16% and -27% respectively, p=0.049).
Conclusions: Coconut oil supplementation (2 g/day) may impact lipid peroxidation as indicated by an increase in plasma TBARS concentration. Future trials are necessary to corroborate these results using other indices of fatty peroxide formation.
ContributorsNorman, Lisa (Author) / Johnston, Carol (Thesis advisor) / Shepard, Christina (Committee member) / Ellis, Melissa (Committee member) / Arizona State University (Publisher)
Created2017
Description
Obesity and its underlying insulin resistance are caused by environmental and genetic factors. DNA methylation provides a mechanism by which environmental factors can regulate transcriptional activity. The overall goal of the work herein was to (1) identify alterations in DNA methylation in human skeletal muscle with obesity and its underlying insulin resistance, (2) to determine if these changes in methylation can be altered through weight-loss induced by bariatric surgery, and (3) to identify DNA methylation biomarkers in whole blood that can be used as a surrogate for skeletal muscle.
Assessment of DNA methylation was performed on human skeletal muscle and blood using reduced representation bisulfite sequencing (RRBS) for high-throughput identification and pyrosequencing for site-specific confirmation. Sorbin and SH3 homology domain 3 (SORBS3) was identified in skeletal muscle to be increased in methylation (+5.0 to +24.4 %) in the promoter and 5’untranslated region (UTR) in the obese participants (n= 10) compared to lean (n=12), and this finding corresponded with a decrease in gene expression (fold change: -1.9, P=0.0001). Furthermore, SORBS3 was demonstrated in a separate cohort of morbidly obese participants (n=7) undergoing weight-loss induced by surgery, to decrease in methylation (-5.6 to -24.2%) and increase in gene expression (fold change: +1.7; P=0.05) post-surgery. Moreover, SORBS3 promoter methylation was demonstrated in vitro to inhibit transcriptional activity (P=0.000003). The methylation and transcriptional changes for SORBS3 were significantly (P≤0.05) correlated with obesity measures and fasting insulin levels. SORBS3 was not identified in the blood methylation analysis of lean (n=10) and obese (n=10) participants suggesting that it is a muscle specific marker. However, solute carrier family 19 member 1 (SLC19A1) was identified in blood and skeletal muscle to have decreased 5’UTR methylation in obese participants, and this was significantly (P≤0.05) predicted by insulin sensitivity.
These findings suggest SLC19A1 as a potential blood-based biomarker for obese, insulin resistant states. The collective findings of SORBS3 DNA methylation and gene expression present an exciting novel target in skeletal muscle for further understanding obesity and its underlying insulin resistance. Moreover, the dynamic changes to SORBS3 in response to metabolic improvements and weight-loss induced by surgery.
Assessment of DNA methylation was performed on human skeletal muscle and blood using reduced representation bisulfite sequencing (RRBS) for high-throughput identification and pyrosequencing for site-specific confirmation. Sorbin and SH3 homology domain 3 (SORBS3) was identified in skeletal muscle to be increased in methylation (+5.0 to +24.4 %) in the promoter and 5’untranslated region (UTR) in the obese participants (n= 10) compared to lean (n=12), and this finding corresponded with a decrease in gene expression (fold change: -1.9, P=0.0001). Furthermore, SORBS3 was demonstrated in a separate cohort of morbidly obese participants (n=7) undergoing weight-loss induced by surgery, to decrease in methylation (-5.6 to -24.2%) and increase in gene expression (fold change: +1.7; P=0.05) post-surgery. Moreover, SORBS3 promoter methylation was demonstrated in vitro to inhibit transcriptional activity (P=0.000003). The methylation and transcriptional changes for SORBS3 were significantly (P≤0.05) correlated with obesity measures and fasting insulin levels. SORBS3 was not identified in the blood methylation analysis of lean (n=10) and obese (n=10) participants suggesting that it is a muscle specific marker. However, solute carrier family 19 member 1 (SLC19A1) was identified in blood and skeletal muscle to have decreased 5’UTR methylation in obese participants, and this was significantly (P≤0.05) predicted by insulin sensitivity.
These findings suggest SLC19A1 as a potential blood-based biomarker for obese, insulin resistant states. The collective findings of SORBS3 DNA methylation and gene expression present an exciting novel target in skeletal muscle for further understanding obesity and its underlying insulin resistance. Moreover, the dynamic changes to SORBS3 in response to metabolic improvements and weight-loss induced by surgery.
ContributorsDay, Samantha Elaine (Author) / Coletta, Dawn K. (Thesis advisor) / Katsanos, Christos (Committee member) / Mandarino, Lawrence J. (Committee member) / Shaibi, Gabriel Q. (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Latino youth have substantially higher rates of obesity and T2D than their white peers. The higher prevalence of obesity and T2D among Latino youth places them at greater risk for cognitive dysfunction, an urgent and serious health threat to the United States. Exercise has been the cornerstone to combat the negative effects of obesity, diabetes and recent research also supports this effects for preventing cognitive dysfunction. A wealth of evidence suggests that a mediating mechanism linking exercise with brain health is BDNF, a cognitive biomarker that increases in the brain with exercise. BDNF is the most abundant neurotrophic factor that supports growth, survival and synaptic plasticity of neurons, all vital for cognitive function and brain health. The present study sought to investigate the effects of a 12-week lifestyle intervention of physical activity and lifestyle education on serum BDNF, in obese pre diabetic Latino youth.
A total of twelve obese pre diabetic Latino youth were selected from a larger RCT sample to be the focus for this analysis. After an overnight fast, a serum concentration was collected from all youth to be used for the BDNF analysis. In addition, the following cardio metabolic measures were also at taken at baseline and post intervention: Submaximal VO2max, medical and family history questionnaire, anthropometric, fasting glucose and a 2-hour oral glucose tolerance test (OGTT). A 12-weeks Lifestyle Intervention that involved a progressive moderate to high intensity exercise component and lifestyle education program did not significantly change serum BDNF levels in obese pre diabetic Latino youth. In conclusion, the variation of our serum BDNF results are highly speculative at this time, therefore the need for future investigations is crucial.
A total of twelve obese pre diabetic Latino youth were selected from a larger RCT sample to be the focus for this analysis. After an overnight fast, a serum concentration was collected from all youth to be used for the BDNF analysis. In addition, the following cardio metabolic measures were also at taken at baseline and post intervention: Submaximal VO2max, medical and family history questionnaire, anthropometric, fasting glucose and a 2-hour oral glucose tolerance test (OGTT). A 12-weeks Lifestyle Intervention that involved a progressive moderate to high intensity exercise component and lifestyle education program did not significantly change serum BDNF levels in obese pre diabetic Latino youth. In conclusion, the variation of our serum BDNF results are highly speculative at this time, therefore the need for future investigations is crucial.
ContributorsBarraza, Estela (Author) / Shaibi, Gabriel Q. (Thesis advisor) / Swan, Pamela (Committee member) / Nanez, Jose E (Committee member) / Arizona State University (Publisher)
Created2016
Description
Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to 25% when breast cancer is diagnosed late. Immune checkpoint blockage has shown to be a promising therapy to improve patients’ outcome in many other cancers. However, due to the lack of early diagnosis, the treatment is normally given in the later stages. An early diagnosis system for breast cancer could potentially revolutionize current treatment strategies, improve patients’ outcomes and even eradicate the disease. The current breast cancer diagnostic methods cannot meet this demand. A simple, effective, noninvasive and inexpensive early diagnostic technology is needed. Immunosignature technology leverages the power of the immune system to find cancer early. Antibodies targeting tumor antigens in the blood are probed on a high-throughput random peptide array and generate a specific binding pattern called the immunosignature.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
ContributorsDuan, Hu (Author) / Johnston, Stephen Albert (Thesis advisor) / Hartwell, Leland Harrison (Committee member) / Dinu, Valentin (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015
Description
In the U.S., breast cancer (BC) incidences among African American (AA) and CA (CA) women are similar, yet AA women have a significantly higher mortality rate. In addition, AA women often present with tumors at a younger age, with a higher tumor grade/stage and are more likely to be diagnosed with the highly aggressive triple-negative breast cancer (TNBC) subtype. Even within the TNBC subtype, AA women have a worse clinical outcome compared to CA. Although multiple socio-economic and lifestyle factors may contribute to these observed health disparities, it is essential that the underlying biological differences between CA and AA TNBC are identified. In this study, gene expression profiling was performed on archived FFPE samples, obtained from CA and AA women diagnosed with early stage TNBC. Initial analysis revealed a pattern of differential expression in the AA cohort compared to CA. Further molecular characterization results showed that the AA cohort segregated into 3-TNBC molecular subtypes; Basal-like (BL2), Immunomodulatory (IM) and Mesenchymal (M). Gene expression analyses resulted in 190 differentially expressed genes between the AA and CA cohorts. Pathway enrichment analysis demonstrated that differentially expressed genes were over-represented in cytoskeletal remodeling, cell adhesion, tight junctions, and immune response in the AA TNBC -cohort. Furthermore, genes in the Wnt/β-catenin pathway were over-expressed. These results were validated using RT-qPCR on an independent cohort of FFPE samples from AA and CA women with early stage TNBC, and identified Caveolin-1 (CAV1) as being significantly expressed in the AA-TNBC cohort. Furthermore, CAV1 was shown to be highly expressed in a cell line panel of TNBC, in particular, those of the mesenchymal and basal-like molecular subtype. Finally, silencing of CAV1 expression by siRNA resulted in a significant decrease in proliferation in each of the TNBC cell lines. These observations suggest that CAV1 expression may contribute to the more aggressive phenotype observed in AA women diagnosed with TNBC.
ContributorsGetz, Julie (Author) / Baumbach-Reardon, Lisa L (Thesis advisor) / Lake, Douglas F (Thesis advisor) / Bussey, Kimberly (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2015
Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array
Description
Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the potential to detect diseases in a non-invasive way. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce autoantibody (AAb) responses, which can amplify the signal and persist in the blood even if the antigen is gone. Circulating autoantibodies is a promising class of molecules that have potential to serve as early detection biomarkers for cancers. This Ph.D thesis aims to screen for autoantibody biomarkers for the early detection of two deadly cancer, basal-like breast cancer and lung adenocarcinoma. First, a method was developed to display proteins in both native and denatured conformation on protein array. This method adopted a novel protein tag technology, called HaloTag, to covalently immobilize proteins on glass slide surface. The covalent attachment allowed these proteins to endure harsh treatment without getting dissociated from slide surface, which enabled the profiling of antibody responses against both conformational and linear epitopes. Next, a plasma screening protocol was optimized to significantly increase signal to noise ratio of protein array based AAb detection. Following this, the AAb responses in basal-like breast cancer were explored using nucleic acid programmable protein arrays (NAPPA) containing 10,000 full-length human proteins in 45 cases and 45 controls. After verification in a large sample set (145 basal-like breast cancer cases / 145 controls / 70 non-basal breast cancer) by ELISA, a 13-AAb classifier was developed to differentiate patients from controls with a sensitivity of 33% at 98% specificity. Similar approach was also applied to the lung cancer study to identify AAbs that distinguished lung cancer patients from computed-tomography positive benign pulmonary nodules (137 lung cancer cases, 127 smoker controls, 170 benign controls). In this study, two panels of AAbs were discovered that showed promising sensitivity and specificity. Six out of eight AAb targets were also found to have elevated mRNA level in lung adenocarcinoma patients using TCGA data. These projects as a whole provide novel insights on the association between AAbs and cancer, as well as general B cell antigenicity against self-proteins.
ContributorsWang, Jie (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Lake, Douglas F (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015