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You searched for +publisher:"University of Texas Southwestern Medical Center" +contributor:("Xiao, Guanghua"). Showing records 1 – 3 of 3 total matches.

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University of Texas Southwestern Medical Center

1. Ma, Tsung-wei. Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients.

Degree: 2017, University of Texas Southwestern Medical Center

This dissertation is composed of two psychological survey studies. In the first study, people's negative attitudes toward lung cancer are assessed and discussed. The second topic is about predicting the remission rates of major depressive disorder patients with patients' self-reported questionnaires. In the first topic, I analyzed data from The Lung Cancer Project, an online survey study, to assess both explicit and implicit attitudes expressed by the four participant groups: health care professionals, cancer patients, caregivers and the general public. Negative attitudes toward lung cancer were detected among all these participant groups. I also discovered several demographic factors significantly associated with negative attitudes toward lung cancer. Furthermore, I investigated the association between state-level perceptions of lung cancer (including both explicit and implicit attitudes) and rates of treatment (drug treatment rates or total treatment rates, including surgery, chemotherapy, radiation, and immunotherapy) for lung cancer patients in the corresponding states. In the second topic, existing data from the Combining Medications to Enhance Depression Outcomes (CO-MED) trial were utilized to develop a data-driven method for mapping the behavioral factors to the constructs defined in Research Domain Criteria (RDoC). And I used the defined behavioral factors from CO-MED to discover patient subgroups. In further analysis, I found that the discovered patient subgroups have significantly different remission rates to the antidepressant treatment, which indicates that there are three endo-phenotypes in major depression disorder. Advisors/Committee Members: Zhan, Xiaowei, Xie, Yang, Xiao, Guanghua, Schiller, Joan H., Gazdar, Adi.

Subjects/Keywords: Attitude of Health Personnel; Health Personnel; Lung Neoplasms; Mental Disorders; Psychiatry; Research; Social Stigma

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ma, T. (2017). Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients. (Thesis). University of Texas Southwestern Medical Center. Retrieved from http://hdl.handle.net/2152.5/7741

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ma, Tsung-wei. “Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients.” 2017. Thesis, University of Texas Southwestern Medical Center. Accessed May 07, 2021. http://hdl.handle.net/2152.5/7741.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ma, Tsung-wei. “Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients.” 2017. Web. 07 May 2021.

Vancouver:

Ma T. Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients. [Internet] [Thesis]. University of Texas Southwestern Medical Center; 2017. [cited 2021 May 07]. Available from: http://hdl.handle.net/2152.5/7741.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ma T. Two Psychological Survey Studies: (1) Understanding the Stigma Toward Lung Cancer and (2) Using Research Domain Criteria Project (RDoC) to Predict Remission Rates of Major Depressive Disorder Patients. [Thesis]. University of Texas Southwestern Medical Center; 2017. Available from: http://hdl.handle.net/2152.5/7741

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas Southwestern Medical Center

2. Wang, Tao. Understanding RNA Regulation Through Analysis of CLIP-Seq Data.

Degree: 2015, University of Texas Southwestern Medical Center

The past decades have witnessed a surge of discoveries revealing RNA regulation as a central player in cellular processes. The advent of cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) technology has recently enabled the investigation of genome-wide RNA binding protein-RNA interactions, which is a very important component of RNA-regulation. However, proper and systematic bioinformatics analysis of CLIP-Seq data is still lacking and challenging. For the past few years, I have been devoting my research to methodological developments of CLIP-Seq data analysis, and developed MiClip and dCLIP for peak calling and differential analysis of CLLIP-Seq data, respectively. I have also applied my CLIP-Seq analysis pipelines in on-campus collaborating projects, in which I identified ORF57 and nuclear AGO2 binding sites. Finally, I conducted analysis of public CLIP-Seq datasets to systematically characterize RNA binding protein targeting sites on circular RNAs. Advisors/Committee Members: Mendell, Joshua T., Xie, Yang, Xiao, Guanghua, Mangelsdorf, David J., Zhang, Michael Q..

Subjects/Keywords: Genomics; High-Throughput Nucleotide Sequencing; RNA; RNA-Binding Proteins; Sequence Analysis, RNA

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, T. (2015). Understanding RNA Regulation Through Analysis of CLIP-Seq Data. (Thesis). University of Texas Southwestern Medical Center. Retrieved from http://hdl.handle.net/2152.5/4457

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Tao. “Understanding RNA Regulation Through Analysis of CLIP-Seq Data.” 2015. Thesis, University of Texas Southwestern Medical Center. Accessed May 07, 2021. http://hdl.handle.net/2152.5/4457.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Tao. “Understanding RNA Regulation Through Analysis of CLIP-Seq Data.” 2015. Web. 07 May 2021.

Vancouver:

Wang T. Understanding RNA Regulation Through Analysis of CLIP-Seq Data. [Internet] [Thesis]. University of Texas Southwestern Medical Center; 2015. [cited 2021 May 07]. Available from: http://hdl.handle.net/2152.5/4457.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang T. Understanding RNA Regulation Through Analysis of CLIP-Seq Data. [Thesis]. University of Texas Southwestern Medical Center; 2015. Available from: http://hdl.handle.net/2152.5/4457

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas Southwestern Medical Center

3. Zhong, Rui. Tackling Computational Challenges in High-Throughput RNA Interference Screening.

Degree: 2014, University of Texas Southwestern Medical Center

Since the discovery of RNAi decades ago, it has been increasingly used in biomedical and biological research. The success of analyzing single genes using siRNAs has resulted in the large-scale application of RNAi for genome-wide loss-of-function phenotype screening while reducing cost and decreasing time. High-throughput RNAi screening (HTS) has been widely accepted and used in a variety of biomedical and biological research projects as the first step to identifying novel drug targets or pathway components. Huge data sets are being generated, but computational challenges remain in data analysis and hit identification, which have become hurdles in HTS. These must be tackled before we can more accurately and precisely interpret the HTS results, since they are often blurred by spatial noise and off-target effects. In my thesis research, I have been working on statistical modeling of high-throughput RNAi screening results. I developed SbacHTS (spatial background noise correction in high-throughput RNAi screening) to identify and remove spatially-correlated background noise from HTS, which helps enhance statistical detection power in triplicate experiments. On top of that, I also created a novel algorithm, DeciRNAi (deconvolution analysis high-throughput RNAi screening results), to quantify the strength and direction of siRNA-mimic-miRNA off-target effects in HTS projects. As a special case, image-based high-content HTS requires management of high-dimensional data analysis and visualization. I built a new R package “iScreen” (image-based high-throughput RNAi screening analysis tools) to deal with such problems. Advisors/Committee Members: Minna, John D., Shay, Jerry W., White, Michael A., Xie, Yang, Xiao, Guanghua.

Subjects/Keywords: High-Throughput Screening Assays; RNA Interference; Software

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhong, R. (2014). Tackling Computational Challenges in High-Throughput RNA Interference Screening. (Thesis). University of Texas Southwestern Medical Center. Retrieved from http://hdl.handle.net/2152.5/3335

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhong, Rui. “Tackling Computational Challenges in High-Throughput RNA Interference Screening.” 2014. Thesis, University of Texas Southwestern Medical Center. Accessed May 07, 2021. http://hdl.handle.net/2152.5/3335.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhong, Rui. “Tackling Computational Challenges in High-Throughput RNA Interference Screening.” 2014. Web. 07 May 2021.

Vancouver:

Zhong R. Tackling Computational Challenges in High-Throughput RNA Interference Screening. [Internet] [Thesis]. University of Texas Southwestern Medical Center; 2014. [cited 2021 May 07]. Available from: http://hdl.handle.net/2152.5/3335.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhong R. Tackling Computational Challenges in High-Throughput RNA Interference Screening. [Thesis]. University of Texas Southwestern Medical Center; 2014. Available from: http://hdl.handle.net/2152.5/3335

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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