2015 — 2016 |
Wang, Chi Bonikowski, Bart [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Doctoral Dissertation Research: the Process and Consequences of the Work of 911 Dispatchers
This doctoral dissertation research seeks to understand the process and consequences of 911 dispatchers' daily work. The study focuses on how dispatchers understand and respond to citizens' emergency situations communicated through 911 lines, how dispatchers code citizens' calls in the Computer Aided Dispatch (CAD) system, the source of dispatcher stress, and how dispatchers cope with stress, are trained, treated and perceived by their colleagues and related organizations. Extensive fieldwork will be conducted in an urban police department's emergency communication center where 911 calls are answered and units dispatched. An analysis will be completed of data from day-to-day actions in the center, interviews with dispatchers, directors, supervisors and trainees, records of past calls, incoming calls, information from CAD system, casual interactions in the operation room and dining area, training manuals and books, as well as other relevant documents and files from the media, the city, state court, and the state police. This study contributes to three bodies of literature in the field of sociology: state classification practices, street-level bureaucracy and the sociology of the professions. The empirical analysis seeks to answer the classical and present-day theoretical question of how the state is represented to and experienced by citizens.
The research examines the process through which the state classification system is applied and the costs and consequences of this process. The field site provides access to all incoming phone-calls, phone records, training materials, on-site observation interviews as well as CAD log-ins from an urban, big-city police department. The study will potentially demonstrate how street-level state employees (911 dispatchers) engage in state classification that bridges complex social life and clear-cut bureaucratic categories. It contributes to an increased understanding of how state categories are applied and reproduced, and how the correspondence between the outside world and state forms of representation is accomplished through the constant efforts of human agents. The research will also provide practical and policy-relevant guidance regarding the stress and trails experienced by 911 dispatchers and provide for citizens a greater understanding of how the 911 dispatch system works.
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0.957 |
2016 |
Flight, Robert Maxwell (co-PI) [⬀] Wang, Chi |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Differential Abundance Methods For Large Heterogeneous-Featured Metabolomics Datasets
Project Summary Metabolomics deals with the systematic identification and quantification of small molecules in biological systems. Frequently, metabolomics studies aim to identify those metabolites that have differential abundances between two or more conditions. However, in the very large untargeted metabolomics datasets being generated today, there are often many detected metabolite features that will be zero for a large fraction of samples in either/or both sample classes, creating data sparsity. Previous work has been done to develop statistical methods capable of testing for differential abundances in metabolomics datasets with high data sparsity (i.e. large fraction of zero values in the dataset). However, these methods are not appropriate for data from matched pair experimental designs, which are expected to become the standard as metabolomics is applied to more and more human disease studies. Furthermore, the currently available methods either make simplistic statistical assumptions, or use the simplest method for not making assumptions about the data available, which are not necessarily appropriate. In addition, peak assignment and correspondence ambiguities play a large role in the zero values and redundancy seen in these datasets. However, no methods have been developed to directly address these issues. In this proposal, we will develop novel informatics and statistical methods that address these distinct issues seen in large heterogeneous featured metabolomics datasets: i) a fuzzy set-based algorithm method that addresses peak assignment and correspondence ambiguities and ii) a semi-parametric method to perform differential abundance analysis for metabolomics datasets with high data sparsity, possibly non-normally distributed data, and matched-pairs experimental designs. We will use simulation studies to assess how well these new methods address the aforementioned issues and how much they improve the power of differential abundance analysis. Finally, we will make these new methods available through Bioconductor packages and a web-based service.
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0.954 |
2016 — 2017 |
Moseley, Hunter Nathaniel Wang, Chi |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Statistical Detection and Biochemical Classification of Cancer Driver Mutation Patterns in Biological Networks
? DESCRIPTION (provided by applicant): Cancer arises from somatically acquired genetic and epigenetic alterations. While large consortia like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have profiled genomic somatic mutations of thousands of tumor samples from various cancer types based on whole-genome/exome sequencing, meaningful mechanistic interpretation of these gene variation results are still very limited. One basic yet challenging task is to distinguish driver mutations, which are causally implicated in cancer development, from passenger mutations, which occur randomly with neutral effect. Another critical task is to map, trace, and interpret the functional impact of drivr mutations within biological networks. In a network context, driver mutations associated with genes within a pathway often show a mutually exclusive pattern, meaning that each patient carries exactly one mutation in the pathway, which is sufficient to perturb the function of that pathway. Another prominent pattern is that driver mutations of genes from several different pathways may co-occur, since perturbation of multiple pathways is required for tumor formation. Screening for mutual exclusivity and co-occurrence patterns can greatly facilitate the identification of novel sets of related driver gene mutations, their associated driver pathways, and functional relationships between these driver pathways. Although several de novo driver mutation gene set discovery methods have been proposed in the past few years, they have major limitations due to computational feasibility, an inability to deal with mutational heterogeneity across patients, and lack of biochemical interpretation. The overall goal of this proposal is to develop and combine advance sequence variation analyses with complementary biological network analyses into a highly novel systems biology approach that will: i) detect sets of related mutations in driver regulatory/signaling pathways, ii) classify these pathways as stimulated, inhibited, or mixed with respect to their role in the tumor development process, and iii) predict direct metabolic outcomes of these perturbed pathways. Our specific aims are: 1) to develop a statistical method for de novo discovery of mutually exclusive and co-occurrent sets of driver mutations; and 2) to develop a pathway mapping and classification method for related sets of driver mutations. The identification and biochemical interpretation of aggregated tumor mutations from driver mutation gene sets to inhibited/stimulated pathways to perturbed biological network will provide new mechanistic insights in tumor progression at a systems level. Also with this information, potential drug targets in the detected driver pathways can be classified as requiring agonist or antagonist drug development, making drug target evaluation and prioritization much more effective. Furthermore, identification of co-occurrence between specific genes and pathways may aid in the development of multi- therapeutic cancer treatments that are optimized to groups of patients showing the same mutational patterns of co-occurrence.
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0.954 |
2021 |
Wang, Chi |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Statistical Methods For Cancer Progression Delineation and Subtype Identification
Project Summary Carcinogenesis is a complex process involving somatic mutations in a number of key biological pathways and processes. Full study of the temporal order of somatic mutation occurrences is very important to understand biological mechanisms of cancer development and to inform new therapeutic targets and treatment options. The first and most recognized example of order of mutations is from colon cancer, which is frequently initiated by mutations that affect the Wnt signaling pathway, and then progress upon subsequent mutations in genes involved in MAPK, PI3K, TGF-beta, and p53 signaling pathways. However, for many other cancer types, temporal orders of mutations are still largely unknown. Somatic mutation profiling via high throughput DNA sequencing has provided an unprecedented opportunity for using statistical/computational methods to study cancer progression. We and others have developed methods to infer temporal order of somatic mutations based on combining mutation profile data from a cohort of patients. However, one major limitation of current methods is that they only consider presence or absence of mutations in a patient?s tumor, but do not take into account intra-tumoral heterogeneity (ITH). The ITH refers to the presence of multiple cell populations, i.e. subclones, with distinct mutation profiles within a patient?s tumor. The ITH, which can be inferred from either single-/multi-region bulk sequencing or single cell sequencing, is usually characterized by a phylogenetic tree with nodes in the tree indicating different subclones and edges indicating the evolutionary relationships of subclones. As a phylogenetic tree describes the temporal order of mutations within an individual patient?s tumor, incorporating such in-depth intra-patient information into the tumor progression analysis across patients is likely to substantially increase the power and accuracy of the analysis. Another important priority in cancer research is to identify molecular subtypes. As cancer is a complex disease, patients of the same cancer type may have very different prognoses and responses to therapy. Further classifying patients into subtypes allows clinicians to better predict a patient?s clinical outcomes and design more personalized treatment strategies. By harnessing omics profiling data, statistical/machine learning has emerged as a powerful tool to identify molecular cancer subtypes. However, due to the high complexity of cancer omics data and limited sample size, it is still challenging to obtain stable and biologically interpretable results. Recently, it has been advocated that incorporating biological knowledge and structure into the construction of statistical/machine learning models is a viable approach to improve the mechanistic interpretability and robustness of the models. To advance current capabilities, we propose to develop new statistical methods to better estimate the temporal order of pathway mutations by integrating ITH, pathway and mutational functional annotation information, and thereby, to classify patients into biologically meaningful subtypes.
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0.954 |