Browsing by Author "Wu, Huanmei"
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Item A discovery-based proteomics approach identifies protein disulphide isomerase (PDIA1) as a biomarker of β cell stress in type 1 diabetes(Elsevier, 2023) Syed, Farooq; Singhal, Divya; Raedschelders, Koen; Krishnan, Preethi; Bone, Robert N.; McLaughlin, Madeline R.; Van Eyk, Jennifer E.; Mirmira, Raghavendra G.; Yang, Mei-Ling; Mamula, Mark J.; Wu, Huanmei; Liu, Xiaowen; Evans-Molina, Carmella; Pediatrics, School of MedicineBackground: Stress responses within the β cell have been linked with both increased β cell death and accelerated immune activation in type 1 diabetes (T1D). At present, information on the timing and scope of these responses as well as disease-related changes in islet β cell protein expression during T1D development is lacking. Methods: Data independent acquisition-mass spectrometry was performed on islets collected longitudinally from NOD mice and NOD-SCID mice rendered diabetic through T cell adoptive transfer. Findings: In islets collected from female NOD mice at 10, 12, and 14 weeks of age, we found a time-restricted upregulation of proteins involved in stress mitigation and maintenance of β cell function, followed by loss of expression of protective proteins that heralded diabetes onset. EIF2 signalling and the unfolded protein response, mTOR signalling, mitochondrial function, and oxidative phosphorylation were commonly modulated pathways in both NOD mice and NOD-SCID mice rendered acutely diabetic by T cell adoptive transfer. Protein disulphide isomerase A1 (PDIA1) was upregulated in NOD islets and pancreatic sections from human organ donors with autoantibody positivity or T1D. Moreover, PDIA1 plasma levels were increased in pre-diabetic NOD mice and in the serum of children with recent-onset T1D compared to non-diabetic controls. Interpretation: We identified a core set of modulated pathways across distinct mouse models of T1D and identified PDIA1 as a potential human biomarker of β cell stress in T1D.Item Analyzing Chlamydia and Gonorrhea Health Disparities from Health Information Systems: A Closer Examination Using Spatial Statistics and Geographical Information Systems(2022-05) Lai, Patrick T. S.; Jones, Josette; Dixon, Brian E.; Wilson, Jeffrey; Wu, Huanmei; Shih, PatrickThe emergence and development of electronic health records have contributed to an abundance of patient data that can greatly be used and analyzed to promote health outcomes and even eliminate health disparities. However, challenges exist in the data received with factors such as data inconsistencies, accuracy issues, and unstructured formatting being evident. Furthermore, the current electronic health records and clinical information systems that are present do not contain the social determinants of health that may enhance our understanding of the characteristics and mechanisms of disease risk and transmission as well as health disparities research. Linkage to external population health databases to incorporate these social determinants of health is often necessary. This study provides an opportunity to identify and analyze health disparities using geographical information systems on two important sexually transmitted diseases in chlamydia and gonorrhea using Marion County, Indiana as the geographical location of interest. Population health data from the Social Assets and Vulnerabilities Indicators community information system and electronic health record data from the Indiana Network for Patient Care will be merged to measure the distribution and variability of greatest chlamydia and gonorrhea risk and to determine where the greatest areas of health disparities exist. A series of both statistical and spatial statistical methods such as a longitudinal measurement of health disparity through the Gini index, a hot-spot and cluster analysis, and a geographically weighted regression will be conducted in this study. The outcome and broader impact of this research will contribute to enhanced surveillance and increased effective strategies in identifying the level of health disparities for sexually transmitted diseases in vulnerable localities and high-risk communities. Additionally, the findings from this study will lead to improved standardization and accuracy in data collection to facilitate subsequent studies involving multiple disparate data sources. Finally, this study will likely introduce ideas for potential social determinants of health to be incorporated into electronic health records and clinical information systems.Item AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization(IEEE, 2020-10) Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei; BioHealth Informatics, School of Informatics and ComputingIn this paper, we propose a novel, privacy-preserving, and integrated authentication and authorization scheme (dubbed as AuthN-AuthZ). The proposed scheme can address both the usability and privacy issues often posed by authentication through use of privacy-preserving Biometric-Capsule-based authentication. Each Biometric-Capsule encapsulates a user's biometric template as well as their role within a hierarchical Role-based Access Control model. As a result, AuthN-AuthZ provides novel efficiency by performing both authentication and authorization simultaneously in a single operation. To the best of our knowledge, our scheme's integrated AuthN-AuthZ operation is the first of its kind. The proposed scheme is flexible in design and allows for the secure use of robust deep learning techniques, such as the recently proposed and current state-of-the-art facial feature representation method, ArcFace. We conduct extensive experiments to demonstrate the robust performance of the proposed scheme and its AuthN-AuthZ operation.Item A big data augmented analytics platform to operationalize efficiencies at community clinics(2016-04-15) Kunjan, Kislaya; Jones, Josette F.; Toscos, Tammy; Wu, Huanmei; Holden, RichardCommunity Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowledge gaps in the literature and identify areas for future research in health informatics. The first RQ seeks to understand the information needs to support operations at CHCs and implement an information architecture to support those needs. The second RQ leverages the implemented data infrastructure to evaluate how advanced analytics can guide open access scheduling – a specific use case of this research. Finally, the third RQ seeks to understand how the data can be visualized to support decision making among varying roles in CHCs. Based on the unique work and information flow needs uncovered at these CHCs, an end to-end analytics solution was designed, developed and validated within the framework of a rapid learning health system. The solution comprised of a novel heterogeneous longitudinal clinic data warehouse augmented with big data technologies and dashboard visualizations to inform CHCs regarding operational priorities and to support engagement in the systems redesign initiative. Application of predictive analytics on the health center data guided the implementation of open access scheduling and up to a 15% reduction in the missed appointment rates. Performance measures of importance to specific job profiles within the CHCs were uncovered. This was followed by a user-centered design of an online interactive dashboard to support rapid assessments of care delivery. The impact of the dashboard was assessed over time and formally validated through a usability study involving cognitive task analysis and a system usability scale questionnaire. Wider scale implementation of the data aggregation and analytics platform through regional health information networks could better support a range of health system redesign initiatives in order to address the national ‘triple aim’ of healthcare.Item Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders(2022-05) Chasioti, Danai; Yan, Jingwen; Saykin, Andrew J.; Nho, Kwangsik; Risacher, Shannon L.; Wu, HuanmeiDetermining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.Item Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions(2019-07) Binkheder, Samar Hussein; Jones, Josette; Li, Lang; Quinney, Sara Kay; Wu, Huanmei; Zhang, ChiPhenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement.Item Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA(PLOS, 2019-01-17) Kechavarzi, Bobak D.; Wu, Huanmei; Doman, Thompson N.; Biohealth Informatics, School of Informatics and ComputingThe massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for the extensive aneuploidy observed in TCGA breast cancer samples. We do identify 11 genes that show strong correlation across all features (DNA/mRNA/protein) analogous to that of the well-known oncogene HER2 (ERBB2). These genes are generally less well-characterized regarding their role in cancer and we advocate their further study. We also discover that shRNA knockdown of these genes has an impact on cancer cell growth, suggesting a vulnerability that could be used for cancer therapy. Our study shows the advantages of systematic big data methodologies and also provides future research directions.Item Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA(PLOS, 2019-01-17) Kechavarzi, Bobak D.; Wu, Huanmei; Doman, Thompson N.; Radiation Oncology, School of MedicineThe massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for the extensive aneuploidy observed in TCGA breast cancer samples. We do identify 11 genes that show strong correlation across all features (DNA/mRNA/protein) analogous to that of the well-known oncogene HER2 (ERBB2). These genes are generally less well-characterized regarding their role in cancer and we advocate their further study. We also discover that shRNA knockdown of these genes has an impact on cancer cell growth, suggesting a vulnerability that could be used for cancer therapy. Our study shows the advantages of systematic big data methodologies and also provides future research directions.Item CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional dataLiu, Jiannan; Dong, Chuanpeng; Liu, Yunlong; Wu, HuanmeiHigh throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data.Item CGPE: an integrated online server for Cancer Gene and Pathway Exploration(Oxford University Press, 2021) Liu, Jiannan; Dong, Chuanpeng; Liu, Yunlong; Wu, Huanmei; BioHealth Informatics, School of Informatics and ComputingSummary: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data.