Digital Commons Network^™

Tobacco Smoke Exposure Is A Driver Of Altered Oxidative Stress Response And Immunity In Head And Neck Cancer, Yang Li, Pedram Yadollahi, Fonma N Essien, Vasanta Putluri, Chandra Shekar R Ambati, Karthik Reddy Kami Reddy, Abu Hena Mostafa Kamal, Nagireddy Putluri, Lama M Abdurrahman, Maria E Ruiz Echartea, Keenan J Ernste, Akshar J Trivedi, Jonathan Vazquez-Perez, William H Hudson, William K Decker, Rutulkumar Patel, Abdullah A Osman, Farrah Kheradmand, Stephen Y Lai, Jeffrey N Myers, Heath D Skinner, Cristian Coarfa, Kwangwon Lee, Antrix Jain, Anna Malovannaya, Mitchell J Frederick, Vlad C Sandulache

Faculty, Staff and Student Publications

Background: Exposomes are critical drivers of carcinogenesis. However, how they modulate tumor behavior remains unclear. Extensive clinical data show cigarette smoke to be a key exposome that promotes aggressive tumors, higher rates of metastasis, reduced response to chemoradiotherapy, and suppressed anti-tumor immunity. We sought to determine whether smoke itself can modulate aggressive tumor behavior in head and neck squamous cell carcinoma (HNSCC) through reprogramming of the cellular reductive state.

Methods: Using established human and murine HNSCC cell lines and syngeneic mouse models, we utilized conventional western blotting, steady state and flux metabolomics, RNA sequencing, quantitative proteomics and flow cytometry to …

Alpha-Synuclein Seed Amplification Assays: Data Sharing, Standardization Needed For Clinical Use, Hilal A Lashuel, D James Surmeier, Tanya Simuni, Kalpana Merchant, Byron Caughey, Claudio Soto, Mohamed-Bilal Fares, Roland G Heym, Ronald Melki

Faculty, Staff and Student Publications

Alpha-synuclein seed amplification assays can improve neurodegenerative disease diagnosis and care, but widespread use depends on a framework that standardizes protocols and encourages data sharing.

K-Mer Analysis Of Long-Read Alignment Pileups For Structural Variant Genotyping, Adam C English, Fabio Cunial, Ginger A Metcalf, Richard A Gibbs, Fritz J Sedlazeck

Faculty, Staff and Students Publications

Accurately genotyping structural variant (SV) alleles is crucial to genomics research. We present a novel method (kanpig) for genotyping SVs that leverages variant graphs and k-mer vectors to rapidly generate accurate SV genotypes. Benchmarking against the latest SV datasets shows kanpig achieves a single-sample genotyping concordance of 82.1%, significantly outperforming existing tools, which average 66.3%. We explore kanpig's use for multi-sample projects by testing on 47 genetically diverse samples and find kanpig accurately genotypes complex loci (e.g. SVs neighboring other SVs), and produces higher genotyping concordance than other tools. Kanpig requires only 43 seconds to process a single sample's 20x …

Bright And Photostable Yellow Fluorescent Proteins For Extended Imaging, Jihwan Lee, Shujuan Lai, Shuyuan Yang, Shiqun Zhao, Francisco A Blanco, Anne C Lyons, Raquel Merino-Urteaga, John F Ahrens, Nathan A Nguyen, Haixin Liu, Zhuohe Liu, Gerard G Lambert, Nathan C Shaner, Liangyi Chen, Kimberley F Tolias, Jin Zhang, Taekjip Ha, François St-Pierre

Faculty, Staff and Students Publications

Fluorescent proteins are indispensable molecular tools for visualizing biological structures and processes, but their limited photostability restricts the duration of dynamic imaging experiments. Yellow fluorescent proteins (YFPs), in particular, photobleach rapidly. Here, we introduce mGold2s and mGold2t, YFPs with up to 25-fold greater photostability than mVenus and mCitrine, two commonly used YFPs, while maintaining comparable brightness. These variants were identified using a high-throughput pooled single-cell platform, simultaneously screening for high brightness and photostability. Compared with our previous benchmark, mGold, the mGold2 variants display a ~4-fold increase in photostability without sacrificing brightness. mGold2s and mGold2t extend imaging durations across diverse modalities, …

Normalized And Directional Interplay Scoring For The Interrogation Of Proteoform Data, Karl F Poncha, Alyssa T Paparella, Nicolas L Young

Faculty, Staff and Students Publications

Histone proteoforms, often presenting multiple co-occurring post-translational modifications (PTMs), are central to chromatin regulation and gene expression. A proteoform is a specific form of a protein that includes variations arising from genetic changes, alternative RNA splicing, proteolytic processing, and PTMs. Genome-indexed histone proteoforms define the histone code, influencing cellular phenotype by dictating DNA interacting partners. Understanding the dynamics of histone proteoforms is essential for elucidating chromatin-based regulatory mechanisms. Advances in middle-down and top-down proteomics enable accurate identification and quantitation of thousands of proteoforms in a single run. However, the resulting data complexity presents significant challenges for analysis and visualization. Here, …

Hif Regulates Multiple Translated Endogenous Retroviruses: Implications For Cancer Immunotherapy, Qinqin Jiang, David A Braun, Karl R Clauser, Vijyendra Ramesh, Nitin H Shirole, Joseph E Duke-Cohan, Nancy Nabilsi, Nicholas J Kramer, Cleo Forman, Isabelle E Lippincott, Susan Klaeger, Kshiti M Phulphagar, Vipheaviny Chea, Nawoo Kim, Allison P Vanasse, Eddy Saad, Teagan Parsons, Melissa Carr-Reynolds, Isabel Carulli, Katarina Pinjusic, Yijia Jiang, Rong Li, Sudeepa Syamala, Suzanna Rachimi, Eva K Verzani, Jonathan D Stevens, William J Lane, Sabrina Y Camp, Kevin Meli, Melissa B Pappalardi, Zachary T Herbert, Xintao Qiu, Paloma Cejas, Henry W Long, Sachet A Shukla, Eliezer M Van Allen, Toni K Choueiri, L Stirling Churchman, Jennifer G Abelin, Cagan Gurer, Gavin Macbeath, Richard W Childs, Steven A Carr, Derin B Keskin, Catherine J Wu, William G Kaelin

Faculty, Staff and Student Publications

Clear cell renal cell carcinoma (ccRCC), despite having a low mutational burden, is considered immunogenic because it occasionally undergoes spontaneous regressions and often responds to immunotherapies. The signature lesion in ccRCC is inactivation of the VHL tumor suppressor gene and consequent upregulation of the HIF transcription factor. An earlier case report described a ccRCC patient who was cured by an allogeneic stem cell transplant and later found to have donor-derived T cells that recognized a ccRCC-specific peptide encoded by a HIF-responsive endogenous retrovirus (ERV), ERVE-4. We report that ERVE-4 is one of many ERVs that are induced by HIF, translated …

Breaking The Synaptic Vesicle Cycle: Mechanistic Insights Into Presynaptic Dysfunctions In Epilepsy, Kevin Jiang, Lu-Tang Yang, Mingshan Xue

Duncan NRI Faculty and Staff Publications

Synaptic dysfunction is a hallmark of many neurological disorders including epilepsy. An increasing number of epilepsy-causing pathogenic variants are being identified in genes encoding presynaptic proteins that affect every step of the synaptic vesicle cycle, from vesicle loading, tethering, docking, priming, calcium sensing, fusing, to recycling. These different molecular dysfunctions result in converging impairment of presynaptic neurotransmitter release, yet lead to diverse epileptic disorders. This review focuses on representative monogenic epileptic disorders caused by pathogenic variants of key presynaptic proteins involved in different stages of the synaptic vesicle cycle: SYN1 (vesicle pool regulation), STXBP1 (vesicle docking, priming, and fusion), and …

De Novo Variants In Cdkl1 And Cdkl2 Are Associated With Neurodevelopmental Symptoms, Ali H Bereshneh, Jonathan C Andrews, Daniel F Eberl, Guney Bademci, Nicholas A Borja, Stephanie Bivona, Wendy K Chung, Shinya Yamamoto, Michael F Wangler, Shane Mckee, Mustafa Tekin, Hugo J Bellen, Oguz Kanca

Duncan NRI Faculty and Staff Publications

The CDKL (cyclin-dependent kinase-like) family consists of five members in humans, CDKL1-5, that encode serine-threonine kinases. The only member that has been associated with a Mendelian disorder is CDKL5, and variants in CDKL5 cause developmental and epileptic encephalopathy type 2 (DEE2). Here, we study four de novo variants in CDKL2 identified in five individuals, including three unrelated probands and monozygotic twins. These individuals present with overlapping symptoms, including global developmental delay, intellectual disability, childhood-onset epilepsy, dyspraxia, and speech deficits. We also identified two individuals with de novo missense variants in CDKL1 in the published Deciphering Developmental Disorders (DDD) and GeneDx …

Open Access Dataset And Common Data Model For Pulse Oximeter Performance Data, Nicholas Fong, Michael S Lipnick, Ella Behnke, Yu Chou, Seif Elmankabadi, Lily Ortiz, Christopher S Almond, Isabella Auchus, Garrett W Burnett, Ronald Bisegerwa, Desireé R Conrad, Carolyn M Hendrickson, Shubhada Hooli, Robert Kopotic, Gregory Leeb, Daniel Martin, Eric D Mccollum, Ellis P Monk, Kelvin L Moore, Leonid Shmuylovich, J Brady Scott, An-Kwok Ian Wong, Tianyue Zhou, Romain Pirracchio, Philip E Bickler, John Feiner, Tyler J Law

Faculty, Staff and Students Publications

The OpenOximetry Dataset stores clinical and lab pulse oximetry data. It supports measurements of arterial oxygen saturation (SaO2) by arterial blood gas co-oximetry and pulse oximetry (SpO2), alongside processed and unprocessed photoplethysmography (PPG) data and other metadata. This includes skin color measurements, finger diameter, vital signs (e.g., arterial blood pressure, end-tidal carbon dioxide), and arterial blood gas parameters (e.g., acid-base balance, hemoglobin concentration). All data, from desaturation studies to clinical trials, are collected prospectively to ensure accuracy. A common data model and standardized protocols for consistent archival and interpretation ensure consistent data archival and interpretation. The dataset aims to facilitate …

Clinical Validation Of Rna Sequencing For Mendelian Disorder Diagnostics, Sen Zhao, Kristina Macakova, Jefferson C Sinson, Hongzheng Dai, Jill Rosenfeld, Gladys E Zapata, Shenglan Li, Patricia A Ward, Christiana Wang, Chunjing Qu, Becky Maywald, Brendan Lee, Christine Eng, Pengfei Liu

Faculty, Staff and Students Publications

Despite rapid advancements in clinical sequencing, over half of diagnostic evaluations still lack definitive results. RNA sequencing (RNA-seq) has shown promise in research settings for bridging this gap by providing essential functional data for accurate interpretation of diagnostic sequencing results. However, despite advanced research pipelines, clinical translation of diagnostic RNA-seq has not yet been realized. We have developed and validated a clinical diagnostic RNA-seq test for individuals with suspected genetic disorders who have existing or concurrent comprehensive DNA diagnostic testing. This diagnostic RNA-seq test processes RNA samples from fibroblasts or blood and derives clinical interpretations based on the analytical detection …

Biochemical Profiling And Structural Basis Of Adar1-Mediated Rna Editing, Xiangyu Deng, Lina Sun, Min Zhang, Rashmi Basavaraj, Jin Wang, Yi-Lan Weng, Yang Gao

Faculty, Staff and Students Publications

ADAR1 regulates RNA-induced immune responses by converting adenosine to inosine in double-stranded RNA. Mutations in ADAR1 are associated with human autoimmune disease, and targeting ADAR1 has been proposed for cancer immunotherapy. However, the molecular mechanisms underlying ADAR1-mediated editing remain unclear. Here, we provide detailed biochemical and structural characterizations of human ADAR1. Our biochemical profiling reveals that ADAR1 editing is both sequence and RNA-duplex-length dependent but can well tolerate mismatches near the editing site. High-resolution ADAR1-RNA complex structures, combined with mutagenesis, elucidate RNA binding, substrate selection, dimerization, and the essential role of RNA-binding domain 3. The ADAR1 structures also help explain …

Methylome-Wide Association Analyses Of Lipids And Modifying Effects Of Behavioral Factors In Diverse Race And Ethnicity Participants, Yao Hu, Jeff Haessler, Jessica I Lundin, Burcu F Darst, Eric A Whitsel, Megan Grove, Weihua Guan, Rui Xia, Mindy Szeto, Laura M Raffield, Scott Ratliff, Yuxuan Wang, Xuzhi Wang, Alison E Fohner, Megan T Lynch, Yesha M Patel, S Lani Park, Huichun Xu, Braxton D Mitchell, Joshua C Bis, Nona Sotoodehnia, Jennifer A Brody, Bruce M Psaty, Gina M Peloso, Michael Y Tsai, Stephen S Rich, Jerome I Rotter, Jennifer A Smith, Sharon L R Kardia, Alex P Reiner, Leslie Lange, Myriam Fornage, James S Pankow, Mariaelisa Graff, Kari E North, Charles Kooperberg, Ulrike Peters

Faculty, Staff and Student Publications

Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG …

The Microbiome-Restorative Potential Of Ibezapolstat For The Treatment Of Clostridioides Difficile Infection Is Predicted Through Variant Polc-Type Dna Polymerase Iii In Lachnospiraceae And Oscillospiraceae, Jacob K Mcpherson, Julian G Hurdle, Matthew L Baker, Tahir Hussain, Ashok Kumar, Kevin W Garey

Faculty, Staff and Student Publications

Ibezapolstat (IBZ), a first-in-class antibiotic targeting the PolC-type DNA polymerase III alpha-subunit (PolC) in low G + C bacteria, is in clinical development for the treatment of Clostridioides difficile infection (CDI). In the phase 2 trials, IBZ had potent activity against C. difficile while sparing or causing regrowth of Lachnospiraceae, Oscillospiraceae, and Erysipelotrichales, common commensal low G + C bacteria. The purpose of this study was to utilize in silico approaches to better interpret the narrower than expected IBZ spectrum of activity. IBZ susceptibility to human commensal microbiota was predicted using genomic analysis and PolC phylogenetic tree construction in relation …

Chronic Viral Mimicry Induction Following P53 Loss Promotes Immune Evasion, Charles A Ishak, Sajid A Marhon, Naïri Tchrakian, Anjelica Hodgson, Helen Loo Yau, Isabela M Gonzaga, Melanie Peralta, Ilinca M Lungu, Stephanie Gomez, Sheng-Ben Liang, Shu Yi Shen, Raymond Chen, Jocelyn Chen, Biji Chatterjee, Kevin N Wanniarachchi, Junwoo Lee, Nicholas Zehrbach, Amir Hosseini, Parinaz Mehdipour, Siyu Sun, Alexander Solovyov, Ilias Ettayebi, Kyle E Francis, Aobo He, Taiyi Wu, Shengrui Feng, Tiago Da Silva Medina, Felipe Campos De Almeida, Jane Bayani, Jason Li, Spencer Macdonald, Yadong Wang, Sarah S Garcia, Elisa Arthofer, Noor Diab, Aneil Srivastava, Paul Tran Austin, Peter J B Sabatini, Benjamin D Greenbaum, Catherine A O'Brien, Trevor G Shepherd, Ming Sound Tsao, Katherine B Chiappinelli, Amit M Oza, Blaise A Clarke, Robert Rottapel, Stephanie Lheureux, Daniel D De Carvalho

Faculty, Staff and Student Publications

Epigenetic therapies facilitate transcription of immunogenic repetitive elements that cull cancer cells through ‘viral mimicry’ responses. Paradoxically, cancer-initiating events also facilitate transcription of repetitive elements. Contributions of repetitive element transcription towards cancer initiation, and the mechanisms by which cancer cells evade lethal viral mimicry responses during tumor initiation remain poorly understood. In this report, we characterize premalignant lesions of the fallopian tube along with syngeneic epithelial ovarian cancer models to explore the earliest events of tumorigenesis following loss of the p53 tumor suppressor protein. We report that p53 loss permits transcription of immunogenic repetitive elements and chronic viral mimicry activation …

Computational Sensitivity Evaluation Of Ultrasound Neuromodulation Resolution To Brain Tissue Sound Speed With Robust Beamforming, Boqiang Fan, Wayne Goodman, Sameer A Sheth, Richard R Bouchard, Behnaam Aazhang

Faculty, Staff and Student Publications

Low-intensity focused ultrasound (LIFU) neuromodulation requires precise targeting and high resolution enabled by phased array transducers and beamforming. However, focusing optimization usually relies on phantom measurements or simulations with inaccurate acoustic properties to degrade neuromodulation resolution. Therefore, this work analyzes the sensitivity of neuromodulation resolution, measured by off-target activation area (OTAA), to brain tissue sound speed. A Robust Optimal Resolution (ROR) beamforming method is proposed to minimize the worst-case OTAA with restricted sound speed inaccuracy and propagation information estimated with deviated sound speed. The propagation estimation model utilizes equivalent source method (ESM) to map sound field between different acoustic parameter …

Identifiability And Model Selection Frameworks For Models Of High-Grade Glioma Response To Chemoradiation, Khushi C Hiremath, Kenan Atakishi, Ernesto A B F Lima, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Caroline Chung, Thomas E Yankeelov, David A Hormuth Ii

Faculty, Staff and Student Publications

We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated …

Bone-Induced Her2 Promotes Secondary Metastasis In Hr+/Her2- Breast Cancer, Rahat Alam, Anna Reva, David G Edwards, Bree M Lege, Laura S Munoz-Arcos, Carolina Reduzzi, Swarnima Singh, Xiaoxin Hao, Yi-Hsuan Wu, Zeru Tian, Laura M Natalee, Gargi Damle, Deniz Demircioglu, Yixian Wang, Ling Wu, Elisabetta Molteni, Dan Hasson, Bora Lim, Zbigniew Gugala, Jerry E Chipuk, Julie E Lang, Joseph A Sparano, Chonghui Cheng, Massimo Cristofanilli, Han Xiao, Xiang H-F Zhang, Igor L Bado

Faculty, Staff and Students Publications

Bone metastases can disseminate to secondary sites and promote breast cancer progression creating additional clinical challenges. The mechanisms contributing to secondary metastasis are barely understood. Here, we evaluate the prediction power of Her2-expressing (Her2E) circulating tumor cells (CTCs) after analyzing over 13,000 CTCs from a cohort of 137 metastatic breast cancer (MBC) patients with initial HR+/Her2− status and employ preclinical models of bone metastasis (BM) to validate the role of Her2E CTCs in multi-organ metastases. While Her2 expression was higher in patients with bone metastasis, experimental analyses revealed that Her2E CTCs derived from bone lesions were more dependent on Her2 …

The Structural Inequality Of Status Quo Clinical Communication, Joelle Robertson-Preidler

Faculty and Staff Publications

No abstract provided.

Standardizing Social Determinants Of Health Factors From Heterogeneous Sources To Improve Data Fairness, Yifang Dang

Dissertations and Theses (Open Access)

Social Determinants of Health (SDoH) significantly influence health outcomes, yet their representation in computational models remains fragmented. This dissertation addresses this gap by constructing an SDoH ontology (SDoHO), leveraging it to improve large language model (LLM) performance, and using LLMs to extract new knowledge and enrich the ontology. The overarching goal is to establish a feedback loop where ontology development enhances LLM-based extraction, and LLM-derived insights refine and expand the ontology. The methodology is structured across three aims: (1) ontology construction, (2) ontology-assisted LLM enhancement, and (3) LLM-driven ontology enrichment, with a focus on Alzheimer’s Disease and Related Dementias (ADRD). …

Computer-Aided Integrated Rehabilitation For Post-Stroke Patients Using Deep Learning Techniques, Kaichen Tang

Dissertations and Theses (Open Access)

Stroke is a leading cause of long-term disability, often requiring intensive rehabilitation and frequent clinical assessments such as the Fugl-Meyer Assessment. However, traditional in-person evaluations are resource-heavy and difficult to scale, limiting access for many patients. Additionally, monitoring vital signs like blood pressure and oxygen saturation typically depends on specialized equipment, leading to fragmented care and incomplete recovery insights. Even when assessments and monitoring are available, patients often struggle to maintain high-quality, intensive exercise routines without supervision, further limiting rehabilitation outcomes.

To address these challenges, this dissertation proposes an AI-driven, smartphone-based framework that integrates automated motor function assessment, non-invasive vital …

Leveraging And Advancing The Foundation Models For Clinical Trajectory Analysis Based On Electronic Health Record Data, Jianping He

Dissertations and Theses (Open Access)

Enhancements in foundation models have provided significant potentials for clinical applications. This dissertation leverages and advances foundation models for a range of clinical tasks using electronic health record (EHR) data, demonstrating their applicability in Clinical Temporal Relation Extraction (CTRE), disease prediction, and patients trajectory analysis.

Aim 1 is to efficiently adapt large language models (LLMs) for CTRE in both full data and few-shot settings. This study leveraged four LLMs: GatorTron-Base, GatorTron-Large, LLaMA3.1, and MeLLaMA. The proposed fine-tuning strategies include: (a) standard fine-tuning; (b) hard-prompting; (c) soft-prompting; (d) Low-Rank Adaptation (LoRA). We found that nearly all proposed fine-tuning strategies outperformed existing …

The Relationship Between Physical Activity And Telomere Length In Women: A Systematic Review, Jeni Page, Catherine Stephens, Melissa Richard, Elizabeth Lyons, Elizabeth Baumler, M Terese Verklan, Elizabeth Lorenzo

Faculty, Staff and Student Publications

Telomere length (TL) is a biomarker of cellular aging with variations observed by sex, age, race, and ethnicity. Prior studies have suggested that physical activity (PA) may positively impact TL by potentially elongating telomeres and slowing cellular aging. However, research examining the optimal type and intensity of PA needed to elicit these changes specific to women remains limited. This systematic review aimed to investigate variations in TL in response to PA among women, exploring how these effects differ by age, race, or ethnicity. Following PRISMA guidelines, searches across five databases identified 17 relevant studies published from 2008 to 2022. A …

Computational Methods For Enhancing The Quality And Interoperability Of Biomedical Terminologies, Xubing Hao

Dissertations and Theses (Open Access)

Biomedical ontologies or terminologies not only serve as a part of the metadata standards for describing data in the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable), but also play a vital role in downstream applications such as cohort identification from electronic health records (EHR). However, there are two critical barriers that may lead to ambiguity, complexity, and inaccuracies in such ontology-based downstream applications. The first barrier is the quality of the ontology. Despite efforts by ontology curators to ensure ontology accuracy and comprehensiveness, errors and inconsistencies are unavoidable. The second barrier is the semantic heterogeneity since human experts may use …

Advancing Genetic Association Discovery In Brain Mri Using Unsupervised And Self-Supervised Deep Learning: Exploring Learning Dynamics, Region-Specific Features, And Spatially Resolved Representations, Sheikh Muhammad Saiful Islam

Dissertations and Theses (Open Access)

Deep learning has unlocked significant potential for advancing the discovery of genetic associations in imaging genetics, particularly in brain imaging using T1-weighted Magnetic Resonance Imaging (MRI). Traditional methods for this task often relied on hand-crafted or semi-automated feature extraction, followed by genetic association studies utilizing these features. While effective, these approaches are limited by their lack of data-driven exploration and the generation of features with limited informativeness.

Unsupervised deep learning, in particular, has emerged as a powerful alternative, addressing some of these limitations by enabling automated and data-driven feature discovery. Recent research in this domain has primarily focused on adapting …

Harnessing Knowledge And Data For Clinical Information Extraction In The Era Of Large Language Models, Yan Hu

Dissertations and Theses (Open Access)

The rapid digitization of healthcare records has led to the widespread adoption of Electronic Health Records (EHRs), which contain rich, unstructured clinical notes. These notes hold valuable information for patient care and clinical research, but their complexity and unstructured nature pose significant challenges for effective utilization. Clinical Information Extraction (IE) aims to bridge this gap by transforming unstructured text into structured data, enabling automated analysis. Traditional Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER), have been widely used for clinical IE, but recent advancements in Large Language Models (LLMs) like GPT-3.5, GPT-4, and LLaMA have opened new …

Accelerating Drug Repurposing And Target Discovery Using Deep Learning, Kohong Lin

Dissertations and Theses (Open Access)

Drug discovery is a long-lasting and expensive process. Computational approaches, particularly deep learning techniques, offer the potential to accelerate this process by integrating diverse perspectives from drug discovery theories and capturing intricate patterns within large, multimodal datasets. This dissertation explores deep learning methodologies to accelerate drug repurposing and genetic target discovery. The first aim focuses on integrating multimodal data, including chemical structures, disease genetics, and systems biology, into comprehensive disease knowledge graphs, followed by applying graph neural networks (GNNs) to prioritize repurposable drug candidates. The second aim is to develop a heterogeneous GNN-based approach capable of modelling distinct semantic relationships …

Llm-Ie: A Python Package For Biomedical Generative Information Extraction With Large Language Models, Enshuo Hsu, Kirk Roberts

Faculty, Staff and Student Publications

Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

Materials and methods: The LLM-IE supports named entity recognition, entity attribute extraction, and relation extraction tasks. We benchmarked it on the i2b2 clinical datasets.

Results: The sentence-based prompting algorithm resulted in the best 8-shot performance of over 70% strict F1 for entity extraction and about 60% F1 for entity attribute extraction.

Discussion: We developed a Python package, LLM-IE, …

Environmental Health Disparities In Pediatric Cancer: A Report From The Fourth Symposium On Childhood Cancer Health Disparities, Thanh T Hoang, Zdenko Herceg, Don W Coulter, Adam De Smith, Manish Arora, William E Funk, David Haynes, Stephen H Linder, Leticia M Nogueira, Amy E Hughes, Lindsay A Williams, Jeremy M Schraw, Michael E Scheurer, Philip J Lupo

Faculty, Staff and Student Publications

The 4th Symposium on Childhood Cancer Health Disparities was held at Texas Children's Hospital in Houston, Texas, on September 26, 2023. The symposium registered 94 attendees from different backgrounds (e.g. clinicians, epidemiologists, exposure assessment scientists, geospatial experts) with an interest in environmental health disparities of pediatric cancer susceptibility and treatment outcomes. The focus of the symposium was to provide an overview of the role of environmental risk factors in studies of pediatric cancer, introduce novel exposure assessment tools that can be applied to the field, and highlight opportunities to study the impact of environmental health disparities in pediatric cancer susceptibility …

Rhythm Is Essential: Unraveling The Relation Between The Circadian Clock And Cancer, Olajumoke Ogunlusi, Abantika Ghosh, Mrinmoy Sarkar, Kayla Carter, Harshini Davuluri, Mahul Chakraborty, Kristin Eckel-Mahan, Alex Keene, Jerome S Menet, Deborah Bell-Pedersen, Tapasree Roy Sarkar

The Brown Foundation: Institute of Molecular Medicine

Physiological processes such as the sleep-wake cycle, metabolism, hormone secretion, neurotransmitter release, sensory capabilities, and a variety of behaviors, including sleep, are controlled by a circadian rhythm adapted to 24-hour day-night periodicity. Disruption of circadian rhythm may lead to the risks of numerous diseases, including cancers. Several epidemiological and clinical data reveal a connection between the disruption of circadian rhythms and cancer. On the contrary, oncogenic processes may suppress the homeostatic balance imposed by the circadian clock. The integration of circadian biology into cancer research offers new options for making cancer treatment more effective, and the pharmacological modulation of core …

Epigenome-Wide Association Study Of Incident Type 2 Diabetes In Black And White Participants From The Atherosclerosis Risk In Communities Study, Sowmya Venkataraghavan, James S Pankow, Eric Boerwinkle, Myriam Fornage, Elizabeth Selvin, Debashree Ray

The Brown Foundation: Institute of Molecular Medicine

Aims/hypothesis: DNA methylation studies of incident type 2 diabetes in US populations are limited and to our knowledge none include individuals of African descent. We aimed to fill this gap by identifying methylation sites (CpG sites) and regions likely influencing the development of type 2 diabetes using data from Black and White individuals from the USA.

Methods: We prospectively followed 2091 Black and 1029 White individuals without type 2 diabetes from the Atherosclerosis Risk in Communities study over a median follow-up period of 17 years, and performed an epigenome-wide association analysis of blood-based methylation levels with incident type 2 diabetes …