Data Science Commons^™

Bioprocess Engineering And Intelligent Biomanufacturing, Guan Wang, Yingping Zhuang

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of rapid development of synthetic biology, biomanufacturing, as a bridge between life sciences and engineering technologies, is gradually demonstrating its extraordinary potential to reshape industrial landscapes. However, challenges such as production efficiency, cost control, and process monitoring still hinder the smooth transition from laboratory innovations to industrial-scale implementation. Intelligent biomanufacturing has emerged as a new form of productive force, offering innovative solutions to these problems. This study reviews the latest advances in bioprocess engineering and intelligent biomanufacturing, focusing on three key technological systems: intelligent sensing, intelligent analysis, and intelligent control. Intelligent sensing technology acts as the “eyes” …

Revealing Chronic Disease Progression Patterns Using Gaussian Process For Stage Inference, Yanfei Wang, Weiling Zhao, Angela Ross, Lei You, Hongyu Wang, Xiaobo Zhou

Faculty, Staff and Student Publications

OBJECTIVE: The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.

MATERIALS AND METHODS: We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably …

Development And Validation Of A Nomogram For Predicting Pulmonary Infection In Patients Receiving Immunosuppressive Drugs, Chuxuan Luo, Yue Zhang, Jiajie Zhang, Chen Jin, Xiaolan Ye, Yan Ren, Huajuan Shen, Maosheng Chen, Yiwen Li, Qiang He, Guangbiao Xu, Lina Shao

Faculty, Staff and Student Publications

Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients' prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was …

Expanded T Lymphocytes In The Cerebrospinal Fluid Of Multiple Sclerosis Patients Are Specific For Epstein-Barr-Virus-Infected B Cells, Assaf Gottlieb, H Phuong T Pham, Jerome G Saltarrelli, J William Lindsey

Faculty, Staff and Student Publications

Epstein-Barr virus (EBV) infection has long been associated with multiple sclerosis (MS), but the role of EBV in the pathogenesis of MS is not clear. Our hypothesis is that a major fraction of the expanded clones of T lymphocytes in the cerebrospinal fluid (CSF) are specific for autologous EBV-infected B cells. We obtained blood and CSF samples from eight relapsing-remitting patients in the process of diagnosis. We stimulated cells from the blood with autologous EBV-infected lymphoblastoid cell lines (LCL), EBV, varicella zoster virus, influenza, and candida and sorted the responding cells with flow cytometry after 6 d. We sequenced the …

Qiditangshen Granules Alleviates Diabetic Nephropathy Podocyte Injury: A Network Pharmacology Study And Experimental Validation In Vivo And Vitro, Fei Gao, Ying Zhou, Borui Yu, Huidi Xie, Yang Shi, Xianhui Zhang, Hongfang Liu

Faculty, Staff and Student Publications

BACKGROUND: QiDiTangShen granules (QDTS), a traditional Chinese medicine (TCM) compound prescription, have remarkable efficacy in diabetic nephropathy (DN) patients, and their pharmacological mechanism needs further exploration.

METHODS: According to the active ingredients and targets of the QDTS in the TCMSP database, the network pharmacology of QDTS was investigated. The potential active ingredients were chosen based on the oral bioavailability and the drug similarity index. At the same time, targets for DN-related disease were obtained from GeneCards, OMIM, PharmGKB, TTD, and DrugBank. The TCM-component-target network and the protein-protein interaction (PPI) network were constructed with the Cytoscape and STRING platforms, respectively, and …

Regulation Of Polysaccharide In Wu-Tou Decoction On Intestinal Microflora And Pharmacokinetics Of Small Molecular Compounds In Aia Rats, Di Yang, Xiaoxu Cheng, Meiling Fan, Dong Xie, Zhiqiang Liu, Fei Zheng, Yulin Dai, Zifeng Pi, Hao Yue

Faculty, Staff and Student Publications

Wu-tou decoction (WTD), a traditional Chinese medicine prescription, is used to treat rheumatoid arthritis (RA). It works by controlling intestinal flora and its metabolites, which in turn modulates the inflammatory response and intestinal barrier function. Small molecular compounds (SM) and polysaccharides (PS) were the primary constituents of WTD extract. In this work, a model of adjuvant-induced arthritis (AIA) in rats was established and treated with WTD, SM, and PS, respectively. 16S rRNA gene sequencing was used to examine the regulatory impact of the various groups on the disturbance of the gut flora induced by RA. Further, since PS cannot be …

Prediction Of Hydrogel Degradation Time Based On Central Composite Design, Ying Wang, Deji Liu, Ruiquan Liao, Guangming Zhang, Manlai Zhang, Xiaohui Li

Faculty, Staff and Student Publications

In this study, a self-degrading hydrogel was formed by free-radical-initiated copolymerization, which can be used for oil and gas well strip pressure operations. Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (1H NMR), scanning electron microscopy (SEM), and thermogravimetry-mass spectrometry (TGA-MS) were used to study the reaction mechanism as well as the microstructure of the gels. Then, the effects of the four factors and their interactions on gel degradation time were determined by central composite design (CCD). Then, the effects of copolymer concentration, cross-linker, initiator, and reaction temperature and their interactions on gel degradation time were determined by central composite …

Fusionneoantigen: : A Resource Of Fusion Gene-Specific Neoantigens, Himansu Kumar, Ruihan Luo, Jianguo Wen, Chengyuan Yang, Xiaobo Zhou, Pora Kim

Faculty, Staff and Student Publications

Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels) and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutation and are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on ∼43K fusion proteins of ∼16K in-frame …

Drmref: Comprehensive Reference Map Of Drug Resistance Mechanisms In Human Cancer, Xiaona Liu, Jiahao Yi, Tina Li, Jianguo Wen, Kexin Huang, Jiajia Liu, Grant Wang, Pora Kim, Qianqian Song, Xiaobo Zhou

Faculty, Staff and Student Publications

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in …

Cov2var, A Function Annotation Database Of Sars-Cov-2 Genetic Variation, Yuzhou Feng, Jiahao Yi, Lin Yang, Yanfei Wang, Jianguo Wen, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species …

Ageannomo: A Knowledgebase Of Multi-Omics Annotation For Animal Aging, Kexin Huang, Xi Liu, Zhaocan Zhang, Tiangang Wang, Haixia Xu, Qingxuan Li, Yuhao Jia, Liyu Huang, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes …

Stemdriver: A Knowledgebase Of Gene Functions For Hematopoietic Stem Cell Fate Determination, Yangyang Luo, Jingjing Guo, Jianguo Wen, Weiling Zhao, Kexin Huang, Yang Liu, Grant Wang, Ruihan Luo, Ting Niu, Yuzhou Feng, Haixia Xu, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order …

Fusionpdb:: A Knowledgebase Of Human Fusion Proteins, Himansu Kumar, Lin-Ya Tang, Chengyuan Yang, Pora Kim

Faculty, Staff and Student Publications

Tumorigenic functions due to the formation of fusion genes have been targeted for cancer therapeutics (i.e. kinase inhibitors). However, many fusion proteins involved in various cellular processes have not been studied for targeted therapeutics. This is because the lack of complete fusion protein sequences and their whole 3D structures has made it challenging to develop new therapeutic strategies. To fill these critical gaps, we developed a computational pipeline and a resource of human fusion proteins named FusionPDB, available at https://compbio.uth.edu/FusionPDB. FusionPDB is organized into four levels: 43K fusion protein sequences (14.7K in-frame fusion genes, Level 1), over 2300 + 1267 …

Inpatient Costs Of Treating Patients With Covid-19, Kandice A Kapinos, Richard M Peters, Robert E Murphy, Samuel F Hohmann, Ankita Podichetty, Raymond S Greenberg

Faculty, Staff and Student Publications

IMPORTANCE: With more than 6.2 million hospitalizations due to COVID-19 in the US, recognition of the average hospital costs to provide inpatient care during the pandemic is necessary to understanding the national medical resource use and improving public health readiness and related policies.

OBJECTIVE: To examine the mean cost to provide inpatient care to treat COVID-19 and how it varied through the pandemic waves and by important sociodemographic patient characteristics.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used inpatient-level data from March 1, 2020, to March 31, 2022, extracted from a repository of clinical, administrative, and financial information covering 97% …

When Brain Meets Artificial Intelligence, Lu Zhang

Computer Science and Engineering Dissertations - Archive

When we review the history of development of artificial intelligence (AI), we will find that brain science plays a pivotal role in fostering breakthroughs in AI, such as artificial neural networks (ANNs). Today, AI has made remarkable strides, particularly with the emergence of large language models (LLMs), surpassing expectations and achieving human-level performance in certain tasks. Nonetheless, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI, promoting their mutual enhancement and collaborations. This involve establishing connections from brain science to AI (brain-inspired AI), and reversely, from AI to …

Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta

Theses and Dissertations--Computer Science

End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Erratum: Toward Standardization, Harmonization, And Integration Of Social Determinants Of Health Data: A Texas Clinical And Translational Science Award Institutions Collaboration - Corrigendum, Catherine K Craven, Linda Highfield, Mujeeb Basit, Elmer V Bernstam, Byeong Yeob Choi, Robert L Ferrer, Jonathan A Gelfond, Sandi L Pruitt, Vaishnavi Kannan, Paula K Shireman, Heidi Spratt, Kayla J Torres Morales, Chen-Pin Wang, Zhan Wang, Meredith N Zozus, Edward C Sankary, Susanne Schmidt

Faculty, Staff and Student Publications

This corrects the article "Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration" in volume 8, e17.

Predicting Endothelium-Dependent Diastolic Function (Fmd)And Its Correlation With The Degree Of Coronary Artery Disease (Cad) And Plaque Vulnerability For Cardiovascular Events, Guangming Zhang, Jing Yang, Hanghang Xing, Hongning Yin, Guoqing Gu

Faculty, Staff and Student Publications

OBJECTIVE: This study aims to investigate the correlation between vascular endothelium-dependent diastolic function (FMD) and the degree of coronary artery disease (CAD), plaque vulnerability, and its predictive value for cardiovascular events.

METHODS: Initially, patients (n=100) who were admitted from January 2020 to January 2021 and intended to undergo percutaneous coronary intervention (PCI) were selected. Further, FMD in all patients was determined before the procedure and divided into a high-FMD group (≥4.2%) and a low-FMD group (

RESULTS: No significant differences were observed concerning general information, number of coronary arteries-associated branches, lesion type, involvement of the left main stem (LM), the …

An Automated Approach For Identifying Erroneous Is-A Relations In Snomed Ct, Ran Hu, Jay Shi, Licong Cui, Rashmie Abeysinghe

Faculty, Staff and Student Publications

SNOMED CT is the most comprehensive clinical terminology employed worldwide and enhancing its accuracy is of utmost importance. In this work, we introduce an automated approach to identifying erroneous IS-A relations in SNOMED CT. We first extract linked concept-pairs from which we generate Term Difference Pairs (TDPs) that contain differences between the concepts. Given a TDP, if the reversed TDP also exists and the number of linked-pairs generating this TDP is less than those generating the reversed TDP, then we suggest the former linked-pairs as potentially erroneous IS-A relations. We applied this approach to the Clinical finding and Procedure subhierarchies …

Xuebijing Improves Intestinal Microcirculation Dysfunction In Septic Rats By Regulating The Vegf-A/Pi3k/Akt Signaling Pathway, A-Ling Tang, Yan Li, Li-Chao Sun, Xiao-Yu Liu, Nan Gao, Sheng-Tao Yan, Guo-Qiang Zhang

Faculty, Staff and Student Publications

BACKGROUND: This study aims to explore whether Xuebijing (XBJ) can improve intestinal microcirculation dysfunction in sepsis and its mechanism.

METHODS: A rat model of sepsis was established by cecal ligation and puncture (CLP). A total of 30 male SD rats were divided into four groups: sham group, CLP group, XBJ + axitinib group, and XBJ group. XBJ was intraperitoneally injected 2 h before CLP. Hemodynamic data (blood pressure and heart rate) were recorded. The intestinal microcirculation data of the rats were analyzed via microcirculation imaging. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect the serum levels of interleukin-6 (IL-6), …

A Transformer-Based Deep Learning Approach For Fairly Predicting Post-Liver Transplant Risk Factors, Can Li, Xiaoqian Jiang, Kai Zhang

Faculty, Staff and Student Publications

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, …

Effect Of S-Ketamine On Postoperative Nausea And Vomiting In Patients Undergoing Video-Assisted Thoracic Surgery: A Randomized Controlled Trial, Yu Qi, Meiyan Zhou, Wenting Zheng, Yaqi Dong, Weihua Li, Long Wang, Haijun Xu, Miao Zhang, Dunpeng Yang, Liwei Wang, Hai Zhou

Faculty, Staff and Student Publications

PURPOSE: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy.

PATIENTS AND METHODS: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine …

Disentangling Accelerated Cognitive Decline From The Normal Aging Process And Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach, Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao, Alzheimer’S Disease Neuroimaging Initiative

Faculty, Staff and Student Publications

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

METHODS: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. …

Preparing Healthcare Education For An Ai-Augmented Future, Jiajie Zhang, Susan H Fenton

Faculty, Staff and Student Publications

Artificial intelligence (AI) fundamentally transforms healthcare education as a knowledge enterprise, creating a distributed cognitive system composed of the human brain, which remains relatively unchanged, and AI-based knowledge and cognitive functions, which have accelerated exponentially in scale and power. Education must focus on developing skills to collaborate with AI and on achieving outcomes like problems solved and discoveries made. Curriculum and education policies also need to adapt to this transformation.

Statins Are Rarely Prescribed For Incidentally Discovered Covert Cerebrovascular Disease: A Retrospective Cohort In A Large Electronic Health Record (Ehr) Identified Using Natural Language Processing, Lester Y Leung, Eric Puttock, David F Kallmes, Patrick Luetmer, Sunyang Fu, Chengyi X Zheng, Hongfang Liu, Wansu Chen, David M Kent

Faculty, Staff and Student Publications

Introduction: While incidentally discovered covert cerebrovascular diseases (id-CCD) are associated with future stroke, it is not known if patients with id-CCD are prescribed statins.

Methods: Patients age ≥50 with id-CCD on neuroimaging from 2009 to 2019 with no prior ischaemic stroke, transient ischaemic attack or dementia were identified using natural language processing in a large real-world cohort. Robust Poisson multivariable regression was used to assess statin prescription among patients without prior statins.

Results: Among 2 41 050 patients, 74 975 patients (31.1%; 4.7% with covert brain infarcts (CBI); 29.0% with white matter disease (WMD)) had id-CCD. 53.5% (95% CI 53.2 …

The Enact Network Is Acting On Housing Instability And The Unhoused Using The Open Health Natural Language Processing Toolkit, Daniel R Harris, Sunyang Fu, Andrew Wen, Alexandria Corbeau, Darren Henderson, Jordan Hilsman, David Oniani, Yanshan Wang

Faculty, Staff and Student Publications

Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), …

Descriptor:Benchmarking Secure Neural Network Evaluation Methods For Protein Sequence Classification (Idash24), Arif Harmanci, Luyao Chen, Miran Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations For Targeted Therapy Through Efficient Ngs Data Filtering System, Jinlian Wang, Hui Li, Hongfang Liu

Faculty, Staff and Student Publications

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a …

Sequencing Conversational Turns In Peer Interactions: An Integrated Approach For Evidence-Based Conversational Agent For Just-In-Time Nicotine Cravings Intervention, Tavleen Singh, Michael Truong, Kirk Roberts, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Risky health behaviors place an enormous toll on public health systems. While relapse prevention support is integrated with most behavior modification programs, the results are suboptimal. Recent advances in artificial intelligence (AI) applications provide us with unique opportunities to develop just-in-time adaptive behavior change solutions.

METHODS: In this study, we present an innovative framework, grounded in behavioral theory, and enhanced with social media sequencing and communications scenario builder to architect a conversational agent (CA) specialized in the prevention of relapses in the context of tobacco cessation. We modeled peer interaction data (n = 1000) using the taxonomy of behavior …