Data Science Commons^™

Pancancer Analysis Of A Potential Gene Mutation Model In The Prediction Of Immunotherapy Outcomes, Lishan Yu, Caifeng Gong

Faculty, Staff and Student Publications

Background: Immune checkpoint blockade (ICB) represents a promising treatment for cancer, but predictive biomarkers are needed. We aimed to develop a cost-effective signature to predict immunotherapy benefits across cancers.

Methods: We proposed a study framework to construct the signature. Specifically, we built a multivariate Cox proportional hazards regression model with LASSO using 80% of an ICB-treated cohort (n = 1661) from MSKCC. The desired signature named SIGP was the risk score of the model and was validated in the remaining 20% of patients and an external ICB-treated cohort (n = 249) from DFCI.

Results: SIGP was based on …

Controlling Multiple Covid-19 Epidemic Waves: An Insight From A Multi-Scale Model Linking The Behaviour Change Dynamics To The Disease Transmission Dynamics, Biao Tang, Weike Zhou, Xia Wang, Hulin Wu, Yanni Xiao

Faculty, Staff and Student Publications

COVID-19 epidemics exhibited multiple waves regionally and globally since 2020. It is important to understand the insight and underlying mechanisms of the multiple waves of COVID-19 epidemics in order to design more efficient non-pharmaceutical interventions (NPIs) and vaccination strategies to prevent future waves. We propose a multi-scale model by linking the behaviour change dynamics to the disease transmission dynamics to investigate the effect of behaviour dynamics on COVID-19 epidemics using game theory. The proposed multi-scale models are calibrated and key parameters related to disease transmission dynamics and behavioural dynamics with/without vaccination are estimated based on COVID-19 epidemic data (daily reported …

Immuno-Genomic Profiling Of Biopsy Specimens Predicts Neoadjuvant Chemotherapy Response In Esophageal Squamous Cell Carcinoma, Shota Sasagawa, Hiroaki Kato, Koji Nagaoka, Changbo Sun, Motohiro Imano, Takao Sato, Todd A Johnson, Masashi Fujita, Kazuhiro Maejima, Yuki Okawa, Kazuhiro Kakimi, Takushi Yasuda, Hidewaki Nakagawa

Faculty, Staff and Student Publications

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers and is primarily treated with platinum-based neoadjuvant chemotherapy (NAC). Some ESCCs respond well to NAC. However, biomarkers to predict NAC sensitivity and their response mechanism in ESCC remain unclear. We perform whole-genome sequencing and RNA sequencing analysis of 141 ESCC biopsy specimens before NAC treatment to generate a machine-learning-based diagnostic model to predict NAC reactivity in ESCC and analyzed the association between immunogenomic features and NAC response. Neutrophil infiltration may play an important role in ESCC response to NAC. We also demonstrate that specific copy-number alterations and copy-number …

Measuring And Controlling Medical Record Abstraction (Mra) Error Rates In An Observational Study, Maryam Y Garza, Tremaine Williams, Sahiti Myneni, Susan H Fenton, Songthip Ounpraseuth, Zhuopei Hu, Jeannette Lee, Jessica Snowden, Meredith N Zozus, Anita C Walden, Alan E Simon, Barbara Mcclaskey, Sarah G Sanders, Sandra S Beauman, Sara R Ford, Lacy Malloch, Amy Wilson, Lori A Devlin, Leslie W Young

Faculty, Staff and Student Publications

BACKGROUND: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.

METHODS: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal …

Secure Human Action Recognition By Encrypted Neural Network Inference, Miran Kim, Xiaoqian Jiang, Kristin Lauter, Elkhan Ismayilzada, Shayan Shams

Faculty, Staff and Student Publications

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average …

Immunotherapy For Type 1 Diabetes Mellitus By Adjuvant-Free Schistosoma Japonicum-Egg Tip-Loaded Asymmetric Microneedle Patch (Stamp), Haoming Huang, Dian Hu, Zhuo Chen, Jiarong Xu, Rengui Xu, Yusheng Gong, Zhengming Fang, Ting Wang, Wei Chen

Faculty, Staff and Student Publications

BACKGROUND: Type 1 diabetes mellitus (T1DM) is an autoimmune disease mediated by autoreactive T cells and dominated by Th1 response polarization. Insulin replacement therapy faces great challenges to this autoimmune disease, requiring highly frequent daily administration. Intriguingly, the progression of T1DM has proven to be prevented or attenuated by helminth infection or worm antigens for a relatively long term. However, the inevitable problems of low safety and poor compliance arise from infection with live worms or direct injection of antigens. Microneedles would be a promising candidate for local delivery of intact antigens, thus providing an opportunity for the clinical immunotherapy …

Building Consensus For A Shared Definition Of Adverse Events: A Case Study In The Profession Of Dentistry, Amy Franklin, Elsbeth Kalenderian, Nutan Hebballi, Veronique Delattre, Jini Etoule, Joel White, Ram Vaderhobli, Denice Stewart, Karla Kent, Alfa Yansane, Muhammad Walji

Faculty, Staff and Student Publications

BACKGROUND: To achieve high-quality health care, adverse events (AEs) must be proactively recognized and mitigated. However, there is often ambiguity in applying guidelines and definitions. We describe the iterative calibration process needed to achieve a shared definition of AEs in dentistry. Our alignment process includes both independent and consensus building approaches.

OBJECTIVE: We explore the process of defining dental AEs and the steps necessary to achieve alignment across different care providers.

METHODS: Teams from 4 dental institutions across the United States iteratively reviewed patient records after identification of charts using an automated trigger tool. Calibration across teams was supported through …

Charting The Proteome Landscape In Major Psychiatric Disorders: From Biomarkers To Biological Pathways Towards Drug Discovery, Brisa S Fernandes, Yulin Dai, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying biomarkers are urgent needs for developing objective diagnostic tests and new therapeutics. In this study, we performed a systematic review and then extracted, curated, and analyzed proteomics data from published studies, aiming to assess the proteome in peripheral blood of individuals with SZ, BD, or MDD. Then, we performed pathway and network analyses to illuminate the biological themes concatenated by the differentially expressed proteins by systematically …

Development Of A Quality Improvement Dental Chart Review Training Program, Elsbeth Kalenderian, Nutan B Hebballi, Amy Franklin, Alfa Yansane, Ana M Ibarra Noriega, Joel White, Muhammad F Walji

Faculty, Staff and Student Publications

INTRODUCTION: Chart review is central to understanding adverse events (AEs) in medicine. In this article, we describe the process and results of educating chart reviewers assigned to evaluate dental AEs.

METHODS: We developed a Web-based training program, "Dental Patient Safety Training," which uses both independent and consensus-based curricula, for identifying AEs recorded in electronic health records in the dental setting. Training included (1) didactic education, (2) skills training using videos and guided walkthroughs, (3) quizzes with feedback, and (4) hands-on learning exercises. In addition, novice reviewers were coached weekly during consensus review discussions. TeamExpert was composed of 2 experienced reviewers, …

Knowledge Representation And Management: Notable Contributions In 2021, Licong Cui, Ferdinand Dhombres, Jean Charlet

Faculty, Staff and Student Publications

OBJECTIVES: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021.

METHODS: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries.

RESULTS: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, …

Real-World Matching Performance Of Deidentified Record-Linking Tokens, Elmer V Bernstam, Reuben Joseph Applegate, Alvin Yu, Deepa Chaudhari, Tian Liu, Alex Coda, Jonah Leshin

Faculty, Staff and Student Publications

OBJECTIVE: Our objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions.

METHODS: This study compares tokens alone and token-based matching algorithms against manual annotation for 20,002 record pairs extracted from the University of Texas Houston's clinical data warehouse (CDW) in terms of entity resolution.

RESULTS: The highest precision achieved was 99.9% with a token derived from the first name, last name, gender, and date-of-birth. The highest recall achieved was 95.5% with an algorithm involving tokens that reflected combinations of first name, last name, gender, date-of-birth, and social security number.

DISCUSSION: To protect …

A Method For Bridging Population-Specific Genotypes To Detect Gene Modules Associated With Alzheimer's Disease, Yulin Dai, Peilin Jia, Zhongming Zhao, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.

METHODS: Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction …

Toward A Standard Formal Semantic Representation Of The Model Card Report, Muhammad Tuan Amith, Licong Cui, Degui Zhi, Kirk Roberts, Xiaoqian Jiang, Fang Li, Evan Yu, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of …

Toward A Standard Formal Semantic Representation Of The Model Card Report, Muhammad Tuan Amith, Licong Cui, Degui Zhi, Kirk Roberts, Xiaoqian Jiang, Fang Li, Evan Yu, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of …

Aligning The American Health Information Management Association Entry-Level Curricula Competencies And Career Map With Industry Job Postings: Cross-Sectional Study, Susan H Fenton, David T Marc, Angela Kennedy, Debra Hamada, Robert Hoyt, Karima Lalani, Connie Renda, Rebecca B Reynolds

Faculty, Staff and Student Publications

BACKGROUND: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands.

OBJECTIVE: The primary aim of this study is to analyze HIM job postings on …

Assessment Of Electronic Health Record For Cancer Research And Patient Care Through A Scoping Review Of Cancer Natural Language Processing, Liwei Wang, Sunyang Fu, Andrew Wen, Xiaoyang Ruan, Huan He, Sijia Liu, Sungrim Moon, Michelle Mai, Irbaz B Riaz, Nan Wang, Ping Yang, Hua Xu, Jeremy L Warner, Hongfang Liu

Faculty, Staff and Student Publications

Purpose: The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.

Methods: Published literature studies were searched …

Mobile Health Applications For Postpartum Depression Management: A Theory-Informed Analysis Of Change-Use-Engagement (Cue) Criteria In The Digital Environment, Alexandra Zingg, Laura Carter, Deevakar Rogith, Sudhakar Selvaraj, Amy Franklin, Sahiti Myneni

Faculty, Staff and Student Publications

Postpartum Depression (PPD) is the most common childbirth complication, with approximately 15% of postpartum women experiencing depression symptoms. Mobile applications have potential to expand delivery of mental health interventions. However, our understanding of how these tools engage women with PPD and facilitate positive behavioral changes is limited. In our paper, we analyze 15 commercial PPD applications to understand their role as facilitators of change, engagement, and sustained use. Applications reviewed contained an average of four theory-based behavioral change techniques, and highest patient engagement level reached was to empower patients through patient-generated data. Heuristic violations were identified in areas including user …

A Multi-Task Gaussian Process Self-Attention Neural Network For Real-Time Prediction Of The Need For Mechanical Ventilators In Covid-19 Patients, Kai Zhang, Siddharth Karanth, Bela Patel, Robert Murphy, Xiaoqian Jiang

Faculty, Staff and Student Publications

OBJECTIVE: The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality.

METHODS: We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task …

External Validation Of A Laboratory Prediction Algorithm For The Reduction Of Unnecessary Labs In The Critical Care Setting, Linda T Li, Tongtong Huang, Elmer V Bernstam, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: Unnecessary laboratory tests contribute to iatrogenic harm and are a major source of waste in the health care system. We previously developed a machine learning algorithm to help clinicians identify unnecessary laboratory tests, but it has not been externally validated. In this study, we externally validate our machine learning algorithm.

METHODS: To externally validate the machine learning algorithm that was originally trained on the Medical Information Mart for Intensive Care (MIMIC) III database, we tested the algorithm in a separate institution. We identified and abstracted data for all patients older than 18 years admitted to the intensive care unit …

Tissue-Specific Variations In Transcription Factors Elucidate Complex Immune System Regulation, Hengwei Lu, Yi-Ching Tang, Assaf Gottlieb

Faculty, Staff and Student Publications

Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed "transcriptome imputation" are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models …

Cardiovascular Disease Prevention Education Using A Virtual Environment In Sexual-Minority Men Of Color With Hiv: Protocol For A Sequential, Mixed Method, Waitlist Randomized Controlled Trial, S Raquel Ramos, Constance Johnson, Gail Melkus, Trace Kershaw, Marya Gwadz, Harmony Reynolds, Allison Vorderstrasse

Faculty, Staff and Student Publications

Background: It is estimated that 70% of all deaths each year in the United States are due to chronic conditions. Cardiovascular disease (CVD), a chronic condition, is the leading cause of death in ethnic and racial minority males. It has been identified as the second most common cause of death in persons with HIV. By the year 2030, it is estimated that 78% of persons with HIV will be diagnosed with CVD.

Objective: We propose the first technology-based virtual environment intervention to address behavioral, modifiable risk factors associated with cardiovascular and metabolic comorbidities in sexual-minority men of color with HIV. …

An Observational Retrospective Study Of Adverse Events And Behavioral Outcomes During Pediatric Dental Sedation, Kawtar Zouaidi, Gregory Olson, Helen H Lee, Elsbeth Kalenderian, Muhammad F Walji

Faculty, Staff and Student Publications

Purpose: The purpose of this study was to examine a university-based dental electronic health records (EHR) database to identify sedation-related adverse events (AEs) and assess patients' behavioral outcomes during routine pediatric dental sedations (PDSs) in a dental school clinic.

Methods: A database was screened for patients younger than 18 years old who had received dental sedation in 2019. The qualifying EHRs were then accessed and sedations were reviewed for AEs, which were categorized using a 12-point classification system and the Tracking and Reporting Outcomes of Procedural Sedation Tool. Patient behaviors were assessed using provider progress notes and categorized as presence/ …

Relational Graph Convolutional Networks For Predicting Blood-Brain Barrier Penetration Of Drug Molecules, Yan Ding, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

MOTIVATION: Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional …

An Evidence-Based Lexical Pattern Approach For Quality Assurance Of Gene Ontology Relations, Rashmie Abeysinghe, Yuntao Yang, Mason Bartels, W Jim Zheng, Licong Cui

Faculty, Staff and Student Publications

Gene Ontology (GO) is widely used in the biological domain. It is the most comprehensive ontology providing formal representation of gene functions (GO concepts) and relations between them. However, unintentional quality defects (e.g. missing or erroneous relations) in GO may exist due to the large size of GO concepts and complexity of GO structures. Such quality defects would impact the results of GO-based analyses and applications. In this work, we introduce a novel evidence-based lexical pattern approach for quality assurance of GO relations. We leverage two layers of evidence to suggest potentially missing relations in GO as follows. We first …

Factors Associated With Covid-19 Death In The United States: Cohort Study, Uan-I Chen, Hua Xu, Trudy Millard Krause, Raymond Greenberg, Xiao Dong, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making.

OBJECTIVE: This study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set.

METHODS: A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths …

Prioritization Of Risk Genes In Multiple Sclerosis By A Refined Bayesian Framework Followed By Tissue-Specificity And Cell Type Feature Assessment, Andi Liu, Astrid M Manuel, Yulin Dai, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.

RESULTS: We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. …

Time Dependent Analysis Of Rat Microglial Surface Markers In Traumatic Brain Injury Reveals Dynamics Of Distinct Cell Subpopulations, Assaf Gottlieb, Naama Toledano-Furman, Karthik S Prabhakara, Akshita Kumar, Henry W Caplan, Supinder Bedi, Charles S Cox, Scott D Olson

Faculty, Staff and Student Publications

Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to the activation of microglia. Accurate identification of microglial populations is key to understanding therapeutic approaches that modify microglial responses to TBI and improve long-term outcome measures. Notably, previous studies often utilized an outdated convention to describe microglial phenotypes. We conducted a temporal analysis of the response to controlled cortical impact (CCI) in rat microglia between ipsilateral and contralateral hemispheres across seven time points, identified microglia through expression of activation markers including CD45, CD11b/c, and p2y12 receptor and evaluated their activation state using additional …

The Impact Of Pediatric Opioid-Related Visits On Us Emergency Departments, Tiffany Champagne-Langabeer, Marylou Cardenas-Turanzas, Irma T Ugalde, Christine Bakos-Block, Angela L Stotts, Lisa Cleveland, Steven Shoptaw, James R Langabeer

Faculty, Staff and Student Publications

BACKGROUND: While there is significant research exploring adults' use of opioids, there has been minimal focus on the opioid impact within emergency departments for the pediatric population.

METHODS: We examined data from the Agency for Healthcare Research, the National Emergency Department Sample (NEDS), and death data from the Centers for Disease Control and Prevention. Sociodemographic and financial variables were analyzed for encounters during 2014-2017 for patients under age 18, matching diagnoses codes for opioid-related overdose or opioid use disorder.

RESULTS: During this period, 59,658 children presented to an ED for any diagnoses involving opioids. The majority (68.5%) of visits were …

Privacy-Preserving Logistic Regression With Secret Sharing, Ali Reza Ghavamipour, Fatih Turkmen, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.

METHODS: In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton-Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority …

Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Pora Kim, Hua Tan, Jiajia Liu, Himansu Kumar, Xiaobo Zhou

Faculty, Staff and Student Publications

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).