NCI Data Jamboree (Project Abstract Submission)

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# Created User IP address First Name Middle Initial Last Name Degree(s) Position/Title/Career Status Organization Organization Address Email List of Additional Authors Abstract Category Abstract Keywords Abstract Title Abstract Operations
10 Tue, 07/07/2026 - 09:48 Anonymous 10.208.24.21 Morolake Okanlawon BSc. PhD Student George Mason University Fairfax mokanlaw@gmu.edu Developing, refining, or validating tools, methods, algorithms, and pipelines machine learning, artificial intelligence, reproducible pipelines, model validation, cancer data integration Developing and Validating Machine Learning and AI Pipelines for Integrating and Analyzing Cancer Research Data Reliable analysis of cancer research data depends not only on applying machine learning and artificial intelligence methods but on the tools and pipelines that make those methods reproducible and reusable. Building on prior experience employing statistical and computational methods to analyze data in my own research, this project focuses on developing, refining, and validating machine learning and AI pipelines for integrating and analyzing biomedical and cancer datasets. Proposed work includes constructing modular pipelines for data preprocessing, model training, and evaluation, along with validation procedures to assess reproducibility, robustness, and interpretability of AI models. Particular attention will be given to designing components that can be reused across clinical, genomic, or imaging datasets rather than tailored to a single analysis. As a PhD student with a background in Bioinformatics and Computational Biology, I aim to extend my analytical foundation toward the development of machine learning and AI tools within a collaborative team. The anticipated outcome is a set of validated, reusable pipeline components that support reproducible machine learning and AI workflows for cancer research.
9 Thu, 07/02/2026 - 12:25 Anonymous 10.208.28.16 Andrea R Molino ScM Ph.D. Candidate University of Washington Seattle amolino29@gmail.com Building study cohorts (e.g., with visualization capabilities) Project Seeker: Andrea R. Molino I am a PhD candidate in epidemiology and NCI T32 predoctoral fellow with an interest in improving care access along the entire cancer continuum in the United States. These interests drove me to pursue a dissertation focused on HPV self-sampling, specifically to understand a series of auxiliary questions that address how this novel tool might influence the broader healthcare landscape. I have exceptionally strong epidemiologic methods and study design skills, am a talented programmer, and pride myself in my ability to communicate complex results through effective data visualizations. Prior to my doctoral training, I was an epidemiologist at Johns Hopkins University on a longitudinal cohort study.

My dissertation linked Kaiser Permanente EHR data with publicly available census-tract level information to capture variations in HPV self-sampling uptake by neighborhood socioeconomic status. This work inspired my passion for using publicly available data and showed me that identifying gaps in your own data can drive innovation. It also demonstrated how much more impactful our analyses can be when we simply know which tools and resources already exist and have the skills to use them. Participating in the NCI Data Jamboree will facilitate my ability to continue this type of work, and introduce me to other like-minded researchers.

Through participation, I hope to become more aware of tools available and ongoing research that aligns with my own, hopefully leading to collaborations that make my work stronger and interdisciplinary. Additionally, I have become a strong GitHub advocate for reproducible research and dissemination of open-source tools in epidemiology and hope to meet others who also aim to integrate version control more thoroughly into our field.

I look forward to bringing my skills to this collaborative space while learning from others equally invested in making cancer research more open, reproducible, and impactful.
8 Thu, 07/02/2026 - 04:29 Anonymous 10.208.24.90 Reda Mohamed Elbadawy MD Professor Gastroenterology , Hepatology&Executive Director for Center of Excellence of gut microbiome in fatty liver-fatty pancreas and haert diseases Center of excellence ,Benha University , Egypt Benha reda.albadawy@fmed.bu.edu.eg Developing, refining, or validating tools, methods, algorithms, and pipelines Fatty pancreas, Fibrosis ,FibroScan and Cancer Pancreas Early detection of pancreatic cancer due to fatty pancreas and pancreatic fibrosis diagnosed by FibroScan Project Describtion
o scientific or technical questions to address
Do we need simple , non invasive tool for early detection of pancreatic cancer?
So this project is applicable and important to others in the broader community because the use of FibroScan which is simple techeniuqe, novel and Unique to . Avilable now at most center of gastroenterology , hepatology . the time of examination of patients about 10minutes, without radiation , patients fasting only 3-5 hours , the results automatically . digital at the same sitting and not much time consuming.The data will be 5 Images for every patients plus other laboratory data.We are in need to expertise, computational tools, and/or computing environment needed to carry out your project.
Non – alcoholic fatty pancreatic disease ( NAFPD ) is a hot topic in gastroenterology. Just as obesity and metabolic syndrome are global problems, pancreatic steatosis especially in the form of NAFPD is an important challenge for pancreatologists, diabetologists, and nutritionists . Fatty pancreas could be an initial indicator of ectopic fat deposition and an earlier manifestation of metabolic syndrome than fatty liver . A role of NAFPD in the development of "prediabetes" and T2DM has also been suggested by most human studies and pancreatic cancer.Pancreatic cancer often presents late with vague symptoms and is associated with poor prognosis due to rapid metastasisUnfournately, it is one of the cancers that harbors delayed diagnosis.However, there are currently no screening tool for pancreatic cancer. NAFPD has been strongly suggested to be involved in pancreatic carcinogenesis. So the use of FibroScan as simple , non invasive , unique tool will add much valiable to pick up and screen cases , it also diagnosis pancreatic fibrosis which is has a crucial role in carcinogenesis .To date this project will be the first world wide .
7 Tue, 06/30/2026 - 13:13 Anonymous 10.208.28.16 Mingyu Yang Ph.D. Associate Research Scientist Yale University New Haven mingyu.yang@yale.edu Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data Spatial omics; Computational biology; Machine learning; Cancer genomics; Data integration Computational Analysis of HTAN Spatial Multi-omics Data I have over 15 years of experience in bioinformatics, developing computational methods and analytical pipelines for large-scale sequencing data across cancer and other human diseases. My research has evolved from bulk genomics and transcriptomics to single-cell sequencing and, more recently, spatial multi-omics. At Yale University, I have been involved in developing computational methods for analyzing spatial transcriptomics and proteomics data, with a particular interest in applying AI and machine learning to understand tumor heterogeneity and the tumor microenvironment.

I would like to participate in the HTAN Data Jamboree because I am passionate about cancer research and believe that the extensive HTAN datasets provide an exceptional opportunity to develop new computational methods by reusing existing high-quality data. I look forward to collaborating with researchers from diverse backgrounds, exchanging ideas, and learning from experts in cancer biology, spatial omics, and data science. I believe that combining complementary expertise will inspire innovative approaches that would be difficult to develop independently.

Through the Jamboree, I hope to identify an important computational challenge that can benefit from statistical and machine learning approaches and to brainstorm a novel analytical framework with potential collaborators. My goal is to leave the event with a well-defined project concept and a collaborative team that can continue working together beyond the Jamboree. Ultimately, I hope this effort will lead to new computational methods, open-source software, and publications that help maximize the value of HTAN data for the broader cancer research community.
6 Tue, 06/30/2026 - 10:29 Anonymous 10.208.28.16 Adam X Miranda Ph.D. Computational Genomics Specialist NIAID Bethesda, MD adam.miranda@nih.gov Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data Sarcoma, Epigenetics, Transcriptomics, Bioinformatics, Multiomics Project Seeker My expertise is primarily in the analysis and integration of multiple types of NGS data. In my graduate studies, I integrated RNAseq, ATACseq, and CRISPRKO screen data to interrogate the distinct impacts of two different mutations of the PIK3CA gene in breast cancer. In my post doc, I continued this line of work by integrating DNA sequencing and RNA sequencing of dozens of sarcoma patients to define new molecular definitions of sarcoma subtypes. In my current role, I serve a variety of projects across NIAID and have broadened my skill set to include the analysis of single cell data sets including scRNA and scATAC-seq data. I also have some experience in the development of machine learning models.

For this data jamboree, I want to return to making an impact on the research subject matter that I am most passionate about. I care deeply about cancer research and my career goal is to expand treatment options for all cancer patients, especially those with rarer forms of the disease. I am excited to contribute to any project that I can, and I believe my broad bioinformatic expertise should allow me to contribute to a number of different kinds of projects.

My goals for the this jamboree are to apply my skills to new challenges and to meet like minded researchers in the cancer research space. I also want to learn from others at the event with regards to current trends in cancer research and different techniques of data analysis.
5 Mon, 06/22/2026 - 16:33 Anonymous 10.208.24.144 OSCAR MARINO Vidal Ph.D. PI Universidad del NOrte Barranquilla. Atlantico oorjuela@uninorte.edu.co
  • First Name: Jorge
    Last Name: Velez
    Affiliation: UNINORTE
 Evaluating data quality for reproducibility and AI-readiness Precision Oncology Machine Learning Multi-Modal Data Integration Predictive Modeling AI-Driven Patient Stratification Models for Personalized Treatment in Breast Cancer Using Multi-Omic and Clinical Data Breast cancer is a highly heterogeneous disease characterized by substantial variability in molecular profiles, treatment response, recurrence risk, and survival outcomes. Current clinical stratification approaches based on receptor status and tumor staging do not fully capture this complexity, often leading to suboptimal treatment selection. This project aims to develop and evaluate artificial intelligence (AI)-driven patient stratification models that identify clinically meaningful breast cancer subgroups using multi-omic and clinical data. The primary scientific question is whether integrated machine learning approaches can improve prediction of treatment response and patient outcomes compared with conventional classification methods.

During the 3-day jamboree, the team will construct and compare unsupervised and supervised learning frameworks for patient stratification. Planned analyses include clustering of patients based on genomic, transcriptomic, and clinical features; identification of molecular signatures associated with treatment response; and development of predictive models for outcomes such as overall survival, disease-free survival, and therapeutic response. We will evaluate the interpretability of resulting models using explainable AI techniques to identify key biomarkers and pathways driving subgroup assignments.

Developing reproducible workflows for integrating heterogeneous biomedical datasets can benefit researchers, clinicians, and data scientists working on cancer prognosis, biomarker discovery, and personalized medicine. The resulting analytical framework could be adapted to other malignancies and disease areas.Data Types and Repositories

publicly available datasets, including:

Clinical data: patient demographics, tumor characteristics, treatment records, and survival outcomes from the The Cancer Genome Atlas Breast Invasive Carcinoma (BRCA) cohort.
Transcriptomic data: RNA-sequencing gene expression profiles from The Cancer Genome Atlas.
Genomic data: somatic mutation and copy number variation data from The Cancer Genome Atlas.
Proteomic data (optional): protein expression profiles from Clinical Proteomic Tumor Analysis Consortium.
Validation datasets: independent breast cancer cohorts from Gene Expression Omnibus and/or International Cancer Genome Consortium.
4 Thu, 06/11/2026 - 19:11 Anonymous 10.208.28.22 Sabira Dabeer M.B.B.S ; MD(Clinical Biochemistry); MS(Biological Data Science) Clinical Biochemist and Data Scientist ARIZONA STATE UNIVERSITY Phoenix dabeersabira@gmail.com
  • First Name: Hatim
    Last Name: Palitanawala
    Affiliation: Mumbai University
 Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data Integrating Dietary, Clinical, and Molecular Data to Identify Risk Signatures for Early-Onset Colorectal Cancer Using Explainable Machine Learning Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50 years, has increased substantially over the past two decades despite declining incidence among older adults. Although lifestyle and dietary changes have been proposed as contributing factors, the biological mechanisms linking these exposures to colorectal cancer development remain incompletely understood. This project aims to investigate whether dietary patterns and lifestyle factors are associated with molecular signatures and biological pathways implicated in EOCRC and whether these features can be integrated into explainable machine learning models for risk prediction.

The proposed work will integrate epidemiologic, clinical, and molecular data from publicly available resources including the All of Us Research Program, Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, Surveillance, Epidemiology, and End Results (SEER) program, and molecular datasets available through the Genomic Data Commons (GDC), including The Cancer Genome Atlas (TCGA). Variables of interest may include dietary exposures, body mass index, physical activity, smoking status, alcohol use, demographic factors, colorectal cancer outcomes, and molecular features associated with key colorectal cancer pathways, including WNT signaling, TP53, KRAS, DNA mismatch repair, inflammatory, and metabolic pathways.

Machine learning approaches, including logistic regression, random forest, and gradient boosting methods, will be evaluated to identify factors associated with EOCRC. Explainable AI techniques, including SHAP-based feature attribution, will be used to characterize the relative contribution of dietary, clinical, and molecular variables to model predictions. The project will also explore the feasibility of integrating heterogeneous data sources to generate interpretable risk signatures that may improve understanding of EOCRC development.
Results may help identify candidate risk factors, generate new biological hypotheses, and establish a framework for future integrative analyses of cancer epidemiology and molecular data.
3 Wed, 06/10/2026 - 14:01 Anonymous 10.208.24.28 Jaclyn N Taroni Ph.D. Director of the Childhood Cancer Data Lab Alex's Lemonade Stand Foundation Wynnewood, PA j.taroni@alexslemonade.org Evaluating data quality for reproducibility and AI-readiness Project Seeker: Jaclyn N. Taroni Experience and expertise: I am Director of the Childhood Cancer Data Lab at Alex’s Lemonade Stand Foundation (https://www.ccdatalab.org/), where I lead a multidisciplinary team of data scientists, software engineers, and UX professionals. At the Data Lab, we build tools to make pediatric cancer data accessible, such as the Single-cell Pediatric Cancer Atlas (scpca.alexslemonade.org), and organize open science projects, such as the Open Pediatric Brain Tumor Atlas. I am a computational biologist by training. Historically, my research focus has been on leveraging large collections of transcriptomic data and machine learning in rare disease settings.

Why I want to participate: My work depends on the broader cancer data ecosystem being interoperable, well-documented, and reusable, and I want to engage directly with NCI repositories and a cross-disciplinary group on the practical barriers to finding, accessing, and integrating these resources. I am looking to contribute and learn from investigators in the broader cancer space in a hands-on collaboration setting.

What I hope to achieve: I hope to build collaborations across the cancer data community that extend the reach of open-science tooling and to help produce publicly shareable artifacts that others can build on. I am especially interested in joining a team where reproducibility and data quality are central to the problem.
2 Mon, 06/08/2026 - 20:30 Anonymous 10.208.24.28 Megha B. Srivastava B.S./M.S. in Computer Science PhD Student in Computer Science Stanford University Stanford megha@cs.stanford.edu Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data machine learning, AI-readiness, distribution shift, causal inference, confounding variables, language models, LLMs Project Seeker I am a PhD student in Computer Science, with significant experience in machine learning, large language modeling, and human-AI interaction. I have recently been transition my research towards applications of AI in medicine, healthcare, and drug discovery, and hope to understand what challenges exist on the dataset-level, and what are ideal datasets that can help push different problems forward. One research area I am particularly interested in is challenges of distribution shift -- e.g. mismatch between the training dataset and test time inference, and how to tackle that. I am particularly curious about methods for identifying potential confounding variables that are unmeasured in the current dataset. My hope is to join a project that can help improve the quality and availability of oncology datasets for machine learning research.
1 Fri, 05/29/2026 - 09:31 Anonymous 10.208.28.199 Ying huang MD HSA NCI Rockville ying.huang@nih.gov
  • First Name: Min
    Last Name: Zhang
    Affiliation: UCI
  • First Name: Linel
    Last Name: Demar
    Affiliation: Drew University
 Developing tutorials, workbooks, infographics, or creative use of data for educational and engagement purposes Keywords: Findability; Accessibility; Governance Interoperability; Data Reuse Workflow Observability; NCI Repositories A Pilot Discovery Friction Framework for Quantifying Research Initiation Burden Across Federated Oncology Data Ecosystems The rapid expansion of federated oncology ecosystems has increased controlled-access biomedical datasets, but translational investigators frequently encounter fragmented discovery pathways and complex governance workflows. While essential for participant privacy, the operational burden of these systems remains poorly characterized. This project will develop and pilot the Discovery Friction Framework, a human-centered observability framework designed to quantify "research initiation burden" across federated ecosystems.

The framework employs structured workflow instrumentation—including screen recording, event logging, and rubric-based telemetry extraction—to capture objective operational metrics across multiple domains (discovery burden, authentication complexity, workflow instability, governance complexity, and temporal burden). Metrics include portal transitions, unresolved discovery paths, authentication redirection chains, manual workarounds, Data Access Request (DAR) revision cycles, and time-to-access intervals.

To test the framework against realistic discovery pathways, the project will identify high-value datasets across multiple modalities from published secondary data analyses utilizing dbGaP (genomics), NCCR (clinical), and CRDC IDC (imaging).

By characterizing translational workflow complexity through real-world investigator interactions, this pilot will generate foundational telemetry primitives, workflow observability methods, and evidence-based insights that may support future scalable observability strategies across biomedical data ecosystems. The project will provide actionable guidance for improving pathway transparency, governance intelligibility, and translational data access coordination across the NCI data ecosystem.

Implementation Requirements:
The project requires a standard web-based testing environment (no high-performance computing required). Software tools include open-source user-session logging, screen-capture instrumentation, and text-mining packages (Python/R) to structure qualitative rubrics into quantitative dataframes. The project relies on a cross-disciplinary team featuring: Human-Centered Design/UX Researchers to build telemetry rubrics; Data Governance Specialists and Data Access Committee (DAC) members familiar with dbGaP and NIH data access mechanisms to map workflow pathways; and Front-End/Data Engineers to develop the underlying schema for the friction telemetry database.