GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console


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Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Genomics/Omic Data Interpretation, Biomarker Discovery and Development, Genotype-phenotype Association Studies (including GWAS), Informatics Research/Biomedical Informatics Research Methods
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Novel Methods for Variant Detection and Interpretation from Omics Data

The generation of massive omics and phenotypic data has enabled investigators to study the genetic architecture and markers in many complex diseases; however, it poses a significant challenge in efficiently uncovering valuable knowledge. Here, we introduce GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging ChatGPT, we aim to make GENEVIC a biologist’s ‘copilot’. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer’s disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. The implementation of BrainGeneBot is set to transform genomic research for AD and other brain diseases by improving data accessibility, accelerating discovery processes, and refining the precision of genetic insights.
Speaker(s):
Zhongming Zhao, PhD
University of Texas Heal Sci Ctr Houston

Author(s):
Zhongming Zhao, PhD - University of Texas Heal Sci Ctr Houston;

Estimating single sample gene program dysregulation using latent factor causal graphs


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Presentation Time: 03:45 PM - 04:00 PM

Abstract Keywords: Systems Biology and Network Analysis, Patient-centered Research and Care, Data Mining and Knowledge Discovery
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Translational Bioinformatics Using Multi-Modal Patient Data and AI

Gene expression programs that establish and maintain specific cellular states are orchestrated through a regulatory network composed of transcription factors, cofactors, and chromatin regulators. Dysregulation of this network can lead to a broad range of diseases. In this work, we introduce LaGrACE, a novel method designed to estimate the magnitude of dysregulation of gene programs utilizing both omics data and clinical information. LaGrACE first learns gene programs, represented as latent factors, from gene expression data of a set of reference samples. Then, it facilitates grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms. We rigorously evaluated LaGrACE’s performance using synthetic data, breast cancer and chronic obstructive pulmonary disease (COPD) datasets, and single-cell RNA sequencing (scRNA-seq) datasets. Our findings demonstrate that LaGrACE is exceptionally robust in identifying biologically meaningful and prognostic subtypes. Additionally, it effectively discerns drug-response signals at a single-cell resolution. The COPD analysis revealed a new association between LEF1 and COPD molecular mechanisms and mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating disease mechanisms.
Speaker(s):
Panayiotis Benos, PhD
University of Florida

Author(s):
Minxue Jia, BSc - University of Pittsburgh; Haiyi Mao, BSc - Pittsbusrgh; Mengli Zhou, BSc - University of Pittsburgh; Yu Chih Chen, PhD - University of Pittsburgh;

Genotype and phenotype risk score analyses of genetically admixed multiple sclerosis patients in All of Us


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Presentation Time: 04:00 PM - 04:15 PM

Abstract Keywords: Phenomics and Phenome-wide Association Studies, Secondary Use of EHR Data, Real-World Evidence and Policy Making, Clinical Genomics/Omics and Interventions Based on Omics Data
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Implementation Science and Deployment in Informatics: Enabling Clinical and Translational Research

Multiple sclerosis (MS) is a demyelinating disease influenced by genetic and environmental risk factors. Current research indicates improved patients’ long-term health outcomes are associated with earlier diagnosis and treatment initiation. We developed a well-performing risk score model for MS based on genetic burden alone, and demonstrate the utility of phenotype-based risk scoring. Combination genotype-phenotype risk models have potential to aid in early screening and diagnosis of MS.
Speaker(s):
Mary Davis, PhD
Brigham Young University

AI-driven model to bridge pathology image and transcriptomics


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Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Ontologies, Genomics/Omic Data Interpretation
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Translational Bioinformatics Using Multi-Modal Patient Data and AI

Computational pathology has emerged as a powerful tool for revolutionizing routine pathology through AI-driven analysis of pathology images. Recent advancements in omics technologies, such as spatial transcriptomics, have further enriched the field by providing detailed transcriptomic information alongside tissue histology. However, existing sequencing platforms lack the ability to effectively harness the synergies between tissue images and genomic data. To address this gap, we develop Thor, an AI-based infrastructure for seamless integration of histological and genomic analysis of tissues. Thor infers single-cell resolution spatial transcriptome through an anti-shrinking Markov diffusion method. Its effectiveness and versatility were validated through simulations, diverse datasets, and compelling case studies involving human carcinoma and heart failure samples. Thor enabled unbiased screening of breast cancer hallmarks and identification of fibrotic regions in myocardial infarction tissue. With an extensible framework for genomic and tissue image analysis accessible through an interactive web platform, Thor empowers researchers to understand biological structures and decipher disease pathogenesis, paving the way for significant advancements in research and clinical applications.
Speaker(s):
Guangyu Wang, PhD
Houston Methodist

Clinical and Genomic Insights into Immune-Related Adverse Events


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Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Data-Driven Research and Discovery, Data Mining and Knowledge Discovery, Informatics of Cancer Immunotherapy
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by enhancing the immune system’s ability to target tumor cells, significantly improving survival outcomes in various cancers. However, ICIs are frequently associated with immune-related adverse events (irAEs), including acute kidney injury (ICI-AKI), which complicate patient management. Using data from the OneFlorida+ Clinical Research Network and the All of Us (AoU) cohort, this study identifies clinical and genetic risk factors for these adverse events.
In the OneFlorida+ cohort of 6,526 ICI-treated patients, 56.2% developed irAEs, with younger patients, females, and those with comorbidities (e.g., myocardial infarction and renal disease) being at higher risk. Cancer type and treatment regimens also influenced irAE risk, with combined CTLA4+PD(L)1 inhibitors increasing the risk by 35%. Severe irAEs significantly impacted overall survival and the timing of irAE onset. The genetic analysis of 414 ICI-treated patients from the AoU cohort identified the rs16957301 variant in the PCCA gene as a significant risk marker for ICI-AKI in Caucasians. Patients with the risk genotypes (TC/CC) developed AKI significantly earlier (median: 3.6 months) than those with the reference genotype (TT, median: 7.0 months). The variant’s specificity to ICI-treated patients highlights its potential utility in personalized risk assessment.
These findings emphasize the importance of integrating clinical and genomic insights to optimize ICI therapy. Identifying high-risk patients through genetic screening and tailored management strategies could mitigate adverse events and improve patient outcomes. Future research should validate these findings in diverse populations and explore underlying biological mechanisms.
Speaker(s):
Qianqian Song, Ph.D.
University of Florida