Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献

We leveraged electronic health record (EHR) data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Research Network (CRN) to identify social risk factor clusters, assess their association with obstructive sleep apnea (OSA), and determine relevant clinical predictors of cardiovascular (CV) outcomes among those experiencing OSA. Geographically informed social indicators were used to define social risk factor clusters via latent class analysis. EHR-wide diagnoses were used as predictors of 5-year incidence of major adverse CV events (MACE) using STREAMLINE, an end-to-end rigorous and interpretable automated machine learning pipeline. Analyses among over 1.4 million individuals revealed three major social risk factor clusters: lowest (35.7%), average (43.6%) and highest (22.7%) social burden. In adjusted analyses, those experiencing highest social burden were less likely to have received a diagnosis of OSA when compared to those experiencing lowest social burden (OR [95%CI]=0.85[0.82-0.88]). Among those with OSA and free of prior CV diseases (N=4,405), performance of predicting incident MACE reached a ROC-AUC of 0.70 [0.03] overall but varied when assessed within each social risk factor cluster. Feature importance also revealed that different clinical factors might explain predictions among each cluster. Results suggest relevant health disparities in the diagnosis of OSA and across clinical predictors of CV diseases among those with OSA, across social risk factor clusters, indicating that tailored interventions geared toward minimizing these disparities are warranted.

Adverse drug responses (ADRs) result in over 7,000 deaths annually. Pharmacogenomic studies have shown that many ADRs are partially attributable to genetics. However, emerging data suggest that epigenetic mechanisms, such as DNA methylation (DNAm) also contribute to this variance. Understanding the impact of DNA methylation on drug response may minimize ADRs and improve the personalization of drug regimens. In this work, we identify DNA methylation sites that likely impact drug response phenotypes for anticoagulant and cardiometabolic drugs. We use instrumental variable analysis to integrate genome-wide association study (GWAS) summary statistics derived from electronic health records (EHRs) within the U.K. Biobank (UKBB) with methylation quantitative trait loci (mQTL) data from the Genetics of DNA Methylation Consortium (GoDMC). This approach allows us to achieve a robust sample size using the largest publicly available pharmacogenomic GWAS. For warfarin, we find 71 DNAm sites. Of those, 8 are near the gene VKORC1 and 48 are on chromosome 6 near the human leukocyte antigen (HLA) gene family. We also find 2 warfarin DNAm sites near the genes CYP2C9 and CYP2C19. For statins, we identify 17 DNAm sites. Eight are near the APOB gene, which encodes a carrier protein for low-density lipoprotein cholesterol (LDL-C). We find no novel significant epigenetic results for metformin.

Large language models (LLMs) and biomedical annotations have a symbiotic relationship. LLMs rely on high-quality annotations for training and/or fine-tuning for specific biomedical tasks. These annotations are traditionally generated through expensive and time-consuming human curation. Meanwhile LLMs can also be used to accelerate the process of curation, thus simplifying the process, and potentially creating a virtuous feedback loop. However, their use also introduces new limitations and risks, which are as important to consider as the opportunities they offer. In this workshop, we will review the process that has led to the current rise of LLMs in several fields, and in particular in biomedicine, and discuss specifically the opportunities and pitfalls when they are applied to biomedical annotation and curation.

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.

Founded nearly 30 years ago, the Pacific Symposium on Biocomputing (PSB) has continually promoted collaborative research in computational biology, annually highlighting emergent themes that reflect the expanding interdisciplinary nature of the field. This study aimed to explore the collaborative and thematic dynamics at PSB using topic modeling and network analysis methods. We identified 14 central topics that have characterized the discourse at PSB over the past three decades. Our findings demonstrate significant trends in topic relevance, with a growing emphasis on machine learning and integrative analyses. We observed not only an expanding nexus of collaboration but also PSB's crucial role in fostering interdisciplinary collaborations. It remains unclear, however, whether the shift towards interdisciplinarity was driven by the conference itself, external academic trends, or broader societal shifts towards integrated research approaches. Future applications of next-generation analytical methods may offer deeper insights into these dynamics. Additionally, we have developed a web application that leverages retrieval augmented generation and large language models, enabling users to efficiently explore past PSB proceedings.

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured electronic health records (EHR) data. This approach largely overlooks unstructured EHR, a data format that could be utilized to enhance predictive accuracy. This study aims to enhance suicide risk models' predictive accuracy by developing a model that incorporates both structured EHR predictors and semantic NLP-derived variables from unstructured EHR. XGBoost models were fit to predict suicide risk- the interactions identified by the model were extracted using SHAP, validated using logistic regression models, added to a ridge regression model, which was subsequently compared to a ridge regression approach without the use of interactions. By introducing a selection parameter, α, to balance the influence of structured (α=1) and unstructured (α=0) data, we found that intermediate α values achieved optimal performance across various risk strata, improved model performance of the ridge regression approach and uncovered significant cross-modal interactions between psychosocial constructs and patient characteristics. These interactions highlight how psychosocial risk factors are influenced by individual patient contexts, potentially informing improved risk prediction methods and personalized interventions. Our findings underscore the importance of incorporating nuanced narrative data into predictive models and set the stage for future research that will expand the use of advanced machine learning techniques, including deep learning, to further refine suicide risk prediction methods.

Alzheimer's disease (AD) is a neurodegenerative disorder that results in progressive cognitive decline but without any clinically validated cures so far. Understanding the progression of AD is critical for early detection and risk assessment for AD in aging individuals, thereby enabling initiation of timely intervention and improved chance of success in AD trials. Recent pseudotime approach turns cross-sectional data into "faux" longitudinal data to understand how a complex process evolves over time. This is critical for Alzheimer, which unfolds over the course of decades, but the collected data offers only a snapshot. In this study, we tested several state-of-the-art pseudotime approaches to model the full spectrum of AD progression. Subsequently, we evaluated and compared the pseudotime progression score derived from individual imaging modalities and multi-modalities in the ADNI cohort. Our results showed that most existing pseudotime analysis tools do not generalize well to the imaging data, with either flipped progression score or poor separation of diagnosis groups. This is likely due to the underlying assumptions that only stand for single cell data. From the only tool with promising results, it was observed that all pseudotime, derived from either single imaging modalities or multi-modalities, captures the progressiveness of diagnosis groups. Pseudotime from multi-modality, but not the single modalities, confirmed the hypothetical temporal order of imaging phenotypes. In addition, we found that multi-modal pseudotime is mostly driven by amyloid and tau imaging, suggesting their continuous changes along the full spectrum of AD progression.

The following sections are included: Overview, Advancing multi-ancestry genetic research, Integrating social determinants of health to enhance genetic risk models, Methods to detect and mitigate disparities, Addressing Disparities in Adverse Drug Reactions, Conclusion, Acknowledgments,References.

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

Pharmacogenetics represents one of the most promising areas of precision medicine, with several guidelines for genetics-guided treatment ready for clinical use. Despite this, implementation has been slow, with few health systems incorporating the technology into their standard of care. One major barrier to uptake is the lack of education and awareness of pharmacogenetics among clinicians and patients. The introduction of large language models (LLMs) like GPT-4 has raised the possibility of medical chatbots that deliver timely information to clinicians, patients, and researchers with a simple interface. Although state-of-the-art LLMs have shown impressive performance at advanced tasks like medical licensing exams, in practice they still often provide false information, which is particularly hazardous in a clinical context. To quantify the extent of this issue, we developed a series of automated and expert-scored tests to evaluate the performance of chatbots in answering pharmacogenetics questions from the perspective of clinicians, patients, and researchers. We applied this benchmark to state-of-the-art LLMs and found that newer models like GPT-4o greatly outperform their predecessors, but still fall short of the standards required for clinical use. Our benchmark will be a valuable public resource for subsequent developments in this space as we work towards better clinical AI for pharmacogenetics.