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

Artificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algorithms prior to their adoption into clinical workflows. To address the barriers associated with patient privacy, intellectual property, and diverse model requirements, we introduce ClinValAI, a framework for establishing robust cloud-based infrastructures to clinically validate AI algorithms in medical imaging. By featuring dedicated workflows for data ingestion, algorithm scoring, and output processing, we propose an easily customizable method to assess AI models and investigate biases. Our novel orchestration mechanism facilitates utilizing the complete potential of the cloud computing environment. ClinValAI's input auditing and standardization mechanisms ensure that inputs consistent with model prerequisites are provided to the algorithm for a streamlined validation. The scoring workflow comprises multiple steps to facilitate consistent inferencing and systematic troubleshooting. The output processing workflow helps identify and analyze samples with missing results and aggregates final outputs for downstream analysis. We demonstrate the usability of our work by evaluating a state-of-the-art breast cancer risk prediction algorithm on a large and diverse dataset of 2D screening mammograms. We perform comprehensive statistical analysis to study model calibration and evaluate performance on important factors, including breast density, age, and race, to identify latent biases. ClinValAI provides a holistic framework to validate medical imaging models and has the potential to advance the development of generalizable AI models in clinical medicine and promote health equity.

Large Language Models (LLMs) have shown significant promise across a wide array of fields, including biomedical research, but face notable limitations in their current applications. While they offer a new paradigm for data analysis and hypothesis generation, their efficacy in computational biology trails other applications such as natural language processing. This workshop addresses the state of the art in LLMs, discussing their challenges and the potential for future development tailored to computational biology. Key issues include difficulties in validating LLM outputs, proprietary model limitations, and the need for expertise in critical evaluation of model failure modes.

Gene-environment interaction (GxE) studies provide insights into the interplay between genetics and the environment but often overlook multiple environmental factors' synergistic effects. This study encompasses the use of environment by environment interaction (ExE) studies to explore interactions among environmental factors affecting lipid phenotypes (e.g., HDL, LDL, and total cholesterol, and triglycerides), which are crucial for disease risk assessment. We developed a novel curated knowledge base, GE.db, integrating genomic and exposomic interactions. In this study, we filtered NHANES exposure variables (available 1999-2018) to identify significant ExE using GE.db. From 101,316 participants and 77 exposures, we identified 263 statistically significant interactions (FDR p < 0.1) in discovery and replication datasets, with 21 interactions significant for HDL-C (Bonferroni p < 0.05). Notable interactions included docosapentaenoic acid (22:5n-3) (DPA) - arachidic acid (20:0), stearic acid (18:0) - arachidic acid (20:0), and blood 2,5-dimethyfuran - blood benzene associated with HDL-C levels. These findings underscore GE.db's role in enhancing -omics research efficiency and highlight the complex impact of environmental exposures on lipid metabolism, informing future health strategies.

We present a comparative study on the performance of two popular open-source large language models for early prediction of sepsis: Llama-3 8B and Mixtral 8x7B. The primary goal was to determine whether a smaller model could achieve comparable predictive accuracy to a significantly larger model in the context of sepsis prediction using clinical data.Our proposed LLM-based sepsis prediction system, COMPOSER-LLM, enhances the previously published COMPOSER model, which utilizes structured EHR data to generate hourly sepsis risk scores. The new system incorporates an LLM-based approach to extract sepsis-related clinical signs and symptoms from unstructured clinical notes. For scores falling within high-uncertainty prediction regions, particularly those near the decision threshold, the system uses the LLM to draw additional clinical context from patient notes; thereby enhancing the model's predictive accuracy in challenging diagnostic scenarios.A total of 2,074 patient encounters admitted to the Emergency Department at two hospitals within the University of California San Diego Health system were used for model evaluation in this study. Our findings reveal that the Llama-3 8B model based system (COMPOSER-LLMLlama) achieved a sensitivity of 70.3%, positive predictive value (PPV) of 32.5%, F-1 score of 44.4% and false alarms per patient hour (FAPH) of 0.0194, closely matching the performance of the larger Mixtral 8x7B model based system (COMPOSER-LLMmixtral) which achieved a sensitivity of 72.1%, PPV of 31.9%, F-1 score of 44.2% and FAPH of 0.020. When prospectively evaluated, COMPOSER-LLMLlama demonstrated similar performance to the COMPOSER-LLMmixtral pipeline, with a sensitivity of 68.7%, PPV of 36.6%, F-1 score of 47.7% and FAPH of 0.019 vs. sensitivity of 70.5%, PPV of 36.3%, F-1 score of 47.9% and FAPH of 0.020. This result indicates that, for extraction of clinical signs and symptoms from unstructured clinical notes to enable early prediction of sepsis, the Llama-3 generation of smaller language models can perform as effectively and more efficiently than larger models. This finding has significant implications for healthcare settings with limited resources.

Uterine leiomyomata, or fibroids, are common gynecological tumors causing pelvic and menstrual symptoms that can negatively affect quality of life and child-bearing desires. As fibroids grow, symptoms can intensify and lead to invasive treatments that are less likely to preserve fertility. Identifying individuals at highest risk for fibroids can aid in access to earlier diagnoses. Polygenic risk scores (PRS) quantify genetic risk to identify those at highest risk for disease. Utilizing the PRS software PRS-CSx and publicly available genome-wide association study (GWAS) summary statistics from FinnGen and Biobank Japan, we constructed a multi-ancestry (META) PRS for fibroids. We validated the META PRS in two cross-ancestry cohorts. In the cross-ancestry Electronic Medical Record and Genomics (eMERGE) Network cohort, the META PRS was significantly associated with fibroid status and exhibited 1.11 greater odds for fibroids per standard deviation increase in PRS (95% confidence interval [CI]: 1.05 - 1.17, p = 5.21x10-5). The META PRS was validated in two BioVU cohorts: one using ICD9/ICD10 codes and one requiring imaging confirmation of fibroid status. In the ICD cohort, a standard deviation increase in the META PRS increased the odds of fibroids by 1.23 (95% CI: 1.15 - 1.32, p = 9.68x10-9), while in the imaging cohort, the odds increased by 1.26 (95% CI: 1.18 - 1.35, p = 2.40x10-11). We subsequently constructed single ancestry PRS for FinnGen (European ancestry [EUR]) and Biobank Japan (East Asian ancestry [EAS]) using PRS-CS and discovered a nominally significant association in the eMERGE cohort within fibroids and EAS PRS but not EUR PRS (95% CI: 1.09 - 1.20, p = 1.64x10-7). These findings highlight the strong predictive power of multi-ancestry PRS over single ancestry PRS. This study underscores the necessity of diverse population inclusion in genetic research to ensure precision medicine benefits all individuals equitably.

The rapid advancement of artificial intelligence and machine learning (AI/ML) technologies in healthcare presents significant opportunities for enhancing patient care through innovative diagnostic tools, monitoring systems, and personalized treatment plans. However, these innovative advancements might result in regulatory challenges given recent Supreme Court decisions that impact the authority of regulatory agencies like the Food and Drug Administration (FDA). This paper explores the implications of regulatory uncertainty for the healthcare industry related to balancing innovation in biotechnology and biocomputing with ensuring regulatory uniformity and patient safety. We examine key Supreme Court cases, including Loper Bright Enterprises v. Raimondo, Relentless, Inc. v. Department of Commerce, and Corner Post, Inc. v. Board of Governors of the Federal Reserve System, and their impact on the Chevron doctrine. We also discuss other relevant cases to highlight shifts in judicial approaches to agency deference and regulatory authority that might affect how science is handled in regulatory spaces, including how biocomputing and other health sciences are governed, how scientific facts are applied in policymaking, and how scientific expertise guides decision making. Through a detailed analysis, we assess the potential impact of regulatory uncertainty in healthcare. Additionally, we provide recommendations for the medical community on navigating these challenges.

Human involvement remains critical in most instances of clinical decision-making. Recent advances in AI and machine learning opened the door for designing, implementing, and translating interactive AI systems to support clinicians in decision-making. Assessing the impact and implications of such systems on patient care and clinical workflows requires in-depth studies. Conducting evaluation studies of AI-supported interactive systems to support decision-making in clinical settings is challenging and time-consuming. These studies involve carefully collecting, analyzing, and interpreting quantitative and qualitative data to assess the performance of the underlying AI-supported system, its impact on the human decision-making process, and the implications for patient care. We have previously developed a toolkit for designing and implementing clinical AI software so that it can be subjected to an application-based evaluation. Here, we present a visual analytics frame-work for analyzing and interpreting the data collected during such an evaluation process. Our framework supports identifying subgroups of users and patients based on their characteristics, detecting outliers among them, and providing evidence to ensure adherence to regulatory guidelines. We used early-stage clinical AI regulatory guidelines to drive the system design, implemented multiple-factor analysis and hierarchical clustering as exemplary analysis tools, and provided interactive visualizations to explore and interpret results. We demonstrate the effectiveness of our framework through a case study to evaluate a prototype AI-based clinical decision-support system for diagnosing pediatric brain tumors.

In the Carolina Breast Cancer Study (CBCS), clustering census tracts based on spatial location, demographic variables, and socioeconomic status is crucial for understanding how these factors influence health outcomes and cancer risk. This task, known as spatial clustering, involves identifying clusters of similar locations by considering both geographic and characteristic patterns. While standard clustering methods such as K-means, spectral clustering, and hierarchical clustering are well-studied, spatial clustering is less explored due to the inherent differences between spatial domains and their corresponding covariates. In this paper, we introduce a spatial clustering algorithm called Gaussian Process Spatial Clustering (GPSC). GPSC leverages the flexibility of Gaussian Processes to cluster unobserved functions between different domains, extending traditional clustering techniques to effectively handle geospatial data. We provide theoretical guarantees for GPSC's performance and demonstrate its capability to recover true clusters through several empirical studies. Specifically, we identify clusters of census tracts in North Carolina based on socioeconomic and environmental indicators associated with health and cancer risk.

Down syndrome (DS), caused by the triplication of chromosome 21 (T21), is a prevalent genetic disorder with a higher incidence of obesity. Traditional approaches have struggled to differentiate T21-specific molecular dysregulation from general obesity-related processes. This study introduces the omni-PLIER framework, combining the Pathway-Level Information ExtractoR (PLIER) with the omnigenic model, to uncover molecular mechanisms underlying obesity in DS. The PLIER framework aligns gene expression data with biological pathways, facilitating the identification of relevant molecular patterns. Using RNA sequencing data from the Human Trisome Project, omni-PLIER identified latent variables (LVs) significantly associated with both T21 and body mass index (BMI). Elastic net regression and causal mediation analysis revealed LVs mediating the effect of karyotype on BMI. Notably, LVs involving glutathione peroxidase-1 (GPX1) and MCL1 apoptosis regulator, BCL2 family members emerged as crucial mediators. These findings provide insights into the molecular interplay between DS and obesity. The omni-PLIER model offers a robust methodological advancement for dissecting complex genetic disorders, with implications for understanding obesity-related processes in both DS and the general population.

Recent advancements in Artificial Intelligence (AI) and data center infrastructure have brought the global cloud computing market to the forefront of conversations about sustainability and energy use. Current policy and infrastructure for data centers prioritize economic gain and resource extraction, inherently unsustainable models which generate massive amounts of energy and heat waste. Our team proposes the formation of policy around earth-friendly computation practices rooted in Indigenous models of circular systems of sustainability. By looking to alternative systems of sustainability rooted in Indigenous values of aloha 'āina, or love for the land, we find examples of traditional ecological knowledge (TEK) that can be imagined alongside Solarpunk visions for a more sustainable future. One in which technology works with the environment, reusing electronic waste (e-waste) and improving data life cycles.