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

{"title":"Plasma protein-based and polygenic risk scores serve complementary roles in predicting inflammatory bowel disease.","authors":"Jakob Woerner, Thomas Westbrook, Seokho Jeong, Manu Shivakumar, Allison R Greenplate, Sokratis A Apostolidis, Seunggeun Lee, Yonghyun Nam, Dokyoon Kim","doi":"10.1142/9789819807024_0037","DOIUrl":"10.1142/9789819807024_0037","url":null,"abstract":"<p><p>Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), has a significant genetic component and is increasingly prevalent due to environmental factors. Current polygenic risk scores (PRS) have limited predictive power and cannot inform time of symptom onset. Circulating proteomics profiling offers a novel, non-invasive approach for understanding the inflammatory state of complex diseases, enabling the creation of proteomic risk scores (ProRS). This study utilizes data from 51,772 individuals in the UK Biobank to evaluate the unique and combined contributions of PRS and ProRS to IBD risk prediction. We developed ProRS models for CD and UC, assessed their predictive performance over time, and examined the benefits of integrating PRS and ProRS for enhanced risk stratification. Our findings are the first to demonstrate that combining genetic and proteomic data improves IBD incidence prediction, with ProRS providing time-sensitive predictions and PRS offering additional long-term predictive value. We also show that the ProRS achieves better predictive performance among individuals with high PRS. This integrated approach highlights the potential for multi-omic data in precision medicine for IBD.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"522-534"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding TCR T cell knockout behavior using interpretable machine learning.","authors":"Marcus Blennemann, Archit Verma, Stefanie Bachl, Julia Carnevale, Barbara E Engelhardt","doi":"10.1142/9789819807024_0028","DOIUrl":"10.1142/9789819807024_0028","url":null,"abstract":"<p><p>Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer immunotherapies, but understanding the changes to T cell behavior induced by genetic perturbations remains a challenge. Prior studies have evaluated the effect of different genetic modifications with cytokine production and metabolic activity assays. Live-cell imaging is an inexpensive and robust approach to capture TCR T cell responses to cancer. Most methods to quantify T cell responses in live-cell imaging data use simple approaches to count T cells and cancer cells across time, effectively quantifying how much space in the 2D well each cell type covers, leaving actionable information unexplored. In this study, we characterize changes in TCR T cell's interactions with cancer cells from live-cell imaging data using explainable artificial intelligence (AI). We train convolutional neural networks to distinguish behaviors in TCR T cell with CRISPR knock outs of CUL5, RASA2, and a safe harbor control knockout. We use explainable AI to identify specific interaction types that define different knock-out conditions. We find that T cell and cancer cell coverage is a strong marker of TCR T cell modification when comparing similar experimental time points, but differences in cell aggregation characterize CUL5KO and RASA2KO behavior across all time points. Our pipeline for discovery in live-cell imaging data can be used for characterizing complex behaviors in arbitrary live-cell imaging datasets, and we describe best practices for this goal.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"382-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indigenous Data Sovereignty, Circular Systems, and Solarpunk Solutions for a Sustainable Future.","authors":"Ka'ulawena Alipio, Javier García-Colón, Nima Boscarino, Keolu Fox","doi":"10.1142/9789819807024_0054","DOIUrl":"10.1142/9789819807024_0054","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"717-733"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Determinants of Health and Lifestyle Risk Factors Modulate Genetic Susceptibility for Women's Health Outcomes.","authors":"Lindsay A Guare, Jagyashila Das, Lannawill Caruth, Shefali Setia-Verma","doi":"10.1142/9789819807024_0022","DOIUrl":"10.1142/9789819807024_0022","url":null,"abstract":"<p><p>Women's health conditions are influenced by both genetic and environmental factors. Understanding these factors individually and their interactions is crucial for implementing preventative, personalized medicine. However, since genetics and environmental exposures, particularly social determinants of health (SDoH), are correlated with race and ancestry, risk models without careful consideration of these measures can exacerbate health disparities. We focused on seven women's health disorders in the All of Us Research Program: breast cancer, cervical cancer, endometriosis, ovarian cancer, preeclampsia, uterine cancer, and uterine fibroids. We computed polygenic risk scores (PRSs) from publicly available weights and tested the effect of the PRSs on their respective phenotypes as well as any effects of genetic risk on age at diagnosis. We next tested the effects of environmental risk factors (BMI, lifestyle measures, and SDoH) on age at diagnosis. Finally, we examined the impact of environmental exposures in modulating genetic risk by stratified logistic regressions for different tertiles of the environment variables, comparing the effect size of the PRS. Of the twelve sets of weights for the seven conditions, nine were significantly and positively associated with their respective phenotypes. None of the PRSs was associated with different ages at diagnoses in the time-to-event analyses. The highest environmental risk group tended to be diagnosed earlier than the low and medium-risk groups. For example, the cases of breast cancer, ovarian cancer, uterine cancer, and uterine fibroids in highest BMI tertile were diagnosed significantly earlier than the low and medium BMI groups, respectively). PRS regression coefficients were often the largest in the highest environment risk groups, showing increased susceptibility to genetic risk. This study's strengths include the diversity of the All of Us study cohort, the consideration of SDoH themes, and the examination of key risk factors and their interrelationships. These elements collectively underscore the importance of integrating genetic and environmental data to develop more precise risk models, enhance personalized medicine, and ultimately reduce health disparities.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"296-313"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports.","authors":"Vishwanatha M Rao, Serena Zhang, Julian N Acosta, Subathra Adithan, Pranav Rajpurkar","doi":"10.1142/9789819807024_0006","DOIUrl":"10.1142/9789819807024_0006","url":null,"abstract":"<p><p>Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports. Working with board-certified radiologists, we developed error categories that capture common mistakes in both human and AI-generated reports. Our approach uses a novel sampling scheme to inject diverse errors while maintaining clinical plausibility. ReXErr demonstrates consistency across error categories and produces errors that closely mimic those found in real-world scenarios. This method has the potential to aid in the development and evaluation of report correction algorithms, potentially enhancing the quality and reliability of radiology reporting.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"70-81"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Astrocyte Reactivity Polygenic Risk Score May Predict Cognitive Decline in Alzheimer's Disease.","authors":"Jared M Phillips, Julie A Schneider, David A Bennett, Paul K Crane, Shannon L Risacher, Andrew J Saykin, Logan C Dumitrescu, Timothy J Hohman","doi":"10.1142/9789819807024_0035","DOIUrl":"10.1142/9789819807024_0035","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a polygenic disorder with a prolonged prodromal phase, complicating early diagnosis. Recent research indicates that increased astrocyte reactivity is associated with a higher risk of pathogenic tau accumulation, particularly in amyloid-positive individuals. However, few clinical tools are available to predict which individuals are likely to exhibit elevated astrocyte activation and, consequently, be susceptible to hyperphosphorylated tau-induced neurodegeneration. Polygenic risk scores (PRS) aggregate the effects of multiple genetic loci to provide a single, continuous metric representing an individual's genetic risk for a specific phenotype. We hypothesized that an astrocyte activation PRS could aid in the early detection of faster clinical decline. Therefore, we constructed an astrocyte activation PRS and assessed its predictive value for cognitive decline and AD biomarkers (i.e., cerebrospinal fluid [CSF] levels of Aβ1-42, total tau, and p-tau181) in a cohort of 791 elderly individuals. The astrocyte activation PRS showed significant main effects on cross-sectional memory (β = -0.07, p = 0.03) and longitudinal executive function (β = -0.01, p = 0.03). Additionally, the PRS interacted with amyloid positivity (p.intx = 0.02), whereby indicating that amyloid burden modifies the association between the PRS and annual rate of language decline. Furthermore, the PRS was negatively associated with CSF Aβ1-42 levels (β = -3.4, p = 0.07) and interacted with amyloid status, such that amyloid burden modifies the association between the PRS and CSF phosphorylated tau levels (p.intx = 0.08). These findings suggest that an astrocyte activation PRS could be a valuable tool for early disease risk prediction, potentially enabling intervention during the interval between pathogenic amyloid and tau accumulation.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"488-503"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spherical Manifolds Capture Drug-Induced Changes in Tumor Cell Cycle Behavior.","authors":"Olivia Wen, Samuel C Wolff, Wayne Stallaert, Didong Li, Jeremy E Purvis, Tarek M Zikry","doi":"10.1142/9789819807024_0034","DOIUrl":"10.1142/9789819807024_0034","url":null,"abstract":"<p><p>CDK4/6 inhibitors such as palbociclib block cell cycle progression and improve outcomes for many ER+/HER2- breast cancer patients. Unfortunately, many patients are initially resistant to the drug or develop resistance over time in part due to heterogeneity among individual tumor cells. To better understand these mechanisms of resistance, we used multiplex, single-cell imaging to profile cell cycle proteins in ER+ breast tumor cells under increasing palbociclib concentrations. We then applied spherical principal component analysis (SPCA), a dimensionality reduction method that leverages the inherently cyclical nature of the high-dimensional imaging data, to look for changes in cell cycle behavior in resistant cells. SPCA characterizes data as a hypersphere and provides a framework for visualizing and quantifying differences in cell cycles across treatment-induced perturbations. The hypersphere representations revealed shifts in the mean cell state and population heterogeneity. SPCA validated expected trends of CDK4/6 inhibitor response such as decreased expression of proliferation markers (Ki67, pRB), but also revealed potential mechanisms of resistance including increased expression of cyclin D1 and CDK2. Understanding the molecular mechanisms that allow treated tumor cells to evade arrest is critical for identifying targets of future therapies. Ultimately, we seek to further SPCA as a tool of precision medicine, targeting treatments by individual tumors, and extending this computational framework to interpret other cyclical biological processes represented by high-dimensional data.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"473-487"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11687821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Earth Friendly Computation: Applying Indigenous Data Lifecycles in Medical and Sovereign AI.","authors":"Keolu Fox, Krystal Tsosie, Alex Ioannidis, Kaja Wasik, Alec Calac, Eric Dawson","doi":"10.1142/9789819807024_0053","DOIUrl":"10.1142/9789819807024_0053","url":null,"abstract":"<p><p>The following sections are included: Overview, Background & key terms, Earth Friendly Computation 574: Indigenous Data Sovereignty, Circular Systems, and Solarpunk Solutions for a Sustainable Future, AI in Point-of-Care: A Sustainable Healthcare Revolution at the Edge, Conclusion: The Future of Earth Friendly Computation, Acknowledgments, References.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"711-716"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting clinician implicit biases in diagnoses using proximal causal inference.","authors":"Kara Liu, Russ Altman, Vasilis Syrgkanis","doi":"10.1142/9789819807024_0024","DOIUrl":"10.1142/9789819807024_0024","url":null,"abstract":"<p><p>Clinical decisions to treat and diagnose patients are affected by implicit biases formed by racism, ableism, sexism, and other stereotypes. These biases reflect broader systemic discrimination in healthcare and risk marginalizing already disadvantaged groups. Existing methods for measuring implicit biases require controlled randomized testing and only capture individual attitudes rather than outcomes. However, the \"big-data\" revolution has led to the availability of large observational medical datasets, like EHRs and biobanks, that provide the opportunity to investigate discrepancies in patient health outcomes. In this work, we propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data. Specifically, our method uses proximal mediation to disentangle pathway-specific effects of a patient's sociodemographic attribute on a clinician's diagnosis decision. We test our method on real-world data from the UK Biobank. Our work can serve as a tool that initiates conversation and brings awareness to unequal health outcomes caused by implicit biases.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"330-345"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics.","authors":"Oishi Banerjee, Agustina Saenz, Kay Wu, Warren Clements, Adil Zia, Dominic Buensalido, Helen Kavnoudias, Alain S Abi-Ghanem, Nour El Ghawi, Cibele Luna, Patricia Castillo, Khaled Al-Surimi, Rayyan A Daghistani, Yuh-Min Chen, Heng-Sheng Chao, Lars Heiliger, Moon Kim, Johannes Haubold, Frederic Jonske, Pranav Rajpurkar","doi":"10.1142/9789819807024_0014","DOIUrl":"10.1142/9789819807024_0014","url":null,"abstract":"<p><p>Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology reports across diverse hospitals. We develop ReXamine-Global, a LLM-powered, multi-site framework that tests metrics across different writing styles and patient populations, exposing gaps in their generalization. First, our method tests whether a metric is undesirably sensitive to reporting style, providing different scores depending on whether AI-generated reports are stylistically similar to ground-truth reports or not. Second, our method measures whether a metric reliably agrees with experts, or whether metric and expert scores of AI-generated report quality diverge for some sites. Using 240 reports from 6 hospitals around the world, we apply ReXamine-Global to 7 established report evaluation metrics and uncover serious gaps in their generalizability. Developers can apply ReXamine-Global when designing new report evaluation metrics, ensuring their robustness across sites. Additionally, our analysis of existing metrics can guide users of those metrics towards evaluation procedures that work reliably at their sites of interest.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"185-198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}