Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献_第5页

Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access (PSB2024). 会议简介：生物计算中的数字健康技术数据：研究工作和扩大访问的考虑因素（PSB2024）。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Michelle Holko, Chris Lunt, Jessilyn Dunn

{"title":"Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access (PSB2024).","authors":"Michelle Holko, Chris Lunt, Jessilyn Dunn","doi":"","DOIUrl":"","url":null,"abstract":"Data from digital health technologies (DHT), including wearable sensors like Apple Watch, Whoop, Oura Ring, and Fitbit, are increasingly being used in biomedical research. Research and development of DHT-related devices, platforms, and applications is happening rapidly and with significant private-sector involvement with new biotech companies and large tech companies (e.g. Google, Apple, Amazon, Uber) investing heavily in technologies to improve human health. Many academic institutions are building capabilities related to DHT research, often in cross-sector collaboration with technology companies and other organizations with the goal of generating clinically meaningful evidence to improve patient care, to identify users at an earlier stage of disease presentation, and to support health preservation and disease prevention. Large research consortia, cross-sector partnerships, and individual research labs are all represented in the current corpus of published studies. Some of the large research studies, like NIH's All of Us Research Program, make data sets from wearable sensors available to the research community, while the vast majority of data from wearable sensors and other DHTs are held by private sector organizations and are not readily available to the research community. As data are unlocked from the private sector and made available to the academic research community, there is an opportunity to develop innovative analytics and methods through expanded access. This is the second year for this Session which solicited research results leveraging digital health technologies, including wearable sensor data, describing novel analytical methods, and issues related to diversity, equity, inclusion (DEI) of the research, data, and the community of researchers working in this area. We particularly encouraged submissions describing opportunities for expanding and democratizing academic research using data from wearable sensors and related digital health technologies.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"163-169"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075203","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}

引用次数: 0

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature. Clinfo.ai：利用科学文献回答医学问题的开源检索增强型大语言模型系统。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah

引用次数: 0

Statistical analysis of single-cell protein data. 单细胞蛋白质数据统计分析。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Brooke L Fridley, Simon Vandekar, Inna Chervoneva, Julia Wrobel, Siyuan Ma

引用次数: 0

Impact of Measurement Noise on Genetic Association Studies of Cardiac Function. 测量噪音对心功能遗传关联研究的影响。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Milos Vukadinovic, Gauri Renjith, Victoria Yuan, Alan Kwan, Susan C Cheng, Debiao Li, Shoa L Clarke, David Ouyang

{"title":"Impact of Measurement Noise on Genetic Association Studies of Cardiac Function.","authors":"Milos Vukadinovic, Gauri Renjith, Victoria Yuan, Alan Kwan, Susan C Cheng, Debiao Li, Shoa L Clarke, David Ouyang","doi":"","DOIUrl":"","url":null,"abstract":"Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"134-147"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075171","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}

引用次数: 0

LA-GEM: imputation of gene expression with incorporation of Local Ancestry. LA-GEM：结合当地血统的基因表达估算。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Mrinal Mishra, Layan Nahlawi, Yizhen Zhong, Tanima De, Guang Yang, Cristina Alarcon, Minoli A Perera

{"title":"LA-GEM: imputation of gene expression with incorporation of Local Ancestry.","authors":"Mrinal Mishra, Layan Nahlawi, Yizhen Zhong, Tanima De, Guang Yang, Cristina Alarcon, Minoli A Perera","doi":"","DOIUrl":"","url":null,"abstract":"Gene imputation and TWAS have become a staple in the genomics medicine discovery space; helping to identify genes whose regulation effects may contribute to disease susceptibility. However, the cohorts on which these methods are built are overwhelmingly of European Ancestry. This means that the unique regulatory variation that exist in non-European populations, specifically African Ancestry populations, may not be included in the current models. Moreover, African Americans are an admixed population, with a mix of European and African segments within their genome. No gene imputation model thus far has incorporated the effect of local ancestry (LA) on gene expression imputation. As such, we created LA-GEM which was trained and tested on a cohort of 60 African American hepatocyte primary cultures. Uniquely, LA-GEM include local ancestry inference in its prediction of gene expression. We compared the performance of LA-GEM to PrediXcan trained the same dataset (with no inclusion of local ancestry) We were able to reliably predict the expression of 2559 genes (1326 in LA-GEM and 1236 in PrediXcan). Of these, 546 genes were unique to LA-GEM, including the CYP3A5 gene which is critical to drug metabolism. We conducted TWAS analysis on two African American clinical cohorts with pharmacogenomics phenotypic information to identity novel gene associations. In our IWPC warfarin cohort, we identified 17 transcriptome-wide significant hits. No gene reached are prespecified significance level in the clopidogrel cohort. We did see suggestive association with RAS3A to P2RY12 Reactivity Units (PRU), a clinical measure of response to anti-platelet therapy. This method demonstrated the need for the incorporation of LA into study in admixed populations.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"341-358"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075175","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}

引用次数: 0

Practical Approaches to Enhancing Fairness, Social Responsibility and the Inclusion of Diverse Viewpoints in Biomedicine. 增强生物医学的公平性、社会责任感和多元观点包容性的实用方法。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Daphne O Martschenko, Nicole Martinez-Martin, Meghan Halley

引用次数: 0

Risk prediction: Methods, Challenges, and Opportunities. 风险预测：方法、挑战和机遇。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Ruowang Li, Rui Duan, Lifang He, Jason H Moore

引用次数: 0

Session Introduction: Drug-repurposing and discovery in the era of "big" real-world data: how the incorporation of observational data, genetics, and other -omic technologies can move us forward. 会议简介：大 "真实世界数据时代的药物再利用和发现：观察数据、遗传学和其他原子技术如何推动我们前进。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Megan M Shuey, Jacklyn N Hellwege, Nikhil Khankari, Marijana Vujkovic, Todd L Edwards

引用次数: 0

intCC: An efficient weighted integrative consensus clustering of multimodal data. intCC：多模态数据的高效加权综合共识聚类。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Can Huang, Pei Fen Kuan

引用次数: 0

KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome. KombOver：基于 K 核心和 K 桁架的人类肠道微生物群扰动高效表征。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2024-01-01

Nicolae Sapoval, Marko Tanevski, Todd J Treangen

{"title":"KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome.","authors":"Nicolae Sapoval, Marko Tanevski, Todd J Treangen","doi":"","DOIUrl":"","url":null,"abstract":"The microbes present in the human gastrointestinal tract are regularly linked to human health and disease outcomes. Thanks to technological and methodological advances in recent years, metagenomic sequencing data, and computational methods designed to analyze metagenomic data, have contributed to improved understanding of the link between the human gut microbiome and disease. However, while numerous methods have been recently developed to extract quantitative and qualitative results from host-associated microbiome data, improved computational tools are still needed to track microbiome dynamics with short-read sequencing data. Previously we have proposed KOMB as a de novo tool for identifying copy number variations in metagenomes for characterizing microbial genome dynamics in response to perturbations. In this work, we present KombOver (KO), which includes four key contributions with respect to our previous work: (i) it scales to large microbiome study cohorts, (ii) it includes both k-core and K-truss based analysis, (iii) we provide the foundation of a theoretical understanding of the relation between various graph-based metagenome representations, and (iv) we provide an improved user experience with easier-to-run code and more descriptive outputs/results. To highlight the aforementioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring less than 10 minutes and 10 GB RAM per sample to process these data. Furthermore, we highlight how graph-based approaches such as k-core and K-truss can be informative for pinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at: https://github.com/treangenlab/komb.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"506-520"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075174","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}

引用次数: 0