Health data science最新文献_第2页

Association between abortion and all-cause and cause-specific premature mortality: a prospective cohort study from the UK Biobank 人工流产与全因和特定原因过早死亡之间的关系：英国生物库前瞻性队列研究

Health data science Pub Date : 2024-05-19 DOI: 10.34133/hds.0147

Shaohua Yin, Yingying Yang, Qin Wang, Wei Guo, Qian He, Lei Yuan, Keyi Si

引用次数: 0

Association Between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study 成人体重指数与脑健康之间的关系：一项为期 16 年的基于人群的队列和孟德尔随机研究

Health data science Pub Date : 2024-03-01 DOI: 10.34133/hds.0087

Han Lv, Na Zeng, Mengyi Li, Jing Sun, Ning Wu, Mingze Xu, Qian Chen, Xinyu Zhao, Shuohua Chen, Wenjuan Liu, Xiaoshuai Li, Pengfei Zhao, Max Wintermark, Ying Hui, Jing Li, Shouling Wu, Zhenchang Wang

引用次数: 0

Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data. 学者在讨论 COVID-19 问题时的反应速度是否快于谷歌趋势？文本大数据的一种方法。

Health data science Pub Date : 2024-02-26 eCollection Date: 2024-01-01 DOI: 10.34133/hds.0116

Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So

{"title":"Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data.","authors":"Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So","doi":"10.34133/hds.0116","DOIUrl":"10.34133/hds.0116","url":null,"abstract":"Background: The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process. Nevertheless, due to the abundance of data, there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data. Methods: Using Oxford COVID-19 Government Response Tracker, we categorize the policy responses into 6 different categories: (a) containment and closure, (b) health systems, (c) vaccines, (d) economic, (e) country, and (f) others. We proposed a novel research framework to compare the response times of the scholars and the general public. To achieve this, we analyzed more than 400,000 research abstracts published over the past 2.5 years, along with text information from Google Trends as a proxy for topics of public concern. We introduced an innovative text-mining method: coherent topic clustering to analyze the huge number of abstracts. Results: Our results show that the research abstracts not only discussed almost all of the COVID-19 issues earlier than Google Trends did, but they also provided more in-depth coverage. This should help policymakers identify core COVID-19 issues and act earlier. Besides, our clustering method can better reflect the main messages of the abstracts than a recent advanced deep learning-based topic modeling tool. Conclusion: Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10895931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133416","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

Toward Unified AI Drug Discovery with Multimodal Knowledge. 利用多模态知识实现统一的人工智能药物发现。

Health data science Pub Date : 2024-02-23 eCollection Date: 2024-01-01 DOI: 10.34133/hds.0113

Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, Zaiqing Nie

{"title":"Toward Unified AI Drug Discovery with Multimodal Knowledge.","authors":"Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, Zaiqing Nie","doi":"10.34133/hds.0113","DOIUrl":"10.34133/hds.0113","url":null,"abstract":"Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10886071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133417","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

Identification and analysis of sex-biased copy number alterations 性别差异拷贝数改变的鉴定和分析

Health data science Pub Date : 2024-02-21 DOI: 10.34133/hds.0121

Chenhao Zhang, Yang Yang, Qinghua Cui, Dongyu Zhao, Chunmei Cui

引用次数: 0

Large-scale machine learning analysis reveals DNA-methylation and gene-expression response signatures for gemcitabine-treated pancreatic cancer 大规模机器学习分析揭示了吉西他滨治疗胰腺癌的DNA甲基化和基因表达反应特征

Health data science Pub Date : 2023-12-12 DOI: 10.34133/hds.0108

Adeolu Z Ogunleye, Chayanit Piyawajanusorn, G. Ghislat, Pedro Ballester

引用次数: 0

See your stories: Visualisation for Narrative Medicine 看看你的故事叙事医学的可视化

Health data science Pub Date : 2023-12-04 DOI: 10.34133/hds.0103

Hua Ma, Xiaoru Yuan, Xu Sun, Glyn Lawson, Qingfeng Wang