Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献_第3页

Using Twitter Data Analysis to Understand the Perceptions, Awareness, and Barriers to the Wide Use of Pre-Exposure Prophylaxis in the United States. 使用Twitter数据分析了解美国广泛使用暴露前预防的认知、意识和障碍。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI: 10.1109/bibm55620.2022.9995568

Arslan Erdengasileng, Shubo Tian, Sara S Green, Sylvie Naar, Zhe He

{"title":"Using Twitter Data Analysis to Understand the Perceptions, Awareness, and Barriers to the Wide Use of Pre-Exposure Prophylaxis in the United States.","authors":"Arslan Erdengasileng, Shubo Tian, Sara S Green, Sylvie Naar, Zhe He","doi":"10.1109/bibm55620.2022.9995568","DOIUrl":"https://doi.org/10.1109/bibm55620.2022.9995568","url":null,"abstract":"User-generated social media posts such as tweets can provide insights about the public's perception, cognitive, and behavioral responses to health-related issues. Pre-Exposure Prophylaxis (PrEP) is one of the most effective ways to reduce the risk of HIV infection. However, its utilization is low in the US, especially among populations disproportionately affected by HIV such as the age group of under 24 years old. It is therefore important to understand the barriers to the wider use of PrEP in the US using social media posts. In this study, we collected tweets from Twitter about PrEP in the past 4 years to identify such barriers by first identifying tweets about personal discussions, and then performing textual analysis using word analysis, UMLS semantic type analysis, and topic modeling. We found that the public often discussed advocacy, risks/benefits, access, pricing, insurance coverage, legislation, stigma, health education, and prevention of HIV. This result is consistent with the literature and can help identify strategies for promoting the use of PrEP, especially among young adults.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"3000-3007"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937556/pdf/nihms-1871903.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9549806","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: 1

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics. 基于深度学习的 MS2 特征检测，用于与数据无关的射枪蛋白质组学。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 Epub Date: 2023-01-02 DOI: 10.1109/bibm55620.2022.9995258

Jonathan He, Olivia Liu, Xuan Guo

{"title":"Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.","authors":"Jonathan He, Olivia Liu, Xuan Guo","doi":"10.1109/bibm55620.2022.9995258","DOIUrl":"10.1109/bibm55620.2022.9995258","url":null,"abstract":"Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"2342-2348"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457098/pdf/nihms-1874655.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10107810","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

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics. 细粒度分类特异性错误发现率控制在元蛋白质组学。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI: 10.1109/bibm55620.2022.9995401

Shengze Wang, Shichao Feng, Chongle Pan, Xuan Guo

{"title":"FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.","authors":"Shengze Wang, Shichao Feng, Chongle Pan, Xuan Guo","doi":"10.1109/bibm55620.2022.9995401","DOIUrl":"https://doi.org/10.1109/bibm55620.2022.9995401","url":null,"abstract":"Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"287-292"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998077/pdf/nihms-1868490.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9112293","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

AutoMed: Automated Medical Risk Predictive Modeling on Electronic Health Records. AutoMed：电子健康记录上的自动医疗风险预测建模。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI: 10.1109/bibm55620.2022.9995209

Suhan Cui, Jiaqi Wang, Xinning Gui, Ting Wang, Fenglong Ma

{"title":"AutoMed: Automated Medical Risk Predictive Modeling on Electronic Health Records.","authors":"Suhan Cui, Jiaqi Wang, Xinning Gui, Ting Wang, Fenglong Ma","doi":"10.1109/bibm55620.2022.9995209","DOIUrl":"10.1109/bibm55620.2022.9995209","url":null,"abstract":"Electronic health records (EHR) have been widely applied to various tasks in the medical domain such as risk predictive modeling, which aims to predict further health conditions by analyzing patients' historical EHR. Existing work mainly focuses on modeling the sequential and temporal characteristics of EHR data with advanced deep learning techniques. However, the network architectures of these models are all manually designed based on experts' prior knowledge, which largely impedes non-experts from exploring this task. To address this issue, in this paper, we propose a novel automated risk prediction model named AutoMed to automatically search the optimal model architecture for modeling the complex EHR data and improving the performance of the risk prediction task. In particular, we follow the idea of neural architecture search to design a search space that contains three separate searchable modules. Two of them are used for analyzing sequential and temporal features of EHR data, respectively. The third is to automatically fuse both features together. Besides these three modules, AutoMed contains an embedding module and a prediction module. All the three searchable modules are jointly optimized in the search stage to derive the optimal model architecture. In such a way, the model design can be automatically achieved with few human interventions. Experimental results on three real-world datasets show that AutoMed outperforms state-of-the-art baselines in terms of PR-AUC, F1, and Cohen's Kappa. Moreover, the ablation study shows that AutoMed can obtain reasonable model architectures and offer useful insights to the future risk prediction model design.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"948-953"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102833/pdf/nihms-1889654.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9379505","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

KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction. KIT-LSTM：用于临床风险持续预测的知识导向时间感知LSTM。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI: 10.1109/bibm55620.2022.9994931

Lucas Jing Liu, Victor Ortiz-Soriano, Javier A Neyra, Jin Chen

引用次数: 0

Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding. 基于图嵌入的多组学疾病网络的有效主题表示。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2022-12-01 DOI: 10.1109/bibm55620.2022.9995707

Sundous Hussein, Thao Vu, Leslie Lange, Russell P Bowler, Katerina J Kechris, Farnoush Banaei-Kashani

{"title":"Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding.","authors":"Sundous Hussein, Thao Vu, Leslie Lange, Russell P Bowler, Katerina J Kechris, Farnoush Banaei-Kashani","doi":"10.1109/bibm55620.2022.9995707","DOIUrl":"10.1109/bibm55620.2022.9995707","url":null,"abstract":"The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects. With extensive empirical evaluation using real-world multi-omics networks, we show that our proposed framework significantly outperforms existing and baseline methods in terms of subject representation accuracy, particularly when the multi-omics network representing the cohort is sparse and structured and therefore, more informative.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"1911-1918"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9916186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9274804","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

Deep ensemble learning over the microbial phylogenetic tree (DeepEn-Phy). 微生物系统发育树的深度集合学习（DeepEn-Phy）。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2021-12-01 DOI: 10.1109/bibm52615.2021.9669654

Wodan Ling, Youran Qi, Xing Hua, Michael C Wu

{"title":"Deep ensemble learning over the microbial phylogenetic tree (DeepEn-Phy).","authors":"Wodan Ling, Youran Qi, Xing Hua, Michael C Wu","doi":"10.1109/bibm52615.2021.9669654","DOIUrl":"10.1109/bibm52615.2021.9669654","url":null,"abstract":"Successful prediction of clinical outcomes facilitates tailored diagnosis and treatment. The microbiome has been shown to be an important biomarker to predict host clinical outcomes. Further, the incorporation of microbial phylogeny, the evolutionary relationship among microbes, has been demonstrated to improve prediction accuracy. We propose a phylogeny-driven deep neural network (PhyNN) and develop an ensemble method, DeepEn-Phy, for host clinical outcome prediction. The method is designed to optimally extract features from phylogeny, thereby take full advantage of the information in phylogeny while harnessing the core principles of phylogeny (in contrast to taxonomy). We apply DeepEn-Phy to a real large microbiome data set to predict both categorical and continuous clinical outcomes. DeepEn-Phy demonstrates superior prediction performance to existing machine learning and deep learning approaches. Overall, DeepEn-Phy provides a new strategy for designing deep neural network architectures within the context of phylogeny-constrained microbiome data.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2021 ","pages":"470-477"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875567/pdf/nihms-1860461.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10686576","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

Therapeutic Claims in Cannabidiol (CBD) Marketing Messages on Twitter. 推特上大麻二酚(CBD)营销信息的治疗声明。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2021-12-01 Epub Date: 2022-01-14 DOI: 10.1109/bibm52615.2021.9669404

Mohammad Soleymanpour, Sofia Saderholm, Ramakanth Kavuluru

{"title":"Therapeutic Claims in Cannabidiol (CBD) Marketing Messages on Twitter.","authors":"Mohammad Soleymanpour, Sofia Saderholm, Ramakanth Kavuluru","doi":"10.1109/bibm52615.2021.9669404","DOIUrl":"https://doi.org/10.1109/bibm52615.2021.9669404","url":null,"abstract":"Although the U.S. FDA has only approved exactly one cannabidiol (CBD) drug product (specifically to treat seizures), CBD products are proliferating rapidly through different modes of usage including food products, cosmetics, vaping pods, and supplements (typically, oils). Despite the FDA clearly warning consumers about unproven health claims made by manufacturers selling CBD products over the counter, the CBD market share was nearly 3 billion USD in 2020 and is expected to top 55 billion USD in 2028. In this context, it is important to assess the presence of health claims being made on social media, especially claims that are part of marketing messages. To this end, we collected over two million English tweets discussing CBD themes. We created a hand-labeled dataset and built machine learned classifiers to identify marketing tweets from regular tweets that may be generated by consumers. The best classifier achieved 85% precision, 83% recall, and 84% F-score. Our analyses showed that pain, anxiety disorders, sleep disorders, and stress are the four main therapeutic claims made constituting 31.67%, 27.11%, 13.77%, and 10.37% of all medical claims made on Twitter, respectively. Also, more than 93% of advertised CBD products are edibles or oil/tinctures. Our effort is the first to demonstrate the feasibility of surveillance of marketing claims for CBD products. We believe this could pave way for more explorations into this indispensable task in the current landscape of social media driven health (mis)information and communication.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"3083-3088"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794048/pdf/nihms-1772253.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39734700","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: 15

Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data. 利用纵向神经影像数据对阿尔茨海默病预后预测的可解释时间图神经网络。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2021-12-01 DOI: 10.1109/bibm52615.2021.9669504

Mansu Kim, Jaesik Kim, Jeffrey Qu, Heng Huang, Qi Long, Kyung-Ah Sohn, Dokyoon Kim, Li Shen

{"title":"Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.","authors":"Mansu Kim, Jaesik Kim, Jeffrey Qu, Heng Huang, Qi Long, Kyung-Ah Sohn, Dokyoon Kim, Li Shen","doi":"10.1109/bibm52615.2021.9669504","DOIUrl":"10.1109/bibm52615.2021.9669504","url":null,"abstract":"Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2021 ","pages":"1381-1384"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922159/pdf/nihms-1784457.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10617208","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

Wearable Sensor Gait Analysis of Fall Detection using Attention Network. 基于注意网络的可穿戴传感器跌倒检测步态分析。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2021-12-01 DOI: 10.1109/bibm52615.2021.9669795

Haben Yhdego, Jiang Li, Christopher Paolini, Michel Audette

{"title":"Wearable Sensor Gait Analysis of Fall Detection using Attention Network.","authors":"Haben Yhdego, Jiang Li, Christopher Paolini, Michel Audette","doi":"10.1109/bibm52615.2021.9669795","DOIUrl":"https://doi.org/10.1109/bibm52615.2021.9669795","url":null,"abstract":"The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2021 ","pages":"3137-3141"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10205066/pdf/nihms-1883205.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9892793","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: 5