Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics最新文献

{"title":"ArcheGEO","authors":"Huey-Eng Chua, L. Tucker-Kellogg, S. Bhowmick","doi":"10.1145/3535508.3545531","DOIUrl":"https://doi.org/10.1145/3535508.3545531","url":null,"abstract":"Transciptomic data stored in the Gene Expression Omnibus (GEO) serves thousands of queries per day, but a lack of standardized machine-readable metadata causes many searches to return irrelevant hits, which impede convenient access to useful data in the GEO repository. Here, we describe ArcheGEO, a novel end-to-end framework that improves results from the GEO Browser by automatically determining the relevance of these results. Unlike existing tools, ArcheGEO reports on the irrelevant results and provides reasoning for their exclusion. Such reasoning can be leveraged to improve annotations of metadata.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087432","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":"PRRSView","authors":"Michael A. Zeller, Anugrah Saxena, G. Trevisan, Aditi Sharma, D. Linhares, Karen M. Krueger, Jianqiang Zhang, P. Gauger","doi":"10.1145/3535508.3545105","DOIUrl":"https://doi.org/10.1145/3535508.3545105","url":null,"abstract":"Porcine reproductive and respiratory syndrome virus (PRRSV) is the most economically important swine pathogen in North America and is second globally only to African swine fever virus. PRRSV is a positive sense, single stranded RNA virus associated with reproductive disorder of sows and respiratory disease of pigs at all age. Diagnostic tests are commonly used to monitor the presence of PRRSV in swine populations including sequencing the open reading frame 5 (ORF5) gene to track the epidemiology of the virus and lateral introductions into a farm. PRRSView is a web portal created at the Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) to host analytical and phylogenetic tools related to PRRSV ORF5 sequences with the goal of assisting veterinarians and producers in evaluating the genetic diversity, spatial, and temporal aspects of ORF5 sequences maintained in the ISU VDL database. PRRSView works in conjunction with the broader Swine Disease Reporting System (SDRS) project to contextualize the ever changing patterns of PRRSV diversity, and supports interactive tools for veterinarians to analyze their sequence data compared to other sequences detected throughout the United States. The PRRSView homepage provides a phylogenetic overview of the sequences generated by the ISU VDL within the previous month, indicating the current strains detected in circulation. There are currently three ORF5 analytical tools available on PRRSView: a genetic sequence BLAST tool, a vaccine identity tool, and an RFLP tool. The ORF5 BLAST tool allows the users to submit their ORF5 gene sequences and returns up to 10 closely related sequences from the ISU VDL database, with metadata that includes the state, genetic lineage, RFLP, and identity to the query sequence. The vaccine identity tool allows users to quickly calculate the percent homology of their sequence(s) to five different PRRSV vaccines: Inglevac PRRS ATP, Inglevac PRRS MLV, Prime Pac PRRS, Fostera PRRS, and Prevacent PRRS, as well as the distance to the Lelystad strain. This tool also builds a neighbor joining tree with a set of curated strains to estimate the genetic lineage of the sequence, which is rendered in the web browser for viewing. Additionally, this tool will calculate the RFLP of the sequence, and the exact positions of the cut sites are shown when hovering over the RFLP value. The last analytical tool provided is the ORF5 RFLP tool, which quickly calculates the RFLP pattern of the input sequences. These analytical tools are designed to allow veterinarians and researchers to easily analyze their PRRSV ORF5 sequences against the expansive ISU VDL database to gain valuable epidemiologic information and comparative data regarding the genetic lineages and related metadata of the PRRSV circulating in a production system, while lowering the barrier of entry for use.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129493819","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":"TopographyNET","authors":"Lillian Zhu, Feng Zhu, J. Price","doi":"10.1145/3535508.3545533","DOIUrl":"https://doi.org/10.1145/3535508.3545533","url":null,"abstract":"We often find our minds drifting off a current task towards something else, a phenomenon known as mind wandering. Mind wandering can negatively impact performance of many tasks (e.g., learning). Thus, it is crucial to find a way to detect mind wandering. Using deep learning and electroencephalogram (EEG) seems very promising. EEG systems offer high temporal precision and accessibility, and deep learning can automatically extract features from EEG signals. However, three key challenges hinder deep learning performance: the dynamic and distributed nature of mind wandering, small EEG datasets, and diverse EEG systems. Existing deep learning solutions do not perform well on the small datasets and cannot use data from other EEG systems. We propose a novel deep learning model, TopographyNET, which 1) captures the dynamic and distributed properties through spatial and temporal processing via 2D topographic scalp maps and a recurrent neural network; 2) applies transfer learning to address the issue of small datasets using a pretrained image classification neural network on topographic scalp maps; and 3) represents data in a uniform format and thus enables the usage of EEG data from diverse systems. Compared to an existing solution, our approach achieves a much higher classification accuracy. In addition, we present the hyperparameter tuning process that helped us achieve a high classification accuracy.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114742271","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":"Timestamp analysis of mental health tweets of Twitter users along with COVID-19 confirmed cases","authors":"Sudha Tushara Sadasivuni, Yanqing Zhang","doi":"10.1145/3535508.3545543","DOIUrl":"https://doi.org/10.1145/3535508.3545543","url":null,"abstract":"Twitter users post tweets on many topics, emotions, and events. The technological advancement and ease of tweeting quicken people's interaction with social network sites. Engagement with tweets led to product promotion in many corporate companies. Many studies focused on understanding tweeting patterns for marketing, retweeting, getting noticed, and receiving feedback. The time of a tweet was used for marketing strategies. Domain-based tweet timestamp patterns helped corporates in their tweet schedules and attracted more customers for their products. We collected 2.3 million depressive, anti-depressive, and COVID-19 tweets for one year. Our analysis of these tweets results in detailed tweet patterns in different timings in a day and days in a week. The depressive tweets follow the diurnal pattern, whereas the anti-depressive tweets follow a similar trend with intermediate aberrations. We also classified the tweet keywords into three different types with their frequency and amplitude of tweet patterns. Analyzing multi-domain tweets to discover time series patterns related to human health will be helpful for the planning and execution of medical disaster preparedness and emergency teams.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127605521","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":"Using natural language processing on free-text clinical notes to identify patients with long-term COVID effects","authors":"Yuanda Zhu, A. Mahale, Kourtney Peters, Lejy Mathew, F. Giuste, B. Anderson, May D. Wang","doi":"10.1145/3535508.3545555","DOIUrl":"https://doi.org/10.1145/3535508.3545555","url":null,"abstract":"As of May 15th, 2022, the novel coronavirus SARS-COV-2 has infected 517 million people and resulted in more than 6.2 million deaths around the world. About 40% to 87% of patients suffer from persistent symptoms weeks or months after their original infection. Despite remarkable progress in preventing and treating acute COVID-19 conditions, the clinical diagnosis of long-term COVID remains difficult. In this work, we use free-text clinical notes and natural language processing (NLP) techniques to explore long-term COVID effects. We first obtain free-text clinical notes from 719 outpatient encounters representing patients treated by physicians at Emory Clinic to detect patterns in patients with long-term COVID symptoms. We apply state-of-the-art NLP frameworks to automatically identify patients with long-term COVID effects, achieving 0.881 recall (sensitivity) score for note-level prediction. We further interpret the prediction outcomes and discuss potential phenotypes. Our work aims to provide a data-driven solution to identify patients who have developed persistent symptoms after acute COVID infection. With this work, clinicians may be able to identify patients who have long-term COVID symptoms to optimize treatment.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"21 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124086339","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":"FiT","authors":"Aysegül Bumin, Anna M. Ritz, Donna K. Slonim, Tamer Kahveci, Kejun Huang","doi":"10.1145/3535508.3545527","DOIUrl":"https://doi.org/10.1145/3535508.3545527","url":null,"abstract":"While the focus of ITD 2022 was our hopes for a world without barriers and our role in building culture, understanding, and lasting peace, it seems we still can only hope for a world without conflict, crisis, exclusion, and disadvantage. Yet we have a role to play here, too. So often in panels, discussions, and presentations, we see the themes of solidarity, learning, sharing, and community coming through, along with the vital role of professional associations in supporting individuals. It is so President’s message","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123696689","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":"Analysis of impact of diabetes mellitus in Arkansas and U.S","authors":"Vinay Raj","doi":"10.1145/3535508.3545108","DOIUrl":"https://doi.org/10.1145/3535508.3545108","url":null,"abstract":"Diabetes mellitus is a metabolic disease characterized by abnormally high blood glucose levels. Today, about 15 % of the population has been diagnosed with diabetes in Arkansas. Nationally, diabetes affects about 8.7% of the population and is a leading cause of death in adults. An analysis of the impact of diabetes to Arkansas and the U.S over the years could elucidate significant trends and factors that could aid in combating and reducing the economic effects of this devastating disease. Trends in the utilization of health care services by studying health care costs and hospital utilization of diabetes patients with and without complications was examined both at the state and national level between the years 2006 and 2018. A high level of hospitalizations was seen in the age groups of 18--44 yrs and 45--64 yrs compared to the other age groups both in diabetes patients with and without complications in Arkansas and U.S. Levels of hospital use were higher among females in Arkansas while it was higher in men in the U.S. The prevalence of diabetes with complications shows an increasing trend in younger adults while the prevalence of diabetes without complications shows an increase in children and adolescents over the last few years. An increase in hospital costs is seen overall for patients with diabetes.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130770723","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":"LINgroups as a principled approach to compare and integrate multiple bacterial taxonomies","authors":"R. Mazloom, L. Pritchard, C. Brown, B. Vinatzer, L. Heath","doi":"10.1145/3535508.3545546","DOIUrl":"https://doi.org/10.1145/3535508.3545546","url":null,"abstract":"Traditional taxonomy provides a hierarchical organization of bacteria and archaea across taxonomic ranks from kingdom to subspecies. More recently, bacterial taxonomy has been more robustly quantified using comparisons of sequenced genomes, as in the Genome Taxonomy Database (GTDB), resolving down to genera and species. Such taxonomies have proven useful in many contexts, yet lack the flexibility and resolution of a more fine-grained approach. We apply our Life Identification Number (LIN) approach as a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. We utilize our existing concept of a LINgroup as an organizational concept for microorganisms that are closely related by overall genomic similarity, to help resolve some of the confusions and unforeseen negative effects of nomenclature changes of microbes due to genome-based reclassification. Our results obtained from experimentation demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126094191","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":"Session details: Systems biology","authors":"Alisa Yurovsky","doi":"10.1145/3552472","DOIUrl":"https://doi.org/10.1145/3552472","url":null,"abstract":"","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"136 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121067034","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":"Computing attractors of large-scale asynchronous boolean networks using minimal trap spaces","authors":"V. Trinh, K. Hiraishi, B. Benhamou","doi":"10.1145/3535508.3545520","DOIUrl":"https://doi.org/10.1145/3535508.3545520","url":null,"abstract":"Boolean Networks (BNs) play a crucial role in modeling, analyzing, and controlling biological systems. One of the most important problems on BNs is to compute all the possible attractors of a BN. There are two popular types of BNs, Synchronous BNs (SBNs) and Asynchronous BNs (ABNs). Although ABNs are considered more suitable than SBNs in modeling real-world biological systems, their attractor computation is more challenging than that of SBNs. Several methods have been proposed for computing attractors of ABNs. However, none of them can robustly handle large and complex models. In this paper, we propose a novel method called mtsNFVS for exactly computing all the attractors of an ABN based on its minimal trap spaces, where a trap space is a subspace of state space that no path can leave. The main advantage of mtsNFVS lies in opening the chance to reach easy cases for the attractor computation. We then evaluate mtsNFVS on a set of large and complex real-world models with crucial biologically motivations as well as a set of randomly generated models. The experimental results show that mtsNFVS can easily handle large-scale models and it completely outperforms the state-of-the-art method CABEAN as well as other recently notable methods.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122843009","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}