Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献_第6页

A linked data graph approach to integration of immunological data. 免疫数据整合的关联数据图方法。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8982986

Syed Ahmad Chan Bukhari, Jeff Mandell, Steven H Kleinstein, Kei-Hoi Cheung

{"title":"A linked data graph approach to integration of immunological data.","authors":"Syed Ahmad Chan Bukhari, Jeff Mandell, Steven H Kleinstein, Kei-Hoi Cheung","doi":"10.1109/bibm47256.2019.8982986","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8982986","url":null,"abstract":"Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"1742-1749"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm47256.2019.8982986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39566086","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

Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data. 基于可穿戴传感器数据的集成特征选择的连续疼痛评估。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8983282

Fan Yang, Tanvi Banerjee, Mark J Panaggio, Daniel M Abrams, Nirmish R Shah

{"title":"Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data.","authors":"Fan Yang, Tanvi Banerjee, Mark J Panaggio, Daniel M Abrams, Nirmish R Shah","doi":"10.1109/bibm47256.2019.8983282","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8983282","url":null,"abstract":"Sickle cell disease (SCD) is a red blood cell disorder complicated by lifelong issues with pain. Management of SCD related pain is particularly challenging due to its subjective nature. Hence, the development of an objective automatic pain assessment method is critical to pain management in SCD. In this work, we developed a continuous pain assessment model using physiological and body movement sensor signals collected from a wearable wrist-worn device. Specifically, we implemented ensemble feature selection methods to select robust and stable features extracted from wearable data for better understanding of pain. Our experiments showed that the stability of feature selection methods could be substantially increased by using the ensemble approach. Since different ensemble feature selection methods prefer varying feature subsets for pain estimation, we further utilized stacked generalization to maximize the information usage contained in the selected features from different methods. Using this approach, our best performing model obtained the root-mean-square error of 1.526 and the Pearson correlation of 0.618 for continuous pain assessment. This indicates that subjective pain scores can be estimated using objective wearable sensor data with high precision.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"569-576"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm47256.2019.8983282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38261293","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: 4

Midline Shift vs. Mid-Surface Shift: Correlation with Outcome of Traumatic Brain Injuries. 中线移位与中线表面移位:与创伤性脑损伤预后的相关性。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8983159

Cheng Jiang, Jie Cao, Craig Williamson, Negar Farzaneh, Venkatakrishna Rajajee, Jonathan Gryak, Kayvan Najarian, S M Reza Soroushmehr

引用次数: 2

Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. 危重儿童多器官功能障碍综合征的时间趋势表型分析

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8983126

Emily Kunce Stroup, Yuan Luo, L Nelson Sanchez-Pinto

{"title":"Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children.","authors":"Emily Kunce Stroup, Yuan Luo, L Nelson Sanchez-Pinto","doi":"10.1109/bibm47256.2019.8983126","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8983126","url":null,"abstract":"Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"968-972"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm47256.2019.8983126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25581737","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: 4

Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification. 分层自适应多任务学习框架的患者诊断和诊断类别分类。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8983298

Salim Malakouti, Milos Hauskrecht

{"title":"Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification.","authors":"Salim Malakouti, Milos Hauskrecht","doi":"10.1109/bibm47256.2019.8983298","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8983298","url":null,"abstract":"The problems a patient suffers from can be summarized in terms of a list of patient diagnoses. The diagnoses are typically organized in a hierarchy (or a lattice structure) in which many different low-level diagnoses are covered by one or more diagnostic categories. An interesting machine learning problem is related to learning of a wide range of diagnostic models (at different levels of abstraction) that can automatically assign a diagnosis or a diagnostic category to a specific patient. While one can always approach this problem by learning models for each diagnostic task independently, an interesting open question is how one can leverage the knowledge of a diagnostic hierarchy to improve the classification and outperform independent diagnostic models. In this work, we study this problem by designing a new hierarchical classification learning framework in which multiple diagnostic classification targets are explicitly related via diagnostic hierarchy relations. By conducting experiments on MIMIC-III data and ICD-9 diagnosis hierarchy, we demonstrate that our framework leads to improved classification performance on individual diagnostic tasks when compared to independently learned diagnostic models. This improvement is stronger for diagnoses with a low prior and smaller number of positive training examples.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm47256.2019.8983298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38818763","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: 10

Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs. 同步多特征心搏跟踪：减少重症监护室误报率的表征学习方法。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2019-11-01 Epub Date: 2020-02-06 DOI: 10.1109/bibm47256.2019.8983408

Behzad Ghazanfari, Sixian Zhang, Fatemeh Afghah, Nathan Payton-McCauslin

{"title":"Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs.","authors":"Behzad Ghazanfari, Sixian Zhang, Fatemeh Afghah, Nathan Payton-McCauslin","doi":"10.1109/bibm47256.2019.8983408","DOIUrl":"10.1109/bibm47256.2019.8983408","url":null,"abstract":"The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rule-based and machine learning-based techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the state-of-the-art works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the \"2015 Physionet computing in cardiology challenge\" dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5 %, and 90.8 % in terms of sensitivity, specificity, and the challenge's score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"2350-2355"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552433/pdf/nihms-1635259.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38493524","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

Read cloud sequencing elucidates microbiome dynamics in a hematopoietic cell transplant patient. 读取云测序阐明了造血细胞移植患者的微生物组动力学。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2018-12-01 Epub Date: 2019-01-24 DOI: 10.1109/bibm.2018.8621297

Joyce Kang, Benjamin Siranosian, Eli Moss, Tessa Andermann, Ami Bhatt

{"title":"Read cloud sequencing elucidates microbiome dynamics in a hematopoietic cell transplant patient.","authors":"Joyce Kang, Benjamin Siranosian, Eli Moss, Tessa Andermann, Ami Bhatt","doi":"10.1109/bibm.2018.8621297","DOIUrl":"https://doi.org/10.1109/bibm.2018.8621297","url":null,"abstract":"Low intestinal microbial diversity, often leading to domination of the intestine by a single organism, is associated with poor outcomes following hematopoietic cell transplantation (HCT). Understanding how certain organisms achieve domination in the intestine is limited by current metagenomic sequencing technologies, which are typically unable to reconstruct complete genome drafts without bacterial isolation and culture. Recently, we developed a metagenomic read cloud sequencing approach that provides significantly improved genome drafts for individual organisms compared to conventional short-read sequencing methods. Here, we apply read cloud sequencing to four longitudinal stool samples collected from an HCT patient before and after heavy antibiotic exposure. During this time period, the patient experienced Escherichia coli gut domination and an E. coli bloodstream infection. We find that read clouds enable the placement of multiple copies of antibiotic resistance genes both within and across genomes, and the presence of resistance genes correlates with the timing of antibiotics administered to the patient. Comparative genomic analysis reveals that the E. coli bloodstream infection likely originated from the gut. The pre-transplant E. coli genome harbors 46 known resistance genes, whereas all other organisms from the pre-transplant time point contain 5 or fewer resistance genes, supporting a model in which the E. coli outgrowth was a result of selection by heavy antibiotic exposure. This case study highlights the application of metagenomic read cloud sequencing in a clinical context to elucidate the genomic underpinnings of microbiome dynamics under extreme selective pressures.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2018 ","pages":"234-241"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm.2018.8621297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25590158","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

Extended Analysis of Topological-Pattern-Based Ontology Enrichment. 基于拓扑模式的本体充实扩展分析。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2018-12-01 Epub Date: 2019-01-24 DOI: 10.1109/BIBM.2018.8621564

Zhe He, Vipina Kuttichi Keloth, Yan Chen, James Geller

{"title":"Extended Analysis of Topological-Pattern-Based Ontology Enrichment.","authors":"Zhe He, Vipina Kuttichi Keloth, Yan Chen, James Geller","doi":"10.1109/BIBM.2018.8621564","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621564","url":null,"abstract":"Maintenance of biomedical ontologies is difficult. We have previously developed a topological-pattern-based method to deal with the problem of identifying concepts in a reference ontology that could be of interest for insertion into a target ontology. Assuming that both ontologies are parts of the Unified Medical Language System (UMLS), the method suggests approximate locations where the target ontology could be extended with new concepts from the reference ontology. However, the final decision about each concept has to be made by a human expert. In this paper, we describe the universe of cross-ontology topological patterns in quantitative terms. We then present a theoretical analysis of the number of potential placements of reference concepts in a path in a target ontology, allowing for new cross-ontology synonyms. This provides a rough estimate of what expert resources need to be allocated for the task. One insight in previous work on this topic was the large percentage of cases where importing concepts was impossible, due to a configuration called \"alternative classification.\" In this paper, we confirm this observation. Our target ontology is the National Cancer Institute thesaurus (NCIt). However, the methods can be applied to other pairs of ontologies with hierarchical relationships from the UMLS.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2018 ","pages":"1641-1648"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2018.8621564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37217069","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: 4

Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms. 基于特征分解的电子冷冻层析成像显著性检测。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2018-12-01 Epub Date: 2019-01-24 DOI: 10.1109/BIBM.2018.8621363

Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu

{"title":"Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms.","authors":"Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu","doi":"10.1109/BIBM.2018.8621363","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621363","url":null,"abstract":"Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular structures at the submolecular resolution in close to the native state. However, due to the high degree of structural complexity and imaging limits, the automatic segmentation of cellular components from ECT images is very difficult. To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data. As an important step towards this goal, in this paper, we propose a saliency detection method that computes the likelihood that a subregion in a tomogram stands out from the background. Our method consists of four steps: supervoxel over-segmentation, feature extraction, feature matrix decomposition, and computation of saliency. The method produces a distribution map that represents the regions' saliency in tomograms. Our experiments show that our method can successfully label most salient regions detected by a human observer, and able to filter out regions not containing cellular components. Therefore, our method can remove the majority of the background region, and significantly speed up the subsequent processing of segmentation and recognition of cellular components captured by ECT.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2018 ","pages":"2467-2473"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2018.8621363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37334033","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: 7

Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective. 用通用数据模型实现便携式临床 NLP 系统--Lisp 的视角。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2018-12-01 Epub Date: 2019-01-24 DOI: 10.1109/bibm.2018.8621521

Yuan Luo, Peter Szolovits

{"title":"Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective.","authors":"Yuan Luo, Peter Szolovits","doi":"10.1109/bibm.2018.8621521","DOIUrl":"10.1109/bibm.2018.8621521","url":null,"abstract":"This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with in-line annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2018 ","pages":"461-466"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769694/pdf/nihms-1656127.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38762862","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