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检索条件"任意字段=6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015"

共 237 条记录，以下是41-50 订阅

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Diagnosing PTSD Using Electronic Medical Records from Canadian Primary Care Data 19

Diagnosing PTSD Using Electronic Medical Records from Canadi...

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6th international conference on Networking, Systems and Security (NSysS)

作者： Kaczmarek, Emily Salgo, Alexander Zafari, Hasan Kosowan, Leanne Singer, Alexander Zulkernine, Farhana Queens Univ Sch Comp Kingston ON Canada Univ Manitoba Dept Family Med Coll Med Winnipeg MB Canada

ISBN: (纸本)9781450376990

Post-traumatic stress disorder (PTSD) can be a debilitating condition and early intervention can be instrumental in preventing patients' suffering. Identifying patients at risk for PTSD is challenging because of the limitations of the available data set, variations in the symptoms of PTSD for different patients, and misdiagnosis due to symptoms being shared with other conditions. In this preliminary study, we explore a small set of structured primary care data extracted from patients' electronic medical records (EMR) from Manitoba, Canada. this data has a small subset of PTSD positive cases, and is used to assess the feasibility of applying machine learning algorithms to diagnose PTSD. We developed three supervised machine learning models, a multilayered perceptron artificial neural network (ANN) model, a support vector machine (SVM), and a random forest classifier (RF) to identify PTSD patients using 890 patients' records. these methods obtained 0.79, 0.78, and 0.83 AUC respectively, which are better than all of the previous work that used EMR data having comparable size as our data. this study is geared towards understanding the primary care standard for PTSD patients in Canada in general and military-veteran population and developing a case definition for PTSD. this initial result demonstrates that an automated PTSD screening tool can be developed based on historical medical data for further study. In our ongoing work, we are exploring the providers' chart notes from the EMR data, which is unstructured text data, to improve the model accuracy and understand the progression of PTSD.

关键词： PTSD detection machine learning bioinformatics psychology artificial neural networks SVM random forests

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A Knowledge Based Self-Adaptive Differential Evolution Algorithm for Protein Structure Prediction 19th

A Knowledge Based Self-Adaptive Differential Evolution Algor...

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19th Annual international conference on Computational Science (ICCS)

作者： Narloch, Pedro H. Dorn, Marcio Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9783030227449;9783030227432

Tertiary protein structure prediction is one of the most challenging problems in Structural bioinformatics, and it is a NP-Complete problem in computational complexity theory. the complexity is related to the significant number of possible conformations a single protein can assume. Metaheuristics became useful algorithms to find feasible solutions in viable computational time since exact algorithms are not capable. However, these stochastic methods are highly-dependent from parameter tuning for finding the balance between exploitation (local search refinement) and exploration (global exploratory search) capabilities. thus, self-adaptive techniques were created to handle the parameter definition task, since it is time-consuming. In this paper, we enhance the Self-Adaptive Differential Evolution with problem-domain knowledge provided by the angle probability list approach, comparing it with every single mutation we used to compose our set of mutation operators. Moreover, a population diversity metric is used to analyze the behavior of each one of them. the proposed method was tested with ten protein sequences with different folding patterns. Results obtained showed that the self-adaptive mechanism has a better balance between the search capabilities, providing better results in regarding root mean square deviation and potential energy than the non-adaptive single-mutation methods.

关键词： Protein structure prediction Self-Adaptive Differential Evolution Structural bioinformatics Knowledge-based methods

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Workshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data, FAIR 2019, 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis, MREBA 2019, 2nd international Workshop on Empirical methods in Conceptual Modeling, EmpER 2019, 8th international Workshop on Modeling and Management of Big Data, MoBiD19 2019 and 7th international Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th international conference on Conceptual Modeling, ER 2019

Workshop on Conceptual Modeling, Ontologies and Metadata Man...

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Workshop on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data, FAIR 2019, 6th Workshop on Conceptual Modeling in Requirements Engineering and Business Analysis, MREBA 2019, 2nd international Workshop on Empirical methods in Conceptual Modeling, EmpER 2019, 8th international Workshop on Modeling and Management of Big Data, MoBiD19 2019 and 7th international Workshop on Ontologies andConceptual Modelling, OntoCom 2019 held at the 38th international conference on Conceptual Modeling, ER 2019

ISBN: (纸本)9783030341459

the proceedings contain 24 papers. the special focus in this conference is on Conceptual Modeling, Ontologies and Metadata Management for FAIR Data. the topics include: Facilitating data exploration in industry 4.0;towards understanding aggressive behavior in residential care facilities using process mining;on complex value relations in hive;data capture and analyses from conversational devices in the homes of the elderly;GORO 2.0: Evolving an ontology for goal-oriented requirements engineering;using ontologies for comparing modeling techniques: Experience report;domain ontology for digital marketplaces;exploring semantics in clinical data interoperability;framework for construction and incremental maintenance of high-quality linked data mashup;Aligning DMBOK and open government with the FAIR data principles;a reference architecture for customizable marketplaces;Keyword search algorithm over large RDF datasets;representing the filter bubble: Towards a model to diversification in news;the ontoOO-method: An ontology-driven conceptual modeling approach for evolving the OO-method;stochastic models to improve E-news recommender systems;Exploring reproducibility and FAIR principles in data science using ecological niche modeling as a case study;realizing traceability from the business model to enterprise architecture;creation of multiple conceptual models from user stories – a natural language processing approach;towards a catalog of goals for strategic coopetition;early identification of potential distributed ledger technology business cases using e3value models;exploiting conceptual modeling for searching genomic metadata: a quantitative and qualitative empirical study;What are real JSON schemas like?: An empirical analysis of structural properties.

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6D Pose Estimation for Industrial Applications 2nd

6D Pose Estimation for Industrial Applications

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and Processing, ICIAP 2019

作者： Cunico, Federico Carletti, Marco Cristani, Marco Masci, Fabio Conigliaro, Davide Department of Computer Science University of Verona Verona Italy Genova Italy The Edge Company Srl Rimini Italy Humatics Srl Verona Italy

ISBN: (纸本)9783030307530

Object pose estimation is important for systems and robots to interact with the environment where the main challenge of this task is the complexity of the scene caused by occlusions and clutters. A key challenge is performing pose estimation leveraging on both RGB and depth information: prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. Traditionally, the pose estimation problem is tackled by matching feature points between 3D models and images. However, these methods require rich textured models. In recent years, the raising of deep learning has offered an increasing number of methods based on neural networks, such as DSAC++, PoseCNN, DenseFusion and SingleShotPose. In this work, we present a comparison between two recent algorithms, DSAC++ and DenseFusion, focusing on computational cost, performance and applicability in the industry. © 2019, Springer Nature Switzerland AG.

关键词： Textures

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6th international conference on Statistical Language and Speech Processing, SLSP 2018

6th International Conference on Statistical Language and Spe...

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6th international conference on Statistical Language and Speech Processing, SLSP 2018

ISBN: (纸本)9783030008093

the proceedings contain 16 papers. the special focus in this conference is on Statistical Language and Speech Processing. the topics include: Movie genre detection using topological data analysis;low-resource text classification using domain-adversarial learning;handling ellipsis in a spoken medical phraselator;knowledge transfer for active learning in textual anonymisation;studying the effects of text preprocessing and ensemble methods on sentiment analysis of brazilian portuguese tweets;text documents encoding through images for authorship attribution;DNN-based speech synthesis for arabic: Modelling and evaluation;phone-level embeddings for unit selection speech synthesis;disfluency insertion for spontaneous tts: Formalization and proof of concept;forced alignment of the phonologie du français contemporain corpus;a syllable structure approach to spoken language recognition;investigating a hybrid learning approach for robust automatic speech recognition;a comparison of adaptation techniques and recurrent neural network architectures;restoring punctuation and capitalization using transformer models.

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SpamTracer: Manual fake review detection for O2O commercial platforms by using geolocation features 14th

SpamTracer: Manual fake review detection for O2O commercial ...

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14th international conference on Information Security and Cryptology, Inscrypt 2018

作者： Deng, Ruoyu Ruan, Na Jin, Ruidong Lu, Yu Jia, Weijia Su, Chunhua Xu, Dandan Department of CSE Shanghai Jiao Tong University Shanghai China Division of CS University of Aizu Aizuwakamatsu Japan China Unicom Research Institute Beijing China

ISBN: (纸本)9783030142339

Nowadays, O2O commercial platforms are playing a crucial role in our daily purchases. However, some people are trying to manipulate the online market maliciously by opinion spamming, a kind of web fraud behavior like writing fake reviews, due to fame and profits, which will harm online purchasing environment and should be detected and eliminated. Moreover, manual fake reviewers are more deceptive compared with old web spambots. Although several efficient methods were proposed in the fake review detection field, the manual fake reviewers are also evolving rapidly. they imitate to be benign users to control the velocity of review fraud actions, and deceive the detection system. Our investigation presented that geolocation factor is potential and can well reflect the distinctions between fake reviewers and benign users. In this research, we analyzed the geolocations of shops in reviews, found the distinct distribution features of those in fake reviewers and benign users, and proposed a SpamTracer model that can identify fake reviewers and benign users by exploiting an improved HMM (Hidden Markov Model). Our experiment demonstrated that SpamTracer could achieve 71% accuracy and 76% recall in the unbalanced dataset, outperforming some excellent classical approaches in the aspect of stability. Furthermore, SpamTracer can help to analyze the regularities of review fraud actions. those regularities reflect the time and location in which online shops are likely to hire fake reviewers to increase their turnover. We also found that a small group of fake reviewers tend to work with plural shops located in a small business zone. © 2019, Springer Nature Switzerland AG.

关键词： Hidden Markov models

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Trigger words detection by integrating attention mechanism into Bi-LSTM neural network—A case study in PubMED-wide trigger words detection for pancreatic cancer 17th

Trigger words detection by integrating attention mechanism i...

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17th China National conference on Computational Linguistics, CCL 2018 and 6th international Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Zhou, Kaiyin Yao, Xinzhi Wang, Shuguang Kim, Jin-Dong Cohen, Kevin Bretonnel Chen, Ruiying Wang, Yuxing Xia, Jingbo College of Informatics Huazhong Agricultural University Wuhan China Hubei Key Laboratory of Agricultural Bioinformatics Wuhan China Tokyo Japan School of Medicine University of Colorado Denver Anschutz Medical Campus Aurora United States

ISBN: (纸本)9783030017156

A Bi-LSTM based encode/decode mechanism for named entity recognition was studied in this research. In the proposed mechanism, Bi-LSTM was used for encoding, an Attention method was used in the intermediate layers, and an unidirectional LSTM was used as decoder layer. By using element wise product to modify the conventional decoder layers, the proposed model achieved better F-score, compared with other three baseline LSTM-based models. For the purpose of algorithm application, a case study of causal gene discovery in terms of disease pathway enrichment was designed. In addition, the causal gene discovery rate of our proposed method was compared with another baseline methods. the result showed that trigger genes detection effectively increase the performance of a text mining system for causal gene discovery. © Springer Nature Switzerland AG 2018.

关键词： Decoding

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An Empirical Study of Word Sense Disambiguation for Biomedical Information Retrieval System 6th

An Empirical Study of Word Sense Disambiguation for Biomedic...

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6th international Work-conference on bioinformatics and Biomedical Engineering (IWBBIO)

作者： Rais, Mohammed Lachkar, Abdelmonaime USMBA Dept Elect & Comp Engn LISA ENSA Fes Morocco

ISBN: (纸本)9783319787220;9783319787237

Document representation is an important stage to ensure the indexation of biomedical document. the ordinary way to represent a text is a bag of words BoW, this Representation suffers from the lack of sense in resulting representations ignoring all semantics that reside in the original text;instead of, the Conceptualization using background knowledge enriches document representation models. three strategies can be used in order to realize the conceptualization task: Adding Concept, Partial Conceptualization, and Complete Conceptualization. While searching polysemic term corresponding senses in semantic resources, multiple matches are detected then introduce some ambiguities in the final document representation, three strategies for Disambiguation can be used: First Concept, All Concepts and Context-Based. SenseRelate is a well-known Context-Based algorithm, which uses a fixed window size and taking into consideration the distance weight on how far the terms in the context are from the target word. this may impact negatively on the yielded concepts or senses, we propose a simple modified version of SenseRelate algorithm namely NoDistanceSenseRelate, which simply ignore the distance that is the terms in the context will have the same distance weight. In order to evaluate the effect of the conceptualization strategies and Disambiguation strategies in the indexing process, in this study, several experiments have been conducted using OHSUMED corpus on a biomedical information retrieval system. the obtained results using OHSUMED corpus show that the Context-Based methods (SenseRelate and NoDistanceSenseRelate) outperform the others ones when applying Adding Concept Conceptualization strategy results using Biomedical Information retrieval system. the obtained results prove the evidence of adding the sense of concepts to the Term Representation in the IR process.

关键词： Natural language processing Biomedical Information Retrieval Word Sense Disambiguation Biomedical indexing methods Strategy of disambiguation Conceptualization Sense based indexing

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HmmUFOtu: An HMM and Phylogenetic Placement based Ultra-fast Taxonomic Assignment and OTU Picking Tool for Microbiome Amplicon Sequencing Studies 18

HmmUFOtu: An HMM and Phylogenetic Placement based Ultra-fast...

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9th ACM international conference on bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Zheng, Qi Meisel, Jacquelyn Bartow-McKenney, Casey Grice, Elizabeth A. Univ Penn Perelman Sch Med Dept Dermatol Philadelphia PA 19104 USA Univ Penn Genom & Computat Biol Program Dept Dermatol Philadelphia PA 19104 USA

ISBN: (纸本)9781450357944

Over the last decade, joint advances in next-generation sequencing technology and bioinformatics pipelines have dramatically improved our understanding of host-associated and environmental microbiota. Standard microbiome community analysis typically involves amplicon sequencing of the prokaryotic 16S rRNA gene. these sequences are then clustered into operational taxonomic units (OTUs) for downstream diversity analyses, but also to reduce computational burden and allow for rapid analysis of datasets. Taxonomy is then assigned to all reads of an OTU, based on the assignment of a representative read. Although straightforward in principle, present methods often rely on heuristics while constructing (or "picking") OTUs to avoid computationally expensive algorithms, and ignore the prior knowledge of microbial phylogeny to further reduce the computational complexity. Here, we present HmmUFOtu, a novel tool for processing 16S rRNA sequences that addresses major limitations of current OTU picking and taxonomy assignment methods. HmmUFOtu relies on rapid per-read phylogenetic placement, followed by OTU picking and taxonomic assignment based on the phylogeny of known taxa. By benchmarking on simulated, mock community, and real datasets, we show that HmmUFOtu achieves high assignment accuracy, sensitivity, specificity and precision, even at species-level resolution. Compared to standard pipelines, HmmUFOtu more accurately recapitulates community diversity and composition. HmmUFOtu can perform taxonomic assignment in a species-resolution reference tree with ~ 200,000 nodes for 1 million 16S sequencing reads within 6 hours on a modest Linux workstation with 16 processors and 32 GB RAM. HmmUFOtu is written in C++98 and freely available at https://***/Grice-Lab/HmmUFOtu/.

关键词： Microbiome 16S rRNA gene FM-index HMM profile alignment phylogenetic placement taxonomic assignment Operational Taxonomic Unit Dirichlet models DNA substitution models Discrete Gamma models among-site rate variation

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6th international conference on ABZ conference on ASM, Alloy, B, TLA, VDM, and Z, ABZ 2018

6th International Conference on ABZ Conference on ASM, Alloy...

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6th international conference on ABZ conference on ASM, Alloy, B, TLA, VDM, and Z, ABZ 2018

ISBN: (纸本)9783319912707

the proceedings contain 35 papers. the special focus in this conference is on ASM, Alloy, B, TLA, VDM, and Z. the topics include: Proof-based approach to hybrid systems development: Dynamic logic and event-B;issues in automated urban train control: ‘Tackling’ the rugby club problem;clarification of ambiguity for the simple authentication and security layer;systematic refinement of abstract state machines with higher-order logic;refinement of timing constraints for concurrent tasks with scheduling;verifiable code generation from scheduled event-B models;the hybrid ERTMS/ETCS level 3 case study;Modeling the hybrid ERTMS/ETCS level 3 standard using a formal requirements engineering approach;Modelling the hybrid ERTMS/ETCS level 3 case study in Spin;distributed adaptive systems: theory, specification, reasoning;Using a formal B model at runtime in a demonstration of the ETCS hybrid level 3 concept with real trains;Validating the hybrid ERTMS/ETCS level 3 concept with electrum;the ABZ-2018 case study with event-B;Diagram-led formal modelling using iUML-B for hybrid ERTMS level 3;An event-B model of the hybrid ERTMS/ETCS level 3 standard;asmetaA: Animator for abstract state machines;formal specification of the semantics of control state diagrams;Capturing membrane computing by ASMs;Towards creating a DSL facilitating modelling of dynamic access control in event-B;state-based formal methods in scientific computation;on B and event-B: Principles, success and challenges;proposition of an action layer for electrum;insulin pump: Modular modeling of hybrid systems using event-B;an automation-friendly set theory for the B method;modelling dynamic data structures with the B method.

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