检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Kowsari, Kamran Sali, Rasoul Khan, Marium N. Adorno, William Asad Ali, S. Moore, Sean R. Amadi, Beatrice C. Kelly, Paul Syed, Sana Brown, Donald E. Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States Aga Khan University Karachi Pakistan Tropical Gastroenterology and Nutrition group University of Zambia School of Medicine Lusaka Zambia Blizard Institute Barts and The London School of Medicine Queen Mary University of London London United Kingdom

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Risk prediction models for patients who leave without being seen 67

Risk prediction models for patients who leave without being ...

引用

67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

作者： Sun, Muyang Kang, Hyojung Data Science Institute University of Virginia CharlottesvilleVA22904 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States

ISBN: (纸本)9780983762461

In the U.S., approximately two percent of patients who visit an emergency department (ED) leave the system without being seen by care providers. Leaving without being seen (LWBS) is a serious issue in the ED because appropriate medical treatment may be delayed, and this delay can lead to poor patient health outcomes. Previous studies have examined various factors associated with LWBS in the ED. However, these studies tend to focus on developing descriptive models that estimate a probability of LWBS patients without considering patients' willingness to wait to see a care provider in the ED. The objective of this paper is to develop a new risk index that incorporates patients' ED length of stay (LOS). We compared the performance of this new index with the risk computed through a machine-learning method that does not include the time component. The result showed that the newly-introduced, time-sensitive risk index performs better at identifying LWBS patients compared to other prediction models considered in the study. This new index may help EDs identify patients who tend to leave the system after staying in the ED for a short amount of time and may help intervene promptly before these patients leave the system.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Deep learning for visual recognition of environmental enteropathy and celiac disease

arXiv

引用

arXiv 2019年

作者： Shrivastava, Aman Kant, Karan Sengupta, Saurav Kang, Sung-Jun Khan, Marium Asad Ali, S. Moore, Sean R. Amadi, Beatrice C. Kelly, Paul Brown, Donald E. Syed, Sana Data Science Institute University of Virginia CharlottesvilleVA22903 United States Department of Pediatrics University of Virginia CharlottesvilleVA United States Department of Pediatrics & Child Health Aga Khan University Karachi Pakistan Tropical Gastroenterology and Nutrition group University of Zambia School of Medicine Lusaka Zambia Blizard Institute Barts and The London School of Medicine Queen Mary University of London London United Kingdom Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model. Copyright © 2019, The Authors. All rights reserved.

关键词： Biopsy

来源：评论

学校读者我要写书评

暂无评论

JTAV: Jointly learning social media content representation by fusing textual, acoustic, and visual features

arXiv

引用

arXiv 2018年

作者： Liang, Hongru Wang, Haozheng Wang, Jun You, Shaodi Sun, Zhe Wei, Jin-Mao Yang, Zhenglu CCCE Nankai University Tianjin China College of Mathematics and Statistics Science Ludong University China Data61-CSIRO Australian National University Canberra Australia RIKEN Head Office for Information Systems and CybersecurityComputational Engineering Applications Unit Japan

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin. © 2018, CC BY.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Natural Language Interfaces to Domain Specific Knowledge Bases: An Illustration for Querying Elements of the Periodic Table

Natural Language Interfaces to Domain Specific Knowledge Bas...

引用

IEEE International Conference on Cognitive Informatics

作者： Mukesh Kumar Rohil Rohan Kumar Rohil Divyesakshi Rohil Anurag Runthala Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani India Department of Mathematics Birla Institute of Technology and Science Pilani Pilani India Department of Chemistry Birla Institute of Technology and Science Pilani Pilani India Dept. of Data Protection and Governance Engineering Veritas Technologies LLC Mountain View USA

ISBN: (纸本)9781538633618

The task of providing Natural Language Interface (NLI) to any domain specific knowledge base is much demanding despite (potentially) favorable factors like low volume of vocabulary, unambiguous and precise meaning of words, less number of relations among the entities, etc. The simplification of this task has been proposed and presented in this research. The authors have made a successful effort to develop an NLI system to answer the user's simple queries (in English) about the properties of chemical elements and their grouping in the Periodic Table. Adding to the ease, the user is not required to know anything about the structure of the knowledge base of the elements, since the software is implemented (using Logic Programming constructs) in Prolog wherein program and data are treated indistinguishably. Firstly, the system accepts a query and subsequently, it can analyze and understand the query, if the query contains all words within the domain specific vocabulary. Finally, it efficiently searches the knowledge base to answer the query, by reducing search space using artificial intelligence techniques (like symbolic manipulation). If the query is not understood by the system, it reports to the user the words not available in the knowledge base and the particular relations among the entities which could not be set. The knowledge base (~150 KB) contains the properties of chemical elements, their arrangement in Periodic Table and the inter-relationships among these properties. In a nutshell, the research suggests that to develop an NLI to a domain specific knowledge base, it is better to develop a parser capable of handling the entities and their interrelationships as understood in the domain; hence, only little is to be coded for the various grammars, languages, transition networks, etc.

关键词： Natural language processing Knowledge based systems Handheld computers Cognitive informatics Cognitive systems

来源：评论

学校读者我要写书评

暂无评论

GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization

引用

ACM Transactions on information systems 2018年第3期36卷 33-33页

作者： Lian, Defu Zheng, Kai Ge, Yong Cao, Longbing Chen, Enhong Xie, Xing Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Management Information Systems University of Arizona TucsonAZ85721 United States Advanced Analytics Institute University of Technology Sydney NSW Australia School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Microsoft Research Beijing100080 China

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data;Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collectivematrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space. © 2018 ACM.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

引用

Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

来源：评论

学校读者我要写书评

暂无评论

A Direct Proof of Improved Biased Random Walk with Gastric Cancer dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

引用

International Conference on Applied Mathematics & Computer science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

来源：评论

学校读者我要写书评

暂无评论

Low-resolution face recognition in the wild via selective knowledge distillation

arXiv

引用

arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Community assessment of methods to deconvolve cellular composition from bulk gene expression

引用

Nature communications 2024年第1期15卷 9783页

作者： Brian S White Aurélien de Reyniès Aaron M Newman Joshua J Waterfall Andrew Lamb Florent Petitprez Yating Lin Rongshan Yu Martin E Guerrero-Gimenez Sergii Domanskyi Gianni Monaco Verena Chung Jineta Banerjee Daniel Derrick Alberto Valdeolivas Haojun Li Xu Xiao Shun Wang Frank Zheng Wenxian Yang Carlos A Catania Benjamin J Lang Thomas J Bertus Carlo Piermarocchi Francesca P Caruso Michele Ceccarelli Thomas Yu Xindi Guo Julie Bletz John Coller Holden Maecker Caroline Duault Vida Shokoohi Shailja Patel Joanna E Liliental Stockard Simon Julio Saez-Rodriguez Laura M Heiser Justin Guinney Andrew J Gentles Sage Bionetworks Seattle WA USA. The Jackson Laboratory for Genomic Medicine Farmington CT USA. Centre de Recherche des Cordeliers INSERM U1138 Université Paris Cité Paris France. Institute for Stem Cell Biology and Regenerative Medicine Stanford University Stanford CA USA. Department of Biomedical Data Science Stanford University Stanford CA USA. INSERM U830 and Translational Research Department Institut Curie PSL Research University Paris France. Programme Cartes d'Identité des Tumeurs Ligue Nationale Contre le Cancer Paris France. MRC Centre for Reproductive Health the Queen's Medical Research Institute University of Edinburgh Edinburgh United Kingdom. Xiamen University Xiamen Fujian China. Institute of Biochemistry and Biotechnology School of Medicine National University of Cuyo Mendoza Argentina. Michigan State University East Lansing MI USA. BIOGEM Institute of Molecular Biology and Genetics Ariano Irpino AV Italy. Department of Biomedical Engineering Knight Cancer Institute Oregon Health & Science University Portland OR USA. Heidelberg University Faculty of Medicine Heidelberg Germany. Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany. Aginome Scientific Xiamen Fujian China. Department of Pathology Cancer Hospital Chinese Aacdemy of Medical Science Beijing China. AmoyDx Xiamen Fujian China. Laboratory of Intelligent Systems (LABSIN) Engineering School National University of Cuyo Mendoza Argentina. Department of Radiation Oncology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Electrical Engineering and Information Technology (DIETI) University of Naples "Federico II" Ariano Irpino AV Italy. Sylvester Comprehensive Cancer Center Department of Public Health Sciences University of Miami Miller School of Medicine Miami Florida USA. Stanford Functional Genomics Facility Stanford University School of Medicine Stanford CA USA. Institute fo

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：