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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Kekeç, Taygun Tax, D.M.J. Pattern Recognition and Bioinformatics Laboratory Delft University of Technology Delft2628CD Netherlands

ISBN: (纸本)9781945626258

Word embedding models learn vectorial word representations that can be used in a variety of NLP applications. When training data is scarce, these models risk losing their generalization abilities due to the complexity of the models and the overfitting to finite data. We propose a regularized embedding formulation, called Robust Gram (RG), which penalizes overfitting by suppressing the disparity between target and context embeddings. Our experimental analysis shows that the RG model trained on small datasets generalizes better compared to alternatives, is more robust to variations in the training set, and correlates well to human similarities in a set of word similarity tasks. © 2016 Association for Computational Linguistics

关键词： Embeddings

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Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

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Intelligence-Based Medicine 2022年 6卷 100071-100071页

作者： Smit, J.M. Krijthe, J.H. Endeman, H. Tintu, A.N. de Rijke, Y.B. Gommers, D.A.M.P.J. Cremer, O.L. Bosman, R.J. Rigter, S. Wils, E.-J. Frenzel, T. Dongelmans, D.A. De Jong, R. Peters, M.A.A. Kamps, M.J.A. Ramnarain, D. Nowitzky, R. Nooteboom, F.G.C.A. De Ruijter, W. Urlings-Strop, L.C. Smit, E.G.M. Mehagnoul-Schipper, D.J. Dormans, T. De Jager, C.P.C. Hendriks, S.H.A. Achterberg, S. Oostdijk, E. Reidinga, A.C. Festen-Spanjer, B. Brunnekreef, G.B. Cornet, A.D. Van den Tempel, W. Boelens, A.D. Koetsier, P. Lens, J.A. Faber, H.J. karakus, A. Entjes, R. De Jong, P. Rettig, T.C.D. Arbous, M.S. Lalisang, R.C.A. Tonutti, M. De Bruin, D.P. Elbers, P.W.G. Van Bommel, J. Reinders, M.J.T. Intensive Care Erasmus University Medical Center Rotterdam Netherlands Pattern Recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands Clinical Chemistry Erasmus University Medical Center Rotterdam Netherlands Intensive Care UMC Utrecht Utrecht Netherlands Intensive Care OLVG Amsterdam Netherlands Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein Netherlands Intensive Care Franciscus Gasthuis Vlietland Rotterdam Netherlands Intensive Care Radboud University Medical Center Nijmegen Netherlands Intensive Care Amsterdam UMC Amsterdam Netherlands Intensive Care Bovenij Ziekenhuis Amsterdam Netherlands Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen Netherlands Intensive Care Catharina Ziekenhuis Eindhoven Eindhoven Netherlands Intensive Care ETZ Tilburg Tilburg Netherlands Intensive Care HagaZiekenhuis Den Haag Netherlands Intensive Care Laurentius Ziekenhuis Roermond Netherlands Intensive Care Northwest Clinics Alkmaar Netherlands Intensive Care Reinier de Graaf Gasthuis Delft Netherlands Intensive Care Spaarne Gasthuis Haarlem en Hoofddorp Netherlands Intensive Care VieCuri Medisch Centrum Venlo Netherlands Intensive Care Zuyderland MC Heerlen Netherlands Intensive Care Jeroen Bosch Ziekenhuis 's-Hertogenbosch Netherlands Intensive Care Albert Schweitzerziekenhuis Dordrecht Netherlands Intensive Care Haaglanden Medisch Centrum Den Haag Netherlands Intensive Care Maasstad Ziekenhuis Rotterdam Rotterdam Netherlands Intensive Care SEH BWC Martiniziekenhuis Groningen Netherlands Intensive Care Ziekenhuis Gelderse Vallei Ede Netherlands Intensive Care Ziekenhuisgroep Twente Almelo Netherlands Intensive Care Medisch Spectrum Twente Enschede Netherlands Intensive Care Ikazia Ziekenhuis Rotterdam Rotterdam Netherlands Anesthesiology Antonius Ziekenhuis Sneek Sneek Netherlands Intensive Care Medisch Centrum Leeuwarden Leeuwarden Netherlands Intensive Care IJsselland Zieke

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85;0.88] and 0.86 [0.84;0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12;0.04] and slope of 0.90 [0.85;0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27;−0.10] and slope of 0.89 [0.84;0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research. © 2022 The Authors

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Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

arXiv

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arXiv 2018年

作者： Elbaz, Mohammed S.M. Le, Trung Bao Garg, Pankaj Roest, Arno A.W. Lelieveldt, Boudewijn P.F. Plein, Sven Westenberg, Jos J.M. Sotiropoulos, Fotis van der Geest, Rob J. Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Civil Engineering Stony Brook University Stony BrookNY United States University of Leeds Leeds United Kingdom Department of Pediatrics Division of Pediatric Cardiology Leiden University Medical Center Leiden Netherlands Pattern Recognition and Bioinformatics Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computational Fluids Laboratory North Dakota State University United States

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time Tgrowth = 1.23 ± 0.25, growth rate α = 0.80 ± 0.17, decay time Tdecay = 0.96 ± 0.39 and decay rate β = -1.02 ± 0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease. Copyright © 2018, The Authors. All rights reserved.

关键词： Heart

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Metric learning for label fusion in multi-atlas based image segmentation

Metric learning for label fusion in multi-atlas based image ...

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IEEE International Symposium on Biomedical Imaging

作者： Hancan Zhu Hewei Cheng Xuesong Yang Yong Fan College of Mathematics Physics and Information Shaoxing University Shaoxing China School of Bioinformatics Chongqing University of Posts and Telecommunications Chongqing China National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China Department of Radiology University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781479923519

A novel metric learning method is proposed to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute the similarity between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset and the experimental results have demonstrated that our method can achieve better segmentation performance than the existing weighted voting label fusion methods with predefined metric models.

关键词： Measurement Image segmentation Hippocampus Training Indexes Computational modeling Image registration

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

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Acta neuropathologica 2020年第5期139卷 959-962页

作者： Sven J van der Lee Olivia J Conway Iris Jansen Minerva M Carrasquillo Luca Kleineidam Erik van den Akker Isabel Hernández Kristel R van Eijk Najada Stringa Jason A Chen Anna Zettergren Till F M Andlauer Monica Diez-Fairen Javier Simon-Sanchez Alberto Lleó Henrik Zetterberg Marianne Nygaard Cornelis Blauwendraat Jeanne E Savage Jonas Mengel-From Sonia Moreno-Grau Michael Wagner Juan Fortea Michael J Keogh Kaj Blennow Ingmar Skoog Manuel A Friese Olga Pletnikova Miren Zulaica Carmen Lage Itziar de Rojas Steffi Riedel-Heller Ignacio Illán-Gala Wei Wei Bernard Jeune Adelina Orellana Florian Then Bergh Xue Wang Marc Hulsman Nina Beker Niccolo Tesi Christopher M Morris Begoña Indakoetxea Lyduine E Collij Martin Scherer Estrella Morenas-Rodríguez James W Ironside Bart N M van Berckel Daniel Alcolea Heinz Wiendl Samantha L Strickland Pau Pastor Eloy Rodríguez Rodríguez Bradley F Boeve Ronald C Petersen Tanis J Ferman Jay A van Gerpen Marcel J T Reinders Ryan J Uitti Lluís Tárraga Wolfgang Maier Oriol Dols-Icardo Amit Kawalia Maria Carolina Dalmasso Mercè Boada Uwe K Zettl Natasja M van Schoor Marian Beekman Mariet Allen Eliezer Masliah Adolfo López de Munain Alexander Pantelyat Zbigniew K Wszolek Owen A Ross Dennis W Dickson Neill R Graff-Radford David Knopman Rosa Rademakers Afina W Lemstra Yolande A L Pijnenburg Philip Scheltens Thomas Gasser Patrick F Chinnery Bernhard Hemmer Martijn A Huisman Juan Troncoso Fermin Moreno Ellen A Nohr Thorkild I A Sørensen Peter Heutink Pascual Sánchez-Juan Danielle Posthuma Jordi Clarimón Kaare Christensen Nilüfer Ertekin-Taner Sonja W Scholz Alfredo Ramirez Agustín Ruiz Eline Slagboom Wiesje M van der Flier Henne Holstege Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Clinical Genetics Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Neuroscience Mayo Clinic Florida Jacksonville FL 32224 USA. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department for Neurodegenerative Diseases and Geriatric Psychiatry University of Bonn Bonn Germany. DZNE German Center for Neurodegenerative Diseases Bonn Germany. Division of Neurogenetics and Molecular Psychiatry Department of Psychiatry and Psychotherapy Faculty of Medicine University Hospital Cologne Cologne Germany. Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands. Pattern Recognition and Bioinformatics Delft University of Technology Delft The Netherlands. Research Center and Memory Clinic Fundació ACE Institut Català de Neurociències Aplicades Universitat Internacional de Catalunya Barcelona Spain. Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED) Madrid Spain. Department of Neurology Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands. Amsterdam UMC-Vrije Universiteit Amsterdam Department of Epidemiology and Biostatistics Amsterdam Public Health Research Institute Amsterdam The Netherlands. Interdepartmental Program in Bioinformatics University of California Los Angeles USA. Neuropsychiatric Epidemiology Unit Department of Psychiatry and Neurochemistry Institute of Neuroscience and Physiology Sahlgrenska Academy Centre for Ageing and Health (AgeCap) at

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than o...

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The Effect of Aggregating Subtype Performances Depends Strongly on the Performance Measure Used

The Effect of Aggregating Subtype Performances Depends Stron...

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International Conference on pattern recognition

作者： David M.J. Tax Herman M.J. Sontrop Marcel J.T Reinders Perry D. Moerland Pattern Recognition Laboratory Delft University of Technology Delft CD The Netherlands Molecular Diagnostics Department Philips Research Eindhoven AE The Netherlands Delft Bioinformatics Lab. Delft University of Technology Delft CD The Netherlands Department of Clinical Epidemiology Academic Medical Center Amsterdam AZ The Netherlands

ISBN: (纸本)9781479952106

For some classification tasks the data can be partitioned into disjoint subsets based on some attribute, for example a disease subtype. It then seems logical to train a classifier with the same classes as the original classification problem for each subtype separately, such that the performance per subtype is optimized. Unfortunately, the influence of the subtype performances on the aggregated overall performance depends strongly on the performance measure used and can be very counterintuitive. We show that for some performance measures (e.g., classification accuracy, precision, recall, Fi) the aggregated performance is a simple linear combination of subtype performances. In these cases, improving the performance of a subtype-specific classifier implies that the overall performance improves. However, for other performance measures (e.g., balanced accuracy rate, area under the ROC curve) and also for performance measures in survival analysis (concordance index), additional cross terms appear in the aggregation of the subtype performances. These cross terms are heavily dependent on both the overall class imbalance and the subtype class imbalances. For these measures, improving subtype performances may actually result in a decrease of the overall performance.

关键词： Nickel Accuracy Indexes Breast cancer Silicon Biomedical measurement Approximation methods

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Wang, Mingjun Shen, Hong-Bin Akutsu, Tatsuya Song, Jiangning State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC 3800 Australia

ISBN: (纸本)9781457716669

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations. © 2011 IEEE.

关键词： Amino acids

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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IEEE International Conference on Systems Biology

作者： Mingjun Wang Hong-Bin Shen Tatsuya Akutsu Jiangning Song State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences (CAS) Tianjin China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC Australia

关键词： Support vector machines Accuracy Proteins Feature extraction Humans Amino acids Databases

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Toward a realistic model for gene network evolution

Toward a realistic model for gene network evolution

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Chen, Wenhai Jiang, Tianzi Xia, Tian Gu, Xun National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing 100080 China Center for Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Center for Bioinformatics and Biological Statistics Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States

We investigated how to establish a realistic model for the evolutionary emergence of gene network that is scale-free. After theoretical analysis and compute simulation, we have demonstrated the evolutionary principles... 详细信息

ISBN: (纸本)1424415098

关键词： Genes

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