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检索条件"机构=Pattern Recognition and Bioinformatics Laboratory"

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Robust Gram embeddings

Robust Gram embeddings

引用

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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pattern recognition in bioinformatics 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Marco Loog Marcel J. T. Reinders Dick Ridder Lodewyk Wessels

ISBN: (数字)9783642248559

ISBN: (纸本)9783642248542

This book constitutes the refereed proceedings of the 6th International Conference on pattern recognition in bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers cover the wide range of possible applications of bioinformatics in pattern recognition: novel algorithms to handle traditional pattern recognition problems such as (bi)clustering, classification and feature selection; applications of (novel) pattern recognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. They are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.

关键词： Computational Biology/bioinformatics Health Informatics pattern recognition Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity Artificial Intelligence

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Toward a realistic model for gene network evolution

Toward a realistic model for gene network evolution

引用

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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Markov Random Field for wind farm planning

Markov Random Field for wind farm planning

引用

IEEE International Conference on Smart Energy Grid Engineering (SEGE)

作者： Hale Cetinay Taygun Kekeç Fernando A. Kuipers D. M. J. Tax Network Architectures and Services Delft University of Technology Delft The Netherlands Pattern Recognition and Bioinformatics Laboratory Delft University of Technology Delft The Netherlands

Many countries aim to integrate a substantial amount of wind energy in the near future. This requires meticulous planning, which is challenging due to the uncertainty in wind profiles. In this paper, we propose a novel framework to discover and investigate those geographic areas that are well suited for building wind farms. We combine the key indicators of wind farm investment using fuzzy sets, and employ multiple-criteria decision analysis to obtain a coarse wind farm suitability value. We further demonstrate how this suitability value can be refined by a Markov Random Field (MRF) that takes the dependencies between adjacent areas into account. As a proof of concept, we take wind farm planning in Turkey, and demonstrate that our MRF modeling can accurately find promising areas.

关键词： Wind farms Investment Markov random fields Wind power generation Decision analysis Planning

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ROSE: Robust Selective Fine-tuning for Pre-trained Language Models

ROSE: Robust Selective Fine-tuning for Pre-trained Language ...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Jiang, Lan Zhou, Hao Lin, Yankai Li, Peng Zhou, Jie Jiang, Rui MOE Key Laboratory of Bioinformatics Center for Synthetic and Systems Biology Department of Automation BNRist Tsinghua University China Pattern Recognition Center WeChat AI Tencent Inc. China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tsinghua University China

Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. However, they are still limited due to redundant attack search spaces and the inability to defend against various types of attacks. In this work, we present a novel fine-tuning approach called RObust SEletive fine-tuning (ROSE) to address this issue. ROSE conducts selective updates when adapting pre-trained models to downstream tasks, filtering out invaluable and unrobust updates of parameters. Specifically, we propose two strategies: the first-order and second-order ROSE for selecting target robust parameters. The experimental results show that ROSE achieves significant improvements in adversarial robustness on various downstream NLP tasks, and the ensemble method even surpasses both variants above. Furthermore, ROSE can be easily incorporated into existing fine-tuning methods to improve their adversarial robustness further. The empirical analysis confirms that ROSE eliminates unrobust spurious updates during fine-tuning, leading to solutions corresponding to flatter and wider optima than the conventional method. Code is available at https://***/jiangllan/ROSE. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

Probabilistic Inference for Camera Calibration in Light Micr...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Guo Fons J. Verbeek Ge Yang Computational Biology and Machine Intelligence National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LIACS Bioinformatics and Imaging Leiden University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address these issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.

关键词： Cameras Three-dimensional displays Calibration Probabilistic logic Microscopy Solid modeling

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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...

引用

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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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

引用

2020 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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Toward a Realistic Model for Gene Network Evolution

Toward a Realistic Model for Gene Network Evolution

引用

IEEE Symposium on bioinformatics and Bioengineering (BIBE)

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

关键词： Proteins Biological system modeling bioinformatics Laboratories pattern recognition Automation Genomics Network topology Large-scale systems Evolution (biology)

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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 ...

引用

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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