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IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Sparse Weighted Canonical Correlation Analysis

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Chinese Journal of Electronics 2018年第3期27卷 459-466页

作者： MIN Wenwen LIU Juan ZHANG Shihua State Key Laboratory of Software Engineering School of Computer Wuhan University National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Sciences University of Chinese Academy of Sciences

Given two data matrices X and Y, Sparse canonical correlation analysis（SCCA） is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Yv. Classical and sparse Canonical correlation analysis（CCA） models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. We propose a novel Sparse weighted canonical correlation analysis（SWCCA）,where weights are used for regularizing different *** solve the L0-regularized SWCCA（L0-SWCCA） using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. We consider also SWCCA with different penalties like Least absolute shrinkage and selection operator（LASSO）and Group LASSO, and extend it for integrating more than three data matrices.

关键词： Canonical correlation analysis(CCA) Sparse canonical correlation analysis(SCCA) Sparse weighted CCA(SWCCA) Group LASSO regularized SWCCA Multi-view SWCCA

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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

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Model and Practice of Crowd-Based Education

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Mao, Xinjun Lu, Yao Yin, Liangze Wang, Tao Yin, Gang Key Laboratory of Software Engineering for Complex Systems College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030012977

Based on connectivism pedagogy crowd-based education provides a practical method to extensively exploit wisdoms of core learners in education organization and external crowds on Internet. However, when applying such a method in education field, several design questions about why, what, when, who, where and how to adopt such method should be clarified and elaborated. In this paper, we introduce our successful applications of the crowd-based method in software engineering course for undergraduates. We design an organization structure consisting of "small-core crowd" and "large-external crowd" for our course projects in which both learners and crowds on Internet work together to contribute their wisdoms. Two kinds of wisdoms of crowds are exploited in our practices. One is the high-quality open source software (OSS) developed by crowds on the Internet, the other is the diverse software development issues, knowledges, experiences, expertise, etc., that are discussed and interacted by crowds in OSS communities and course communities. We design several course practice activities to exploit crowds’ wisdoms, including reading high-quality OSS, searching and reusing OSS in course project, joining and getting helps from OSS communities. The results show that the crowd-based method applied in our software engineering course can significantly improve learner’s engineering capabilities of developing high-quality and large-scale software. © 2018, Springer Nature Switzerland AG.

关键词： Open source software

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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Aspect-based sentiment analysis with the multiple-element attention and part of speech 12

Aspect-based sentiment analysis with the multiple-element at...

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Liu, Ning Shen, Bo Mi, Kun Sun, Mingdong Chen, Naiyue School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing China Beijing Huayu Software Co. Ltd Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9781728128207

Aspect-based Sentiment analysis (ABSA) is a rapidly growing field of research in natural language processing. ABSA is a fine-grained task of Sentiment analysis. How to capture precise sentiment expressions in a sentence towards the specific aspect remains a challenge. In this paper, we propose a novel neural network, named Multiple-element Attention LSTM (MEA-LSTM) to alleviate the problem of self-attention or binary-element attention used in the ABSA task. These attention mechanisms mentioned above are weak attention, they ignore the information of aspect target or sentence representation. To capture the precise sentiment expressions, we make use of multiple-element attention to assign different importance degrees of different words in a sentence. To store these informative aspect-dependent representations, extra representation memory is designed. Part of speech (POS) is an important feature in identifying the sentiment expressions in the ABSA task. We combine POS with the LSTM in the proposed MEA-LSTM. Experimental results show that our proposed model acquires state-of-the-art accuracy at both restaurant and laptop datasets. Besides, a rule of thumb about choosing the number of hops is given on both datasets. © 2019 IEEE.

关键词： Sentiment analysis

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Fine-grained image analysis with deep learning: A survey

arXiv

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arXiv 2021年

作者： Wei, Xiu-Shen Song, Yi-Zhe Aodha, Oisin Mac Wu, Jianxin Peng, Yuxin Tang, Jinhui Yang, Jian Belongie, Serge PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China University of Surrey United Kingdom The University of Edinburgh United Kingdom The State Key Laboratory for Novel Software Technology Nanjing University China Peking University China Nanjing University of Science and Technology China The University of Copenhagen The Pioneer Centre for AI Denmark

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas – fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community. © 2021, CC BY.

关键词： Image recognition

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Molecular communications in viral infections research: Modelling, experimental data and future directions

arXiv

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arXiv 2020年

作者： Barros, Michael Taynnan Veletić, Mladen Kanada, Masamitsu Pierobon, Massimiliano Vainio, Seppo Balasingham, Ilangko Balasubramaniam, Sasitharan School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom CBIG/BioMediTech Tampere University Tampere Finland Intervention Centre Oslo University Hospital Oslo Norway Department of Electronic Systems Norwegian University of Science and Technology Trondheim Norway Institute for Quantitative Health Science and Engineering Michigan State University East LansingMI United States Department of Computer Science and Engineering University of Nebraska-Lincoln LincolnNE United States Biocenter Oulu Laboratory of Developmental Biology Oulu University Oulu Finland Telecommunications Software and Systems Group Waterford Institute of Technology Waterford City Ireland

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and how they can impact society as a whole. Novel techniques that bring in different disciplines must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developing, known as Molecular Communications. This new emerging area uses elements of classical communication systems, and maps it to molecular signalling and communication found inside and outside the body, where the aim is to develop new tools that can serve future medicine. One of these tools is the ability to characterise the signalling processes between cells and infectious disease locations at various levels of the human body. In this paper, we provide an extensive and detailed discussion on how Molecular Communications can be integrated into the research on viral infectious diseases modelling, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on the existing models of Molecular Communications for viral infection (in-body and out-body), a deep analysis on their effects on the host and subsequent communication process for other systems within the body (e.g., immune response), sources of experimental data on known viral infections and how it can be used by the Molecular Communications community, as well as open issues and future directions. Since the development of therapeutics/vaccines needs an interdisciplinary approach centred around ICT, we are confident that Molecular Communications can play a central role here by providing a detail characte

关键词： Diseases

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Scalability Evaluation of Big Data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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