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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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Theoretical insight into business workflow virtualization of E-commerce

Theoretical insight into business workflow virtualization of...

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1st International Conference on information Science and Engineering, ICISE2009

作者： Hong, Daocheng Li, Yangmei Department of Information Management and Information Systems Shanghai Key Lab. of Intelligent Information Processing Fudan University Shanghai China Research Center for Marketing Shanghai China Express Co. Ltd. Shanghai China

ISBN: (纸本)9780769538877

In our daily life, more and more commercial activities that have traditionally been conducted via physical mechanisms are being conducted virtually by means of information technologies (ITs). With the tendency of increasing virtualization of e-commerce, it's necessary to study the emerging phenomena from a novel theoretical perspective. According to previous literature communication media have been playing a vital role in IT-enabled business workflow virtualization. This research focuses on business workflow and proposes theoretical insight from media characteristic and relevant perspective. This study provides a new theoretical insight into workflow virtualization and workflow management, which are playing the core role in the research field of e-commerce. Finally, this work also presents potential future research for workflow management. ©2009 IEEE.

关键词： Workflow management

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Using fuzzy cognitive map to effectively classify e-documents and application

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4th International Conference on Grid and Cooperative computing - GCC 2005

作者： Wang, Jianzeng Xing, Yunpeng Shi, Peng Guo, Fei Wang, Zhen Yao, Erlin Yuan, Kehua Zhang, Junsheng Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Hunan Knowledge Grid Lab Hunan University of Science and Technology Hunan China Graduate School Chinese Academy of Sciences Beijing China

ISBN: (纸本)3540305106

In the current Web, e-document has been the most common vehicle for delivering and exchanging information. As the amount of e-documents has grown enormously, effective classification facilities are urgently needed to classify and query e-documents users want. In this paper, we propose a method to classify e-documents into a set of predefined categories based on Fuzzy Cognitive Map (FCM). The e-documents are collected from Internet by a metasearch engine. FCM has been employed to capture the semantic relationships between keywords of e-documents. Experiments with a set of local e-documents have proved that this approach has high performance and can help users getting the e-documents efficiently and effectively. The proposed method has been implemented and integrated into the Dunhuang Feitian System to manage and classify e-documents. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Electronic document exchange

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Sub-sentence division for tree-based machine translation

Sub-sentence division for tree-based machine translation

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language processing of the AFNLP, ACL-IJCNLP 2009

作者： Xiong, Hao Xu, Wenwen Mi, Haitao Liu, Yang Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Key Lab. of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781617382581

Tree-based statistical machine translation models have made significant progress in recent years, especially when replacing 1-best trees with packed forests. However, as the parsing accuracy usually goes down dramatically with the increase of sentence length, translating long sentences often takes long time and only produces degenerate translations. We propose a new method named subsentence division that reduces the decoding time and improves the translation quality for tree-based translation. Our approach divides long sentences into several sub-sentences by exploiting tree structures. Large-scale experiments on the NIST 2008 Chinese-to-English test set show that our approach achieves an absolute improvement of 1.1 BLEU points over the baseline system in 50% less time. © 2009 ACL and AFNLP.

关键词： Machine translation

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Predicting compositional time series via autoregressive Dirichlet estimation

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Science China(information Sciences) 2018年第9期61卷 300-305页

作者： Ganbin ZHOU Ping LUO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

In recent years,compositional time series(CTS)prediction has become a widely applied data analysis method for modeling tactile sequence data[1],hydrological time series data using a four-

关键词： CTS Predicting compositional time series via autoregressive Dirichlet estimation

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AlphaFold2 and its applications in the fields of biology and medicine

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Signal Transduction and Targeted Therapy 2023年第4期8卷 1630-1643页

作者： Zhenyu Yang Xiaoxi Zeng Yi Zhao Runsheng Chen West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Key Laboratory of RNA Biology Center for Big Data Research in HealthInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Pingshan Translational Medicine Center Shenzhen Bay LaboratoryShenzhen 518118China

AlphaFold2(AF2)is an artificial intelligence(AI)system developed by DeepMind that can predict three-dimensional(3D)structures of proteins from amino acid sequences with atomic-level *** structure prediction is one of the most challenging problems in computational biology and chemistry,and has puzzled scientists for 50 *** advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much *** release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community,especially in the fields of biology and ***2 is thought to have a significant impact on structural biology and research areas that need protein structure information,such as drug discovery,protein design,prediction of protein function,et *** the time is not long since AF2 was developed,there are already quite a few application studies of AF2 in the fields of biology and medicine,with many of them having preliminarily proved the potential of *** better understand AF2 and promote its applications,we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success,and particularly focus on reviewing its applications in the fields of biology and *** of current AF2 prediction will also be discussed.

关键词： system fields Fold

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Exploring differential topic models for comparative summarization of scientific papers 26

Exploring differential topic models for comparative summariz...

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26th International Conference on Computational Linguistics, COLING 2016

作者： He, Lei Li, Wei Zhuge, Hai System Analytics Research Institute Aston University Birmingham United Kingdom Key Lab. of Intelligent Information Processing ICT CAS University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9784879747020

This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differentially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summarization methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics. © 1963-2018 ACL.

关键词： Probability distributions

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Interactive attention for neural machine translation 26

Interactive attention for neural machine translation

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26th International Conference on Computational Linguistics, COLING 2016

作者： Meng, Fandong Lu, Zhengdong Li, Hang Liu, Qun AI Platform Department Tencent Technology Co. Ltd. China Noah's Ark Lab. Huawei Technologies China ADAPT Centre School of Computing Dublin City University Ireland Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Ireland

ISBN: (纸本)9784879747020

Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets. © 1963-2018 ACL.

关键词： Neural machine translation

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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SES: Bridging the Gap between Explainability and Prediction of Graph Neural Networks 40

SES: Bridging the Gap between Explainability and Prediction ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Huang, Zhenhua Li, Kunhao Wang, Shaojie Jia, Zhaohong Zhu, Wentao Mehrotra, Sharad Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle United States University of California Irvine Irvine United States

ISBN: (纸本)9798350317152

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks. © 2024 IEEE.

关键词： Classification (of information)

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