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Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease

Tsinghua Science and Technology 2019年第4期24卷 456-467页

作者： Shengbing Pei Jihong Guan shuigeng zhou the Department of Computer Science and Technology Tongji University Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan University

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each ***, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

关键词： Independent Component Analysis(ICA) group analysis fusion analysis Alzheimer’s Disease(AD)

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Finding MicroRNA Targets in Plants: Current Status and Perspectives

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Genomics, Proteomics & Bioinformatics 2012年第5期10卷 264-275页

作者： Jiandong Ding shuigeng zhou Jihong Guan Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tongji UniversityShanghai 201804China

MicroRNAs （miRNAs）, a class of ～20-24 nt long non-coding RNAs, have critical roles in diverse biological processes including devel- opment, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miR NA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods.

关键词： MicroRNA Target prediction Degradome-seq Integration

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CloudNMF:A MapReduce Implementation of Nonnegative Matrix Factorization for Large-scale Biological Datasets

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Genomics, Proteomics & Bioinformatics 2014年第1期12卷 48-51页

作者： Ruiqi Liao Yifan Zhang Jihong Guan shuigeng zhou School of Computer Science Fudan University Department of Computer Science and Technology Tongji University Shanghai Key Laboratory of Intelligent Information Processing Fudan University

In the past decades,advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and ***,nonnegative matrix factorization （NMF） has been introduced as an efficient way to reduce the complexity of data as well as to interpret them,and has been applied to various fields of biological *** this paper,we present CloudNMF,a distributed open-source implementation of NMF on a MapReduce *** evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data,which may enable various kinds of a high-throughput biological data analysis in the *** is freely accessible at http：//***/projects/***.

关键词： Nonnegative matrix factorization MapReduce Bioinformatics

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Generalized Multidimensional Association Rules

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Journal of Computer Science & Technology 2000年第4期15卷 388-392页

作者：周傲英周水庚金文田增平 Department of Computer Science Fudan University Shanghai P.R. China

The problem of association rule mining has gained considerableprominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of researchactivities around this problem. Traditional association rule mining is limited tointratransaction. Only recently the concept of N-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extendsLu's definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, whichis more general, flexible and reasonable than NDITAR.

关键词： multidimensional transaction database data mining Ndimensionalinter-transaction association rules (NDITAR) generalized multidimensional association rules (GMDAR)

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Approaches for Scaling DBSCAN Algorithm to Large Spatial Databases

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Journal of Computer Science & Technology 2000年第6期15卷 509-526页

作者：周傲英周水庚曹晶范晔胡运发 Department of Computer Science Fudan University Shanghai P.R. China

The huge amount of information stored in databases owned by corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining. Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN algorithm shows good performance in spatial data clustering. However, for large spatial databases, DBSCAN requires large volume of memory support and could incur substantial I/O costs because it operates directly on the entire database. In this paper, several approaches are proposed to scale DBSCAN algorithm to large spatial databases. To begin with, a fast DBSCAN algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Then a sampling based DBSCAN algorithm, a partitioning-based DBSCAN algorithm, and a parallel DBSCAN algorithm are introduced consecutively. Following that, based on the above-proposed algorithms, a synthetic algorithm is also given. Finally, some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.

关键词： spatial database clustering fast DBSCAN algorithm data sampling data partitioning parallel

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HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 976-990页

作者： Yuliang Pan Ruiyi Li Wengen Li Liuzhenghao Lv Jihong Guan shuigeng zhou Department of Computer Science and Technology College of Electronic and Information EngineeringTongji UniversityShanghai 201804China Translational Medical Center for Stem Cell Therapy Shanghai East HospitalSchool of MedicineTongji UniversityShanghai 200120China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of *** a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein ***,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological *** fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to *** integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized *** with the existing methods,our method achieved outstanding performance on both testing and independent ***,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting *** results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological *** HPCAtlas webserver is freely available at http://***/HPC-Atlas.

关键词： Human protein complex Protein interaction network SARS-CoV-2-affected complex Multifunctional protein Complex identification method

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一种基于文档模式的GML压缩方法

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计算机研究与发展 2011年第9期48卷 1704-1713页

作者：魏勍颋关佶红周水庚同济大学电子与信息工程学院上海201804 南昌大学软件学院南昌330046 上海市智能信息处理重点实验室(复旦大学) 上海200433 复旦大学计算机科学技术学院上海200433

GML已成为地理空间数据编码的事实标准.GML文档一般体积庞大,存储和传输时占用巨额资源.提出了一种基于文档模式的有效GML压缩方法,通过用文档推导出的模式验证文档本身,对树自动机的状态转换路径进行比特编码,对坐标数据增量编码,实现... 详细信息

GML已成为地理空间数据编码的事实标准.GML文档一般体积庞大,存储和传输时占用巨额资源.提出了一种基于文档模式的有效GML压缩方法,通过用文档推导出的模式验证文档本身,对树自动机的状态转换路径进行比特编码,对坐标数据增量编码,实现GML文档压缩.对真实GML文档的压缩实验表明,所提出方法的压缩率优于通用文本压缩器(gzip和PPMD)、主要高性能XML压缩器(XMill,XMLPPM和XWRT)以及现有GML压缩器GPress.

关键词： XML压缩 GML压缩模式增量编码树自动机

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N-SHOQ(D):描述逻辑SHOQ(D)的一个非单调扩展

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计算机研究与发展 2005年第4期42卷 570-575页

作者：王淞昕王飞周水庚周傲英上海财经大学计算机科学与技术系上海200433 复旦大学计算机科学与工程系上海200433

描述逻辑SHOQ(D)给出了Web本体语言DAML+OIL的语义,但SHOQ(D)只能处理严格成立的完备知识,不能处理在实际情况中经常出现的不完备知识.对描述逻辑SHOQ(D)进行扩展,提出了能够处理不完备知识的非单调描述逻辑N-SHOQ(D).给出了N-SHOQ(D)... 详细信息

描述逻辑SHOQ(D)给出了Web本体语言DAML+OIL的语义,但SHOQ(D)只能处理严格成立的完备知识,不能处理在实际情况中经常出现的不完备知识.对描述逻辑SHOQ(D)进行扩展,提出了能够处理不完备知识的非单调描述逻辑N-SHOQ(D).给出了N-SHOQ(D)的语法和语义,定义了N-SHOQ(D)中的蕴涵推理关系,研究了N-SHOQ(D)所具有的性质.N-SHOQ(D)为扩展DAML+OIL语言到能够处理不完备知识的情形提供了语义支持.

关键词：语义Web DAML+OIL 描述逻辑非单调推理

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无需词典支持和切词处理的中文文档分类

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高技术通讯 2001年第3期11卷 31-35页

作者：周水庚关佶红胡运发复旦大学计算机系上海200433 武汉测绘科技大学信息学院武汉430079

利用中文文本的N gram信息进行中文文档分类 ,使中文文档分类摆脱了对词典和切词处理的依赖。实验结果表明。

关键词：中文文档分类 N-gram信息属性选择贝叶斯分类 kNN法

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基于线性残差独立性测试的致病基因检测

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武汉大学学报（理学版） 2023年第2期69卷 127-136页

作者：张浩周水庚关佶红复旦大学计算机科学技术学院/上海市智能信息处理重点实验室上海200433 同济大学电子与电信工程学院上海201804

使用因果推断相关的机器学习方法辅助检测致病基因时,作为因果推断的核心工具,条件独立性(CI)测试算法在高维生物数据场景中往往存在时间复杂度高以及准确性低等问题。为此,提出一种融合偏相关测试与线性残差独立性测试算法,压缩CI测试... 详细信息

使用因果推断相关的机器学习方法辅助检测致病基因时,作为因果推断的核心工具,条件独立性(CI)测试算法在高维生物数据场景中往往存在时间复杂度高以及准确性低等问题。为此,提出一种融合偏相关测试与线性残差独立性测试算法,压缩CI测试条件集的搜索空间,同时提高准确率。设计一种因果推断策略,在减少冗余CI测试的同时结合V结构与因果函数模型的优点,在应用于真实癌症数据的致病基因检测场景中可以区分Markov等价类,找到真正的因果关系。实验结果表明,提出的算法有较好的致病基因检测性能。

关键词：因果网络因果推断条件独立性致病基因条件独立性测试

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