107 results on '"Xiguo Yuan"'
Search Results
102. Design of a Four-Layer Model Based on Danger Theory and AIS for IDS
- Author
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Liping Hu, Xiguo Yuan, and Haidong Fu
- Subjects
Immune system ,Computer science ,Artificial immune system ,business.industry ,Intrusion detection system ,Artificial intelligence ,Layer (object-oriented design) ,business - Abstract
After a decade of research into the classical self-non-self model for immune-based intrusion detection system (IDS), a new idea has been developed by immunologists recently, which is called danger theory. Based on this new viewpoint, we present a four-layer model of artificial immune system (AIS) . Also, a mechanism of reasoning with uncertainty is proposed to increase the detection accuracy. There are three merits of this novel model. First, danger theory provides a method of 'grounding' the immune response to reduce false alarms. Second, each layer of the model can work independently and interact with each other, so the efficiency of detecting is greatly improved. Last, the reasoning with uncertainty allows the model to dynamically adapt to real network traffic.
- Published
- 2007
103. Prediction of Cancer-Associated piRNA-mRNA and piRNA-lncRNA Interactions by Integrated Analysis of Expression and Sequence Data.
- Author
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Yajun Liu, Junying Zhang, Aimin Li, Zhaowen Liu, Zhongzhen He, Xiguo Yuan, and Shouheng Tuo
- Subjects
CANCER diagnosis ,MICRORNA ,TRANSPOSONS ,BIOMARKERS ,GENE expression ,GENE ontology - Abstract
piwi-interacting RNAs (piRNAs) are valuable biomarkers, but functional studies are still very limited. Recent research shows that piRNA-mediated cleavage acts on Transposable Elements (TEs), messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). This study aimed to predict cancer-associated piRNA-mRNA and piRNA-lncRNA interactions as well as piRNA regulatory functions. Four cancer types (BRCA, HNSC, KIRC and LUAD) were investigated. Interactions were identified by integrated analysis of the expression and sequence data. For the expression analysis, only piRNA-mRNA and piRNA-lncRNA pairs with expression profiles that were significantly inversely correlated were retained to reduce false-positive rates during the prediction. For the sequence analysis, miRanda was used for the target prediction. We identified 198 piRNA-mRNA and 10 piRNA-lncRNA pairs. Unlike mRNA and lncRNA expressions, the piRNA expression was relatively consistent across the cancer types. Furthermore, the identified piRNAs were consistent with previously published cancer biomarkers, such as piRNA-36741, piR-21032 and piRNA-57125. More importantly, predicted piRNA functions were determined by constructing an interaction network and piRNA targets were placed in gene ontology categories related to the cancer hallmarks "activating invasion and metastasis" and "sustained angiogenesis". [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
104. Applying the Danger Model to Design an Intrusion Detection System.
- Author
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Haidong Fu, Xiguo Yuan, Kui Zhang, Na Wang, and Ting Xia
- Subjects
INTRUSION detection systems (Computer security) ,COMPUTER network security ,AUTOIMMUNITY ,IMMUNOLOGY ,COMPUTER security - Published
- 2008
105. Design of a Four-Layer Model Based on Danger Theory and AIS for IDS.
- Author
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Haidong Fu, Xiguo Yuan, and Liping Hu
- Published
- 2007
- Full Text
- View/download PDF
106. Genome-wide identification of significant aberrations in cancer genome.
- Author
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Xiguo Yuan, Guoqiang Yu, Xuchu Hou, Ie-Ming Shih, Clarke, Robert, Junying Zhang, Hoffman, Eric P., Wang, Roger R., Zhen Zhang, and Yue Wang
- Subjects
- *
GENOMES , *GENOMICS , *OVARIAN cancer , *GLIOBLASTOMA multiforme , *ONCOGENES - Abstract
Background: Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. Results: We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Conclusions: Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
107. BMC Genomics
- Author
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Zhen Zhang, Xiguo Yuan, Ie Ming Shih, Yue Wang, Xuchu Hou, Robert Clarke, Guoqiang Yu, Junying Zhang, Roger R. Wang, Eric P. Hoffman, and Electrical and Computer Engineering
- Subjects
Male ,DNA Copy Number Variations ,lcsh:QH426-470 ,lcsh:Biotechnology ,Computational biology ,Biology ,Genome ,03 medical and health sciences ,Permutation ,0302 clinical medicine ,CDKN2A ,Neoplasms ,lcsh:TP248.13-248.65 ,Databases, Genetic ,medicine ,Null distribution ,Genetics ,Humans ,Computer Simulation ,030304 developmental biology ,0303 health sciences ,Models, Genetic ,Genome, Human ,Methodology Article ,Cancer ,Oncogenes ,medicine.disease ,lcsh:Genetics ,Identification (information) ,030220 oncology & carcinogenesis ,Female ,Human genome ,DNA microarray ,Algorithms ,Biotechnology - Abstract
Background Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. Results We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Conclusions Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open–source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm.
- Full Text
- View/download PDF
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