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Displaying 1-14 out of 14 total
Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Xi Chen,Jianhua Xuan,Chen Wang,Ayesha N. Shajahan,Rebecca B. Riggins,Robert Clarke
Issue Date:November 2013
pp. 1347-1358
Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performan...
 
A novel statistical approach to identify co-regulatory gene modules
Found in: 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
By Xi Chen,Jianhua Xuan,Xu Shi,Ayesha N. Shajahan-Haq,Leena Hilakivi-Clarke,Robert Clarke
Issue Date:December 2013
pp. 16-18
ChlP-chip experiments are performed to determine binding sites for transcription factors (TFs). Conventional TF-gene regulation is generated based on p-value cutoff of the binding sites as well as their distance to nearest genes. Taking into account that b...
   
Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic
Found in: Machine Learning and Applications, Fourth International Conference on
By Jinghua Gu, Jianhua Xuan, Yue Wang, Rebecca B. Riggins, Robert Clarke
Issue Date:December 2010
pp. 281-286
Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises ...
 
A Systems Biology Approach to Identify Affected Regulatory and Signaling Circuits in Protein Interaction Networks
Found in: Bioinformatics, Systems Biology and Intelligent Computing, International Joint Conference on
By Ting Gong,Jianhua Xuan,Rebecca B. Reggins,Robert Clarke
Issue Date:August 2009
pp. 297-300
Microarray technology and high-throughput proteomics have revolutionized cancer biology by generating vast amount of data for various cancers. To gain insights into the biological processes that drive breast cancer recurrence, we integrate gene expression ...
 
Network-Constrained Support Vector Machine for Classification
Found in: Machine Learning and Applications, Fourth International Conference on
By Li Chen, Jianhua Xuan, Yue Wang, Rebecca B. Riggins, Robert Clarke
Issue Date:December 2008
pp. 60-65
One of the major goals in microarray data analysis is to identify biomarkers and build a classification model for future prediction. Many traditional statistical models, based on microarray data alone, often fail in identifying biologically meaningful gene...
 
Biomarker Identification by Knowledge-Driven Multi-Level ICA and Motif Analysis
Found in: Machine Learning and Applications, Fourth International Conference on
By Li Chen, Chen Wang, Ie-Ming Shih, Tian-Li Wang, Zhen Zhang, Yue Wang, Robert Clarke, Eric Hoffman, Jianhua Xuan
Issue Date:December 2007
pp. 560-566
Many statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level independent component a...
 
Normalization of Microarray Data by Iterative Nonlinear Regression
Found in: Bioinformatic and Bioengineering, IEEE International Symposium on
By Jianhua Xuan, Eric Hoffman, Robert Clarke, Yue Wang
Issue Date:October 2005
pp. 267-270
Normalization is an important prerequisite for almost all follow-up microarray data analysis steps. Accurate normalization assures a common base for comparative biomedical studies using gene expression profiles across different experiments and phenotypes. ...
 
Classification of Pathology Data Using a Probabilistic (Bayesian) Model
Found in: Systems Engineering, International Conference on
By Howard Robin, John S. Eberhardt III, Wayne D. Muller, Robert Clark, Jenny Kam
Issue Date:August 2005
pp. 286-291
BACKGROUND: Applying dynamic statistical modeling to clinical laboratory data can help the clinician make more effective use of this essential information. We applied a probabilistic (Bayesian) classifier to a set of 645 breast tumor samples for which we h...
 
Robust Feature Selection by Weighted Fisher Criterion for Multiclass Prediction in Gene Expression Profiling
Found in: Pattern Recognition, International Conference on
By Jianhua Xuan, Yibin Dong, Javed Khan, Eric Hoffman, Robert Clarke, Yue Wang
Issue Date:August 2004
pp. 291-294
This paper presents a robust feature selection approach for multiclass prediction with application to microarray studies. First, individually discriminatory genes (IDGs) are identified by using weighted Fisher Criterion (wFC). Second, jointly discriminator...
 
Ipsilateral Multi-View CAD System for Mass Detection in Digital Mammography
Found in: Mathematical Methods in Biomedical Image Analysis, IEEE Workshop on
By Xuejun Sun, Wei Qian, Dansheng Song, Robert A. Clark
Issue Date:December 2001
pp. 19
In this paper, an ipsilateral multi-view computer-aided diagnosis (CAD) scheme is presented for the earlier mass detection in digital mammograms. Tree structured nonlinear filtering (TSF) is used in image noise supression. Two wavelet-based methods, direct...
 
Integration of network biology and imaging to study cancer phenotypes and responses
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ye Tian,Sean Wang,Zhen Zhang,Olga Rodriguez,Emanuel Petricoin III,Ie-Ming Shih,Daniel Chan,Maria Avantaggiati,Guoqiang Yu,Shaozhen Ye,Robert Clarke,Chao Wang,Bai Zhang,Yue Wang,Chris Albanese
Issue Date:August 2014
pp. 1
Ever growing “omics” data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical m...
 
An Initial Analysis of the Contextual Information Available within Auction Posts on Contract Cheating Agency Websites
Found in: 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA)
By Thomas Lancaster,Robert Clarke
Issue Date:May 2014
pp. 548-553
The advantages of using contextual information in order to detect contract cheating attempts by students have not yet been fully explored in the academic literature. Contract cheating occurs when a student uses a third party to produce assessed work for th...
 
Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Ayesha N. Shajahan, Chen Wang, Jianhua Xuan, Rebecca B. Riggins, Robert Clarke, Xi Chen
Issue Date:November 2013
pp. 1347-1358
Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performan...
     
Commercial aspects of contract cheating
Found in: Proceedings of the 18th ACM conference on Innovation and technology in computer science education (ITiCSE '13)
By Robert Clarke, Thomas Lancaster
Issue Date:July 2013
pp. 219-224
The process of contract cheating, the form of academic dishonesty where students outsource the creation of work on their behalf, has been recognised as a serious threat to the quality of academic awards. Unlike student plagiarism, this cheating behaviour i...
     
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