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Search Results: 1 - 10 of 123789 matches for " Raymond T. Ng "
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Similarity Join Size Estimation using Locality Sensitive Hashing
Hongrae Lee,Raymond T. Ng,Kyuseok Shim
Computer Science , 2011,
Abstract: Similarity joins are important operations with a broad range of applications. In this paper, we study the problem of vector similarity join size estimation (VSJ). It is a generalization of the previously studied set similarity join size estimation (SSJ) problem and can handle more interesting cases such as TF-IDF vectors. One of the key challenges in similarity join size estimation is that the join size can change dramatically depending on the input similarity threshold. We propose a sampling based algorithm that uses the Locality-Sensitive-Hashing (LSH) scheme. The proposed algorithm LSH-SS uses an LSH index to enable effective sampling even at high thresholds. We compare the proposed technique with random sampling and the state-of-the-art technique for SSJ (adapted to VSJ) and demonstrate LSH-SS offers more accurate estimates at both high and low similarity thresholds and small variance using real-world data sets.
Non-monotonic Negation in Probabilistic Deductive Databases
Raymond T. Ng,V. S. Subrahmanian
Computer Science , 2013,
Abstract: In this paper we study the uses and the semantics of non-monotonic negation in probabilistic deductive data bases. Based on the stable semantics for classical logic programming, we introduce the notion of stable formula, functions. We show that stable formula, functions are minimal fixpoints of operators associated with probabilistic deductive databases with negation. Furthermore, since a. probabilistic deductive database may not necessarily have a stable formula function, we provide a stable class semantics for such databases. Finally, we demonstrate that the proposed semantics can handle default reasoning naturally in the context of probabilistic deduction.
Empirical Probabilities in Monadic Deductive Databases
Raymond T. Ng,V. S. Subrahmanian
Computer Science , 2013,
Abstract: We address the problem of supporting empirical probabilities in monadic logic databases. Though the semantics of multivalued logic programs has been studied extensively, the treatment of probabilities as results of statistical findings has not been studied in logic programming/deductive databases. We develop a model-theoretic characterization of logic databases that facilitates such a treatment. We present an algorithm for checking consistency of such databases and prove its total correctness. We develop a sound and complete query processing procedure for handling queries to such databases.
Topic Segmentation and Labeling in Asynchronous Conversations
Shafiq Rayhan Joty,Giuseppe Carenini,Raymond T Ng
Computer Science , 2014, DOI: 10.1613/jair.3940
Abstract: Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.
HLA Class I Restriction as a Possible Driving Force for Chikungunya Evolution
Joo Chuan Tong,Diane Simarmata,Raymond T. P. Lin,Laurent Rénia,Lisa F. P. Ng
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0009291
Abstract: After two decades of quiescence, epidemic resurgence of Chikungunya fever (CHIKF) was reported in Africa, several islands in the Indian Ocean, South-East Asia and the Pacific causing unprecedented morbidity with some cases of fatality. Early phylogenetic analyses based on partial sequences of Chikungunya virus (CHIKV) have led to speculation that the virus behind recent epidemics may result in greater pathogenicity. To understand the reasons for these new epidemics, we first performed extensive analyses of existing CHIKV sequences from its introduction in 1952 to 2009. Our results revealed the existence of a continuous genotypic lineage, suggesting selective pressure is active in CHIKV evolution. We further showed that CHIKV is undergoing mild positive selection, and that site-specific mutations may be driven by cell-mediated immune pressure, with occasional changes that resulted in the loss of human leukocyte antigen (HLA) class I-restricting elements. These findings provide a basis to understand Chikungunya virus evolution and reveal the power of post-genomic analyses to understand CHIKV and other viral epidemiology. Such an approach is useful for studying the impact of host immunity on pathogen evolution, and may help identify appropriate antigens suitable for subunit vaccine formulations.
MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data
Bryan Chi, Ronald J deLeeuw, Bradley P Coe, Raymond T Ng, Calum MacAulay, Wan L Lam
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-243
Abstract: To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations.MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. The deciphering of downstream effects of genetic and epigenetic alterations on expression patterns is paramount in understanding disease phenotype and requires the integration of segmental DNA copy number status, DNA methylation state and single nucleotide polymorphism (SNP) status. The large scale generation of such data has created a need for robust software to integrate multiple large genetically linked data sets generated on diverse microarray platforms. Although several visualization software programs are available publicly (for example [1-5]), there is a growing demand for new bioinformatics tools that allow for the concerted analysis of multiple genome-wide experiments derived from different experimental platforms [6]. Blue Fuse [7] and CGH Analytics [8], two commercially available software tools, offer integrative analysis with expression data but neither contain the full feature set that we deem necessary (Table 1). SeeGH (v1.6) was initially developed to view primarily array CGH data [2] but as we continued to use and develop the
Effect of active smoking on the human bronchial epithelium transcriptome
Raj Chari, Kim M Lonergan, Raymond T Ng, Calum MacAulay, Wan L Lam, Stephen Lam
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-297
Abstract: Twenty-four SAGE profiles of the bronchial epithelium of eight current, twelve former and four never smokers were generated and analyzed. In total, 3,111,471 SAGE tags representing over 110 thousand potentially unique transcripts were generated, comprising the largest human SAGE study to date. We identified 1,733 constitutively expressed genes in current, former and never smoker transcriptomes. We have also identified both reversible and irreversible gene expression changes upon cessation of smoking; reversible changes were frequently associated with either xenobiotic metabolism, nucleotide metabolism or mucus secretion. Increased expression of TFF3, CABYR, and ENTPD8 were found to be reversible upon smoking cessation. Expression of GSK3B, which regulates COX2 expression, was irreversibly decreased. MUC5AC expression was only partially reversed. Validation of select genes was performed using quantitative RT-PCR on a secondary cohort of nine current smokers, seven former smokers and six never smokers.Expression levels of some of the genes related to tobacco smoking return to levels similar to never smokers upon cessation of smoking, while expression of others appears to be permanently altered despite prolonged smoking cessation. These irreversible changes may account for the persistent lung cancer risk despite smoking cessation.Lung cancer has the highest mortality rate among all types of malignancies, accounting for approximately 29% of all cancer-related deaths in the United States [1]. It has been estimated that in 2006 alone, the number of new lung cancer cases will exceed 174,000 and approximately 163,000 people will die of this disease [1]. Tobacco smoking accounts for 85% of the lung cancers. Former heavy smokers remain at an elevated risk for developing lung cancer even years after they stop smoking [2,3]. Fifty percent of newly diagnosed lung cancer patients are former smokers [4]. It is therefore important to understand the effects of tobacco smoking on the
White Blood Cell Differentials Enrich Whole Blood Expression Data in the Context of Acute Cardiac Allograft Rejection
Casey P. Shannon,Zsuzsanna Hollander,Janet Wilson-McManus,Robert Balshaw,Raymond T. Ng
Bioinformatics and Biology Insights , 2012,
Abstract:
Disruption of the Non-Canonical WNT Pathway in Lung Squamous Cell Carcinoma
Eric H.L. Lee,Raj Chari,Andy Lam,Raymond T. Ng
Clinical Medicine : Oncology , 2008,
Abstract: Disruptions of beta-catenin and the canonical Wnt pathway are well documented in cancer. However, little is known of the non-canonical branch of the Wnt pathway. In this study, we investigate the transcript level patterns of genes in the Wnt pathway in squamous cell lung cancer using reverse-transcriptase (RT)-PCR. It was found that over half of the samples examined exhibited dysregulated gene expression of multiple components of the non-canonical branch of the WNT pathway. In the cases where beta catenin (CTNNB1) was not over-expressed, we identified strong relationships of expression between wingless-type MMTV integration site family member 5A (WNT5A)/frizzled homolog 2 (FZD2), frizzled homolog 3 (FZD3)/dishevelled 2 (DVL2), and low density lipoprotein receptor-related protein 5 (LRP5)/secreted frizzled-related protein 4 (SFRP4). This is one of the first studies to demonstrate expression of genes in the non-canonical pathway in normal lung tissue and its disruption in lung squamous cell carcinoma. These findings suggest that the non-canonical pathway may have a more prominent role in lung cancer than previously reported
Genomic imbalances in precancerous tissues signal oral cancer risk
Cathie Garnis, Raj Chari, Timon PH Buys, Lewei Zhang, Raymond T Ng, Miriam P Rosin, Wan L Lam
Molecular Cancer , 2009, DOI: 10.1186/1476-4598-8-50
Abstract: At present, risk of progression in oral premalignant lesions (OPLs) is typically determined based on histopathological evaluation of biopsied material. High grade dysplasia (HGD) and carcinoma in situ (CIS) are considered high risk for progression to invasive disease. In contrast, only a small proportion of low grade dysplasias (LGDs) – which represent the majority of diagnosed OPLs – progress to invasive disease [1,2]. Histological features cannot currently be used to delineate "progressing" and "non-progressing" LGDs [3]. Consequently, LGDs that are prime candidates for early intervention are not easily identified. Novel approaches for defining progression likelihood for histopathologically similar LGDs are required.Chromosome instability, particularly loss of chromosome arms 3p and 9p, has previously been associated with an increased probability of progression in oral cancer, demonstrating the potential utility of molecular markers in predicting progression risk [4-7]. Additionally, p53 status has been used to predict progression in Barretts esophagus and other groups have reported genomic instability in tumor-associated dysplastic oral tissue [8-11]. To date, efforts to undertake whole genome analysis of premalignant lesions have been precluded by 1) the rarity of LGD specimens with longitudinal follow-up and clinical outcome details and 2) the lack of robust high resolution genome profiling methodologies that can utilize the limited DNA yield from microdissected formalin-fixed paraffin-embedded lesions. In this study, we compared the genomes of precancerous oral tissues from different disease stages to identify stage-specific DNA alterations. Analysis of this rare sample set not only revealed qualitative and quantitative differences in DNA alterations depending on histopathological stage, but also showed that these features are associated with known clinical outcomes.Genome profiles were generated by tiling-path array CGH for a panel of 86 oral lesions with lon
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