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- Foo, Jia Nee, et al.
(författare)
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Coding Variants at Hexa-allelic Amino Acid 13 of HLA-DRB1 Explain Independent SNP Associations with Follicular Lymphoma Risk
- 2013
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Ingår i: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 93:1, s. 167-172
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Tidskriftsartikel (refereegranskat)abstract
- Non-Hodgkin lymphoma represents a diverse group of blood malignancies, of which follicular lymphoma (FL) is a common subtype. Previous genome-wide association studies (GWASs) have identified in the human leukocyte antigen (HLA) class II region multiple independent SNPs that are significantly associated with FL risk. To dissect these signals and determine whether coding variants in HLA genes are responsible for the associations, we conducted imputation, HLA typing, and sequencing in three independent populations for a total of 689 cases and 2,446 controls. We identified a hexa-allelic amino acid polymorphism at position 13 of the HLA-DR beta chain that showed the strongest association with FL within the major histocompatibility complex (MHC) region (multiallelic p = 2.3 x 10(-15)). Out of six possible amino acids that occurred at that position within the population, we classified two as high risk (Tyr and Phe), two as low risk (Ser and Arg), and two as moderate risk (His and Gly). There was a 4.2-fold difference in risk (95% confidence interval = 2.9-6.1) between subjects carrying two alleles encoding high-risk amino acids and those carrying two alleles encoding low-risk amino acids (p = 1.01 x 10(-14)). This coding variant might explain the complex SNP associations identified by GWASs and suggests a common HLA-DR antigen-driven mechanism for the pathogenesis of FL and rheumatoid arthritis.
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- Ng, Theam Foo, et al.
(författare)
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Automated feature weighting in fuzzy declustering-based vector quantization
- 2010
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Konferensbidrag (refereegranskat)abstract
- Feature weighting plays an important role in improving the performance of clustering technique. We propose an automated feature weighting in fuzzy declustering-based vector quantization (FDVQ), namely AFDVQ algorithm, for enhancing effectiveness and efficiency in classification. The proposed AFDVQ imposes weights on the modified fuzzy c-means (FCM) so that it can automatically calculate feature weights based on their degrees of importance rather than treating them equally. Moreover, the extension of FDVQ and AFDVQ algorithms based on generalized improved fuzzy partitions (GIFP), known as GIFP-FDVQ and GIFP-AFDVQ respectively, are proposed. The experimental results on real data (original and noisy data) and modified data (biased and noisy-biased data) have demonstrated that the proposed algorithms outperformed standard algorithms in classifying clusters especially for biased data.
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- Ng, Theam Foo, et al.
(författare)
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Feature interaction in subspace clustering using the Choquet integral
- 2012
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:7, s. 2645-2660
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Tidskriftsartikel (refereegranskat)abstract
- Subspace clustering has recently emerged as a popular approach to removing irrelevant and redundant features during the clustering process. However, most subspace clustering methods do not consider the interaction between the features. This unawareness limits the analysis performance in many pattern recognition problems. In this paper, we propose a novel subspace clustering technique by introducing the feature interaction using the concepts of fuzzy measures and the Choquet integral. This new framework of subspace clustering can provide optimal subsets of interacted features chosen for each cluster, and hence can improve clustering-based pattern recognition tasks. Various experimental results illustrate the effective performance of the proposed method.
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- Ng, Theam Foo, et al.
(författare)
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Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis
- 2013
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Ingår i: Knowledge-Based Systems in Biomedicine and Computational Life Science. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642330148 - 9783642330155 ; , s. 177-213
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Features or attributes play an important role when handling multi-dimensional datasets. Generally, not all the features are needed to find several groups of similar objects in traditional clustering methods because some of the features may not be relevant and also redundant. Hence, the concept of identifying subsets of the features that are relevant to clusters is introduced, instead of using the full set of features. This chapter discusses the use of the prior knowledge of the importance of features and their interaction in constructing both fuzzy measures and signed fuzzy measures for subspace clustering. The Choquet integral, which is known as a useful aggregation operator with respect to fuzzy measure, is used to aggregate the importance and interaction of the features. The concept of fuzzy knowledge-based subspace clustering is applied especially to the analysis of life science data in this chapter.
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- Ng, Theam Foo, et al.
(författare)
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Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes
- 2010
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Konferensbidrag (refereegranskat)abstract
- With the fast development of multi‐dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering‐based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype‐image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ‐HMM) and a well‐known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG‐HMM) will be carried out. The experimental results show that the performances of both FDVQ‐HMM and LBG‐HMM are almost similar. Finally, we have justified the competitiveness of FDVQ‐HMM in classification of cellular phenotype image database by using hypotheses t‐test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome‐wide screening image data.
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- Skibola, Christine F, et al.
(författare)
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Genome-wide Association Study Identifies Five Susceptibility Loci for Follicular Lymphoma outside the HLA Region.
- 2014
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Ingår i: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 95:4, s. 462-471
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWASs) of follicular lymphoma (FL) have previously identified human leukocyte antigen (HLA) gene variants. To identify additional FL susceptibility loci, we conducted a large-scale two-stage GWAS in 4,523 case subjects and 13,344 control subjects of European ancestry. Five non-HLA loci were associated with FL risk: 11q23.3 (rs4938573, p = 5.79 × 10(-20)) near CXCR5; 11q24.3 (rs4937362, p = 6.76 × 10(-11)) near ETS1; 3q28 (rs6444305, p = 1.10 × 10(-10)) in LPP; 18q21.33 (rs17749561, p = 8.28 × 10(-10)) near BCL2; and 8q24.21 (rs13254990, p = 1.06 × 10(-8)) near PVT1. In an analysis of the HLA region, we identified four linked HLA-DRβ1 multiallelic amino acids at positions 11, 13, 28, and 30 that were associated with FL risk (pomnibus = 4.20 × 10(-67) to 2.67 × 10(-70)). Additional independent signals included rs17203612 in HLA class II (odds ratio [ORper-allele] = 1.44; p = 4.59 × 10(-16)) and rs3130437 in HLA class I (ORper-allele = 1.23; p = 8.23 × 10(-9)). Our findings further expand the number of loci associated with FL and provide evidence that multiple common variants outside the HLA region make a significant contribution to FL risk.
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