Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Mushlin, Richard; Gallagher, Stephen; Kershenbaum, Aaron; Rebbeck, Timothy R.

doi:10.1371/journal.pone.0004862

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…However, these searches have not employed an exhaustive method that would evaluate all possible interactions while appropriately controlling for multiple comparisons. We sought to address this gap by applying the clique methodology with appropriate control for multiple comparisons to comprehensively evaluate all two‐way gene–gene interactions in our data 7. While multiple methods exist for searching for gene–gene interactions including classification and regression trees (CART) 8, multi‐factor dimensionality reduction 9, random forests 10, support vector machine 11, neural networks 12, and others, these approaches have important limitations.…”

Section: Introductionmentioning

confidence: 99%

Germline genetic variation and treatment response on CCG‐1891

Sepe

McWilliams

Chen

et al. 2011

Pediatric Blood & Cancer

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Background Recent studies suggest that polymorphisms in genes encoding enzymes involved in drug detoxification and metabolism may influence disease outcome in pediatric acute lymphoblastic leukemia (ALL). We sought to extend current knowledge by using standard and novel statistical methodology to examine polymorphic variants of genes and relapse risk, toxicity, and drug dose delivery in standard risk ALL. Procedure We genotyped and abstracted chemotherapy drug dose data from treatment roadmaps on 557 patients on the Children’s Cancer Group ALL study, CCG-1891. Fourteen common polymorphisms in genes involved in folate metabolism and/or phase I and II drug detoxification were evaluated individually and clique-finding methodology was employed for detection of significant gene-gene interactions. Results After controlling for known risk factors, polymorphisms in four genes: GSTP1*B (HR=1.94, p=0.047), MTHFR (HR=1.61, p=0.034), MTRR (HR=1.95, p=0.01), and TS (3R/4R, HR=3.69, p=0.007), were found to significantly increase relapse risk. One gene-gene pair, MTRR A/G and GSTM1 null genotype, significantly increased the risk of relapse after correction for multiple comparisons (p=0.012). Multiple polymorphisms were associated with various toxicities and there was no significant difference in dose of chemotherapy delivered by genotypes. Conclusions These data suggest that various polymorphisms play a role in relapse risk and toxicity during childhood ALL therapy and that genotype does not play a role in adjustment of drug dose administered. Additionally, gene-gene interactions may increase the risk of relapse in childhood ALL and the clique method may have utility in further exploring these interactions. childhood ALL therapy.

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Section: Introductionmentioning

confidence: 99%

Germline genetic variation and treatment response on CCG‐1891

Sepe

McWilliams

Chen

et al. 2011

Pediatric Blood & Cancer

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“…These include MDR,19 29 random forests,30 association rule discovery,31 and clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (CHAMBER) 32. While some algorithms have been successful in accommodating the problem of heterogeneity, explicit characterization has remained a major challenge.…”

Section: Background and Significancementioning

confidence: 99%

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

Urbanowicz

Andrew

Karagas

et al. 2013

J Am Med Inform Assoc

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Background and objectiveDetecting complex patterns of association between genetic or environmental risk factors and disease risk has become an important target for epidemiological research. In particular, strategies that provide multifactor interactions or heterogeneous patterns of association can offer new insights into association studies for which traditional analytic tools have had limited success.Materials and methodsTo concurrently examine these phenomena, previous work has successfully considered the application of learning classifier systems (LCSs), a flexible class of evolutionary algorithms that distributes learned associations over a population of rules. Subsequent work dealt with the inherent problems of knowledge discovery and interpretation within these algorithms, allowing for the characterization of heterogeneous patterns of association. Whereas these previous advancements were evaluated using complex simulation studies, this study applied these collective works to a ‘real-world’ genetic epidemiology study of bladder cancer susceptibility.Results and discussionWe replicated the identification of previously characterized factors that modify bladder cancer risk—namely, single nucleotide polymorphisms from a DNA repair gene, and smoking. Furthermore, we identified potentially heterogeneous groups of subjects characterized by distinct patterns of association. Cox proportional hazard models comparing clinical outcome variables between the cases of the two largest groups yielded a significant, meaningful difference in survival time in years (survivorship). A marginally significant difference in recurrence time was also noted. These results support the hypothesis that an LCS approach can offer greater insight into complex patterns of association.ConclusionsThis methodology appears to be well suited to the dissection of disease heterogeneity, a key component in the advancement of personalized medicine.

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“…However, in many biomedical applications, researchers are interested in finding small and highly connected network patterns rather than network partitions. These network patterns may connect to potential biomarkers and lead to novel scientific discovery (Kutalik et al 2008;Mushlin et al 2009;Ravetti 2008;Zhang et al 2010). As a result, methods in this category are not sufficient for these types of applications.…”

Section: Network Partitionmentioning

confidence: 99%

Merging network patterns: a general framework to summarize biomedical network data

Xiang

Fuhry

Kaya

et al. 2012

Netw Model Anal Health Inform Bioinforma

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The ability to summarize a large number of network patterns discovered from biomedical data provides valuable information for use in many applications. We show that several variants of the problem are all NP-hard, and merging network patterns is a practical solution for these applications. In this work, we propose an algorithmic framework for merging network patterns. We have developed fast algorithms under this general framework which supports several types of biomedical network data. In addition, our empirical study demonstrates that our algorithms are efficient in merging a large number of biomedical network patterns and can be configured for various knowledge discovery purposes.

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Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Cited by 7 publications

References 37 publications

Germline genetic variation and treatment response on CCG‐1891

Germline genetic variation and treatment response on CCG‐1891

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

Merging network patterns: a general framework to summarize biomedical network data

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