| 21. |
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| 22. |
- Spurdle, Amanda B., et al.
(författare)
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Common Genetic Variation at BARD1 Is Not Associated with Breast Cancer Risk in BRCA1 or BRCA2 Mutation Carriers
- 2011
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Ingår i: Cancer Epidemiology Biomarkers & Prevention. - 1538-7755 .- 1055-9965. ; 20:5, s. 1032-1038
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Tidskriftsartikel (refereegranskat)abstract
- Background: Inherited BRCA1 and BRCA2 (BRCA1/2) mutations confer elevated breast cancer risk. Knowledge of factors that can improve breast cancer risk assessment in BRCA1/2 mutation carriers may improve personalized cancer prevention strategies. Methods: A cohort of 5,546 BRCA1 and 2,865 BRCA2 mutation carriers was used to evaluate risk of breast cancer associated with BARD1 Cys557Ser. In a second nonindependent cohort of 1,537 of BRCA1 and 839 BRCA2 mutation carriers, BARD1 haplotypes were also evaluated. Results: The BARD1 Cys557Ser variant was not significantly associated with risk of breast cancer from single SNP analysis, with a pooled effect estimate of 0.90 (95% CI: 0.71-1.15) in BRCA1 carriers and 0.87 (95% CI: 0.59-1.29) in BRCA2 carriers. Further analysis of haplotypes at BARD1 also revealed no evidence that additional common genetic variation not captured by Cys557Ser was associated with breast cancer risk. Conclusion: Evidence to date does not support a role for BARD1 variation, including the Cy557Ser variant, as a modifier of risk in BRCA1/2 mutation carriers. Impact: Interactors of BRCA1/2 have been implicated as modifiers of BRCA1/2-associated cancer risk. Our finding that BARD1 does not contribute to this risk modification may focus research on other genes that do modify BRCA1/2-associated cancer risk. Cancer Epidemiol Biomarkers Prev; 20(5); 1032-38. (C) 2011 AACR.
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| 23. |
- Kriege, Nils M., et al.
(författare)
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A survey on graph kernels
- 2020
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Ingår i: Applied Network Science. - : Springer Science and Business Media LLC. - 2364-8228. ; 5:1
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Forskningsöversikt (refereegranskat)abstract
- Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.
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| 24. |
- Oettershagen, Lutz, et al.
(författare)
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A Higher-Order Temporal H-Index for Evolving Networks
- 2023
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Ingår i: KDD 2023. - : Association for Computing Machinery (ACM). ; , s. 1770-1782
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Konferensbidrag (refereegranskat)abstract
- The H-index of a node in a static network is the maximum value h such that at least h of its neighbors have a degree of at least h. Recently, a generalized version, the n-th order H-index, was introduced, allowing to relate degree centrality, H-index, and the k-core of a node. We extend the n-th order H-index to temporal networks and define corresponding temporal centrality measures and temporal core decompositions. Our n-th order temporal H-index respects the reachability in temporal networks leading to node rankings, which reflect the importance of nodes in spreading processes. We derive natural decompositions of temporal networks into subgraphs with strong temporal coherence. We analyze a recursive computation scheme and develop a highly scalable streaming algorithm. Our experimental evaluation demonstrates the efficiency of our algorithms and the conceptional validity of our approach. Specifically, we show that the n-th order temporal H-index is a strong heuristic for identifying possible super-spreaders in evolving social networks and detects temporally well-connected components.
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