Cluster Editing with Vertex Splitting

Abu-Khzam, Faisal N.; Egan, Judith; Gaspers, Serge; Shaw, Alexis; Shaw, Peter

doi:10.1007/978-3-319-96151-4_1

Cited by 12 publications

(13 citation statements)

References 30 publications

(45 reference statements)

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“…The results of this analysis can then be used to develop prognostic value tools, such as decision trees. By coupling a random forest analysis with the other ensemble methods, such as those provided by the R interface for ‘H2O’(R H2O package), the scalable open source machine learning platform, artificial intelligence (AI) prognosis tools could also be produced [ 69 ]. Nevertheless, a random forest analysis is also an effective way of identifying robust features for predictive modelling.…”

Section: Methodsmentioning

confidence: 99%

Molecular Investigation of miRNA Biomarkers as Chemoresistance Regulators in Melanoma: A Protocol for Systematic Review and Meta-Analysis

Shaw

Raymond

Tzou

et al. 2022

Genes

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Introduction: Melanoma is a global disease that is predominant in Western countries. However, reliable data resources and comprehensive studies on the theragnostic efficiency of miRNAs in melanoma are scarce. Hence, a decisive study or comprehensive review is required to collate the evidence for profiling miRNAs as a theragnostic marker. This protocol details a comprehensive systematic review and meta-analysis on the impact of miRNAs on chemoresistance and their association with theragnosis in melanoma. Methods and analysis: The articles will be retrieved from online bibliographic databases, including Cochrane Review, EMBASE, MEDLINE, PubMed, Scopus, Science Direct, and Web of Science, with different permutations of ‘keywords’. To obtain full-text papers of relevant research, a stated search method will be used, along with selection criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols 2015 (PRISMA-P) standards were used to create this study protocol. The hazard ratio (HR) with a 95% confidence interval will be analyzed using Comprehensive Meta-Analysis (CMA) software 3.0. (CI). The pooled effect size will be calculated using a random or fixed-effects meta-analysis model. Cochran’s Q test and the I2 statistic will be used to determine heterogeneity. Egger’s bias indicator test, Orwin’s and the classic fail-safe N tests, the Begg and Mazumdar rank collection test, and Duval and Tweedie’s trim and fill calculation will all be used to determine publication bias. The overall standard deviation will be evaluated using Z-statistics. Subgroup analyses will be performed according to the melanoma participants’ clinicopathological and biological characteristics and methodological factors if sufficient studies and retrieved data are identified and available. The source of heterogeneity will be assessed using a meta-regression analysis. A pairwise matrix could be developed using either a pairwise correlation or expression associations of miRNA with patients’ survival for the same studies.

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Section: Methodsmentioning

confidence: 99%

Molecular Investigation of miRNA Biomarkers as Chemoresistance Regulators in Melanoma: A Protocol for Systematic Review and Meta-Analysis

Shaw

Raymond

Tzou

et al. 2022

Genes

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“…As for the running time, since each part G i contains less than 2k 2 vertices, we have for each edge set that |E i | ≤ 2k 4 and hence…”

Section: Lemma 11 Reduction Rule 6a Is Correctmentioning

confidence: 99%

“…Being NP-hard, Cluster editing gained high popularity in studies concerning parameterized algorithmics, e.g. [1,4,9,11,14,23,26,29,33]. To the best of our knowledge, to date these parameterized studies mostly focus on a "static scenario".…”

Section: Introductionmentioning

confidence: 99%

Parameterized Dynamic Cluster Editing

et al. 2020

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We introduce a dynamic version of the -hard graph modification problem Cluster Editing. The essential point here is to take into account dynamically evolving input graphs: having a cluster graph (that is, a disjoint union of cliques) constituting a solution for a first input graph, can we cost-efficiently transform it into a “similar” cluster graph that is a solution for a second (“subsequent”) input graph? This model is motivated by several application scenarios, including incremental clustering, the search for compromise clusterings, or also local search in graph-based data clustering. We thoroughly study six problem variants (three modification scenarios edge editing, edge deletion, edge insertion; each combined with two distance measures between cluster graphs). We obtain both fixed-parameter tractability as well as (parameterized) hardness results, thus (except for three open questions) providing a fairly complete picture of the parameterized computational complexity landscape under the two perhaps most natural parameterizations: the distances of the new “similar” cluster graph to (1) the second input graph and to (2) the input cluster graph.

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“…The problem seeks a transformation of an input graph into a disjoint union of cliques via a user-specified (or minimum) number of edge editing operations. Cluster Editing received a notable attention in the parameterized complexity literature [1,2,6,7,12,13,14,15,17,22], and it has found application in various practical settings [3,4,5,8,9,10,16]. In various application scenarios, the requirement for clusters to be cliques is found to be too restrictive; hence, some relaxed clique models for dense subgraph have been proposed as alternatives.…”

Section: Introductionmentioning

confidence: 99%

An Improved Fixed-Parameter Algorithm for 2-Club Cluster Edge Deletion

Abu-Khzam¹,

Makarem²,

Shehab³

2021

Preprint

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A 2-club is a graph of diameter at most two. In the decision version of the parametrized 2-CLUB CLUSTER EDGE DELETION problem, an undirected graph G is given along with an integer k ≥ 0 as parameter, and the question is whether G can be transformed into a disjoint union of 2-clubs by deleting at most k edges. A simple fixed-parameter algorithm solves the problem in O * (3 k ), and a decade-old algorithm was claimed to have an improved running time of O * (2.74 k ) via a sophisticated case analysis. Unfortunately, this latter algorithm suffers from a flawed branching scenario. In this paper, an improved fixed-parameter algorithm is presented with a running time in O * (2.695 k ).

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Cluster Editing with Vertex Splitting

Cited by 12 publications

References 30 publications

Molecular Investigation of miRNA Biomarkers as Chemoresistance Regulators in Melanoma: A Protocol for Systematic Review and Meta-Analysis

Molecular Investigation of miRNA Biomarkers as Chemoresistance Regulators in Melanoma: A Protocol for Systematic Review and Meta-Analysis

Parameterized Dynamic Cluster Editing

An Improved Fixed-Parameter Algorithm for 2-Club Cluster Edge Deletion

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