A network analysis to identify lung cancer comorbid diseases

Rustamaji, Heru Cahya; Suharini, Yustina Sri; Permana, Angga Aditya; Kusuma, Wisnu Ananta; Nurdiati, Sri; Batubara, Irmanida; Djatna, Taufik

doi:10.1007/s41109-022-00466-y

Cited by 8 publications

(8 citation statements)

References 74 publications

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“…The spinglass algorithm is a widely used clustering technique that utilizes the 'Potts model' statistical mechanics [47] to connect nodes with the same spin state and disconnect those with different spin states. The algorithm employs simulated annealing techniques to reduce the resource requirements, as described in [48]. The Spinglass algorithm has several technical and statistical parameters, including the temperature schedule, interaction strength, randomness, initialization, spin update rule, spin glass Hamiltonian formulation, and convergence criteria.…”

Section: ) Spinglassmentioning

confidence: 99%

Partitioning Graph Clustering With User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

IEEE Access

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Graph clustering has attracted many interests in recent years, with numerous applications ranging from the clustering of computer networks to the detection of social communities. It presents a challenging NP-class problem, and as a result, numerous algorithms have been developed, each tailored to specific objectives and quality metrics for evaluation. This research commences by categorizing existing graph clustering algorithms based on two distinct perspectives: parameter-free algorithms and user-defined or adjustable parametric algorithms. Quality metrics are further categorized into three distinct groups: internal connectivity, external connectivity, and a combination of both. If a task can be represented by a simple undirected and unweighted graph, from a management and deployment of resources perspective, having clusters of some kind of similar density is advantageous as it allows efficient management. This research introduces a partitioning graph clustering algorithm that allows users to specify the desired density of a cluster by means of 'relative density'. Clustering process involves the determination of all triangles (i.e., smallest cliques) and selecting a clique as an initial cluster. The expansion of a cluster is done by adding adjacent cliques while the required relative density is monitored. Existing metrics are found unsuitable for evaluating the proposed method; therefore, a suitable new metric, the Mean Relative Density Deviation Coefficient (MRDDC), is introduced.

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Section: ) Spinglassmentioning

confidence: 99%

Partitioning Graph Clustering With User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

IEEE Access

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“…However, if there are more edges, there will be a high chance of false positives occurring, even though the network is denser. In similar research, a threshold of 0.5 was also chosen for this calculation [11].…”

Section: Network Formationmentioning

confidence: 99%

“…To represent its data, DO uses the directed acyclic graph (DAG) form. With DAG representation, it will be easier to maintain and track such complex data [11]. To gain a better understanding of the Disease Ontology entry, an example of a disease information in Disease Ontology is shown in Table 2.…”

Section: Research Stepsmentioning

confidence: 99%

“…Comorbidity discovery with community detection algorithms is discussed in several research papers. Algorithms for disease ontology (DO) similarity, the best algorithms for revealing lung cancer comorbid disease, calculation of fitness score, and several significant lung cancer comorbidities were discussed in the research conducted by Rustamaji et al in the paper titled "A Network Analysis to Identify Lung Cancer Comorbid Disease," released in 2022 [11]. Hepatocellular carcinoma comorbidity risk is discussed by Mu et al in the paper titled "Patterns of Comorbidity in Hepatocellular Carcinoma: A Network Perspective" [12].…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Comorbidities Network Analysis: Break the Code with Optimum Community Detection Algorithms

Permana,

Yaputra

2023

Preprint

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Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. This research aims to discover the breast cancer comorbidity diseases community with community detection algorithms, evaluate them based on modularity and similarity, suggest the best semantic similarity measurement and the optimal threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden-Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain-Leiden (0.99), RB Pots-Leiden (0.97), and RB Pots-RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed upon by four algorithms, two diseases are agreed upon by three algorithms, a disease is agreed upon by two algorithms, and ten diseases are agreed upon by an algorithm.

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“…When applied to a network for community detection, these strategies produce different results. Each algorithm provides different community results and different modularity 16 .…”

Section: Introductionmentioning

confidence: 99%

Community detection with greedy modularity disassembly strategy

Rustamaji¹,

Kusuma²,

Nurdiati³

et al. 2023

Preprint

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One of the problems in networking is community detection, the process of identifying groups of nodes in a network that are more densely connected than the rest of the network. To measure the quality of community detection in the network using modularity. Greedy modularity is one of the community detection algorithms. It optimizes the modularity score locally at each step rather than globally optimizing over all possible network partitions. However, because the algorithm makes local improvements, it can be trapped in a suboptimal network partition. In this research, we developed the Greedy Modularity algorithm by adding an exploration method: a disassembly node and disassembly community formed to increase modularity. There are four strategies for disassembly nodes: disassembly random nodes, weak nodes, nodes with low embeddedness values, and releasing nodes that do not form triads. The disassembly community strategy includes random communities, weak communities, communities with low internal edge density, communities with low triad participation ratio, and communities with low conductance. The results show increased modularity in various real-world and synthetic network datasets. We also compared the results with various community detection algorithms, and the modularity ranking of these algorithms were in positions 1-8 across various datasets.

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A network analysis to identify lung cancer comorbid diseases

Cited by 8 publications

References 74 publications

Partitioning Graph Clustering With User-Specified Density

Partitioning Graph Clustering With User-Specified Density

Breast Cancer Comorbidities Network Analysis: Break the Code with Optimum Community Detection Algorithms

Community detection with greedy modularity disassembly strategy

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