A new attributed graph clustering by using label propagation in complex networks

Berahmand, Kamal; Haghani, Sogol; Rostami, Mehrdad; Li, Yuefeng

doi:10.1016/j.jksuci.2020.08.013

“…In other words, in many of medical and microarray datasets, it is possible that many genes are irrelevant or redundant for machine learning algorithm [29][30][31][32]. Feature selection or gene selection is a popular and powerful approach in medical datasets to overcome this shortcoming [33][34][35]. In gene selection, to decrease the microarray data dimensions, by eliminating the irrelevant and similar genes, only a subset of relevant and dissimilar genes that are strongly related to the objective function are selected [36].…”

Section: Feature Selectionmentioning

confidence: 99%

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

Ataei

¹

,

Majidi

²

,

Zahiri

³

et al. 2021

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Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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“…In other words, in many of medical and microarray datasets, it is possible that many genes are irrelevant or redundant for machine learning algorithm [29][30][31][32]. Feature selection or gene selection is a popular and powerful approach in medical datasets to overcome this shortcoming [33][34][35]. In gene selection, to decrease the microarray data dimensions, by eliminating the irrelevant and similar genes, only a subset of relevant and dissimilar genes that are strongly related to the objective function are selected [36].…”

Section: -3 Feature Selectionmentioning

confidence: 99%

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

Ataei

¹

,

Majidi

²

,

Zahiri

³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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“…Although several community detection approaches has been develped, the community detection of real social network with a large number attributes, remains challenging [13]. In this regard, similarity of nodes can be calculated using two criteria: structural similarity and attributed similarity.…”

Section: Weight Matrix Calculationmentioning

confidence: 99%

“…Several types of community detection methods can be distinguished. The traditional stream of methods focuses solely on the structural dimension of the social networks, i.e., the relationship between network nodes and ignore nodes' attributes [11][12][13]. A such approach uses nodes' structural similarity as a basis for social network analysis and community detection [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

Rostami¹,

Oussalah²

2021

Preprint

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0

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Community detection is one of the basic problems in social network analysis. Community detection on an attributed social networks aims to discover communities that have not only adhesive structure but also homogeneous node properties. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, a novel attributed community detection method through an integration of feature weighting with node centrality techniques is developed in this paper. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state of the art methods and ascertain the effectiveness of the developed method for attributed community detection.

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A new attributed graph clustering by using label propagation in complex networks

Cited by 42 publications

References 44 publications

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

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