Comparison of Attributed Network Clustering Approaches to Analyzing Seoul Public Bike Stations

Aims: We aimed to find common and distinct molecular characteristics between LIHC and CHOL based on miRNA-TF-gene FFL. Background: Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are two main histological subtypes of primary liver cancer with a unified molecular landscape, and feed-forward loops (FFLs) have been shown to be relevant in these complex diseases. Objective: To date, there has been no comparative analysis of the pathogenesis of LIHC and CHOL based on regulatory relationships. Therefore, we investigated the common and distinct regulatory properties of LIHC and CHOL in terms of gene regulatory networks. Method: Based on identified FFLs and an analysis of pathway enrichment, we constructed pathway-specific co-expression networks and further predicted biomarkers for these cancers by network clustering. Resul: We identified 20 and 36 candidate genes for LIHC and CHOL, respectively. The literature from PubMed supports the reliability of our results. Conclusion: Our results indicated that the hsa01522-Endocrine resistance pathway was associated with both LIHC and CHOL. Additionally, six genes (SPARC, CTHRC1, COL4A1, EDIL3, LAMA4 and OLFML2B) were predicted to be highly associated with both cancers, of which SPARC was significantly highly ranked. Other: In addition, we inferred that the Collagen gene family, which appeared more frequently in our overall prediction results, might be closely related to cancer development.

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Efficient Airborne Network Clustering for 5G Backhauling and Fronthauling

2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)(50308) ◽

10.1109/wimob50308.2020.9253390 ◽

2020 ◽

Author(s):

Hloniphani Maluleke ◽

Antoine Bagula ◽

Olasupo Ajayi

Keyword(s):

Network Clustering

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Attributed Network Embedding based on Mutual Information Estimation

Proceedings of the 29th ACM International Conference on Information & Knowledge Management ◽

10.1145/3340531.3412008 ◽

2020 ◽

Author(s):

Xiaomin Liang ◽

Daifeng Li ◽

Andrew Madden

Keyword(s):

Mutual Information ◽

Network Embedding ◽

Attributed Network

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Development of a biomarker database toward performing disease classification and finding disease interrelations

Database ◽

10.1093/database/baab011 ◽

2021 ◽

Vol 2021 ◽

Author(s):

Shaikh Farhad Hossain ◽

Ming Huang ◽

Naoaki Ono ◽

Aki Morita ◽

Shigehiko Kanaya ◽

...

Keyword(s):

Rapid Detection ◽

Molecular Mechanisms ◽

Clustering Algorithm ◽

Disease Diagnosis ◽

Disease Classification ◽

Network Clustering ◽

Medical Field ◽

Database Development ◽

Abnormal State ◽

Comprehensive Information

Abstract A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL: http://www.knapsackfamily.com/Biomarker/top.php

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Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition

IEEE Transactions on Evolutionary Computation ◽

10.1109/tevc.2013.2260862 ◽

2014 ◽

Vol 18 (1) ◽

pp. 82-97 ◽

Cited By ~ 215

Author(s):

Maoguo Gong ◽

Qing Cai ◽

Xiaowei Chen ◽

Lijia Ma

Keyword(s):

Particle Swarm Optimization ◽

Complex Network ◽

Particle Swarm ◽

Network Clustering ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle

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Incremental Community Detection on Large Complex Attributed Network

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3451216 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-20

Author(s):

Zhe Chen ◽

Aixin Sun ◽

Xiaokui Xiao

Keyword(s):

Community Detection ◽

Large Scale ◽

Network Data ◽

Topological Information ◽

Community Membership ◽

Attributed Network ◽

Benchmark Datasets ◽

Modularity Maximization ◽

Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

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