Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks

Lei, Xiujuan; Wang, Fei; Wu, Fang‐Xiang; Zhang, Aidong; Pedrycz, Witold

doi:10.1016/j.ins.2015.09.028

Cited by 69 publications

(34 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Markov clustering algorithm (MCL) tries to simulate a stochastic flow within the network structure, strengthening the flow where nodes are highly interconnected and weakening it in other regions until the flow process stabilizes [74]. MCL has found great popularity in network biology and has been used to characterize protein families within protein networks [75], detect orthologous and homologous groups [76], predict protein complexes from protein interaction networks [77], find gene clusters base on expression profiles [78], and to find clusters of putative pathogens and growth-promoting bacteria in phyllosphere microbiome [23].…”

Section: Topological Clustering Methods For Partitioning Data Into Bimentioning

confidence: 99%

Disentangling Interactions in the Microbiome: A Network Perspective

Layeghifard

Hwang

Guttman

2017

Trends in Microbiology

581

387

View full text Add to dashboard Cite

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

show abstract

Section: Topological Clustering Methods For Partitioning Data Into Bimentioning

confidence: 99%

Disentangling Interactions in the Microbiome: A Network Perspective

Layeghifard

Hwang

Guttman

2017

Trends in Microbiology

581

387

View full text Add to dashboard Cite

show abstract

“…In order to consider interwoven relationships between parts in a product, Markov Cluster Algorithm (MCL) is applied to group parts with high complexity into a module for AM. The MCL is used to cluster complex biological networks in the field of bioinformatics [40,41]. The MCL is a fast and scalable unsupervised clustering algorithm based on the mathematical concept of random walks.…”

Section: Module Identification Based On Graph Clusteringmentioning

confidence: 99%

A Part Consolidation Design Method for Additive Manufacturing based on Product Disassembly Complexity

Kim

Moon

2020

Applied Sciences

View full text Add to dashboard Cite

Parts with complex geometry have been divided into multiple parts due to manufacturing constraints of conventional manufacturing. However, since additive manufacturing (AM) is able to fabricate 3D objects in a layer-by-layer manner, design for AM has been researched to explore AM design benefits and alleviate manufacturing constraints of AM. To explore more AM design benefits, part consolidation has been researched for consolidating multiple parts into fewer number of parts at the manufacturing stage of product lifecycle. However, these studies have been less considered product recovery and maintenance at end-of-life stage. Consolidated parts for the manufacturing stage would not be beneficial at end-of-life stage and lead to unnecessary waste of materials during maintenance. Therefore, in this research, a design method is proposed to consolidate parts for considering maintenance and product recovery at the end-of-life stage by extending a modular identification method. Single part complexity index (SCCI) is introduced to measure part and interface complexities simultaneously. Parts with high SCCI values are grouped into modules that are candidates for part consolidation. Then the product disassembly complexity (PDC) can be used to measure disassembly complexity of a product before and after part consolidation. A case study is performed to demonstrate the usefulness of the proposed design method. The proposed method contributes to guiding how to consolidate parts for enhancing product recovery.

show abstract

“…Saranan Fister et al (2013) terbukti berkesan apabila beberapa penyelidik lain data seperti Jain et al (2017) telah menggunakan AKK dan Set Kasar untuk mengelompok imej. Hassanzadeh dan Meybodi (2012) telah menghibrid AKK dan K-means manakala Lei et al (2016) menggunakan AKK untuk mengenalpasti protein. Nayak et al (2014) telah menghibrid AKK dengan C-Min Kabur untuk mengelompok data dan Mohammed et al (2014) menggunakan AKK untuk mengelompok dokumen setelah AKK diubahsuai.…”

Section: Sorotan Kajian Pembelajaran Taksonomi Dari Teksunclassified