Link-Prediction Enhanced Consensus Clustering for Complex Networks

Burgess, Matthew; Adar, Eytan; Cafarella, Michael

doi:10.1371/journal.pone.0153384

Cited by 41 publications

(26 citation statements)

References 44 publications

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“…6) can significantly improve the quality of the resulting clustering solutions. A computational approach, which could potentially alleviate some of the demand for increased depth has been proposed (EdgeBoost) (Burgess, Adar & Cafarella, 2015) and shown to improve both Louvain and label propagation methods, is a clear candidate for future assessment.…”

Section: Discussionmentioning

confidence: 99%

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

DeMaere

Darling

2016

PeerJ

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BackgroundChromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as a viable means of generating data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised.MethodsWe developed a computational simulation pipeline for metagenomic 3C and Hi-C sequencing to evaluate the accuracy of genomic reconstructions at, above, and below an operationally defined species boundary. We simulated datasets and measured accuracy over a wide range of parameters. Five clustering algorithms were evaluated (2 hard, 3 soft) using an adaptation of the extended B-cubed validation measure.ResultsWhen all genomes in a sample are below 95% sequence identity, all of the tested clustering algorithms performed well. When sequence data contains genomes above 95% identity (our operational definition of strain-level diversity), a naive soft-clustering extension of the Louvain method achieves the highest performance.DiscussionPreviously, only hard-clustering algorithms have been applied to metagenomic 3C and Hi-C data, yet none of these perform well when strain-level diversity exists in a metagenomic sample. Our simple extension of the Louvain method performed the best in these scenarios, however, accuracy remained well below the levels observed for samples without strain-level diversity. Strain resolution is also highly dependent on the amount of available 3C sequence data, suggesting that depth of sequencing must be carefully considered during experimental design. Finally, there appears to be great scope to improve the accuracy of strain resolution through further algorithm development.

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Section: Discussionmentioning

confidence: 99%

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

DeMaere

Darling

2016

PeerJ

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“…In addition, attackers might infer an anonymous user through the existence of connections between users. For example, constructing a probability forecasting for edges, multiplicative weighting and min‐flow analytical scheme . The results in Feng et al show that the increases of local connections and clustering coefficient in social network will lead to the accuracy of the link prediction algorithm enhance.…”

Section: Techniques For Big Data Security and Privacymentioning

confidence: 99%

Challenges and techniques in Big data security and privacy: A review

Bao

Chen

Obaidat

2018

Security and Privacy

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With the rapid development of information technology, Big data has become a hot topic of research in governments, academia, and enterprises. On the one hand, Big data brings opportunities that are not available by small‐scale data in many fields, such as business, education, and healthcare. On the other hand, due to the huge amount, the development of Big data is still facing many security and privacy issues in the whole lifetime of Big data. Security and privacy issues not only plague users and businesses, but also create obstacles to the expected opportunities and progress of Big data. In this study, we first review challenges in Big data security and privacy, especially considering the challenges in different lifetime of Big data. Then we review four popular secure technologies: homomorphic encryption, secure multiparty computation, attribute‐based encryption, and anonymous protection in social network. Based on the reviews, we identify recommendations and 3 possible directions for security and privacy protection of Big data.

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“…We have shown that increase in 3C sampling depth (Figure 6) can significantly improve the quality of the resulting clustering solutions. A computational approach, which could potentially alleviate some of the demand for increased depth has been proposed (EdgeBoost) (Burgess et al, 2015) and shown to improve both Louvain and label propagation methods, is a clear candidate for future assessment.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

DeMaere

Darling

2016

Preprint

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Background. Chromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as viable means to generate data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised.

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Link-Prediction Enhanced Consensus Clustering for Complex Networks

Cited by 41 publications

References 44 publications

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

Challenges and techniques in Big data security and privacy: A review

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C)

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