Protein Complex Detection Based on Semi-Supervised Matrix Factorization

Wu, Jie; Lin, Mingxiong

doi:10.23919/chicc.2018.8484055

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Existing community detection methods have primarily tried to optimize for high scores of community fitness functions, such as that of Eq 1 [5]. These include unsupervised methods, such as implemented by MCL-Markov Clustering [6], MCODE-Molecular COmplex DEtection [7], CFinder [8], SCAN-Structural Clustering Algorithm for Networks [9], CMC-Clustering based on Maximal Cliques [10], COACH-COre-AttaCHment based method [11], GCE-Greedy Clique Expansion [5], and ClusterONE-clustering with overlapping neighborhood expansion [12], as well as semi-supervised machine learning algorithms such as COCDM-Constrained Overlapping Complex Detection Model [13].…”

Section: Introductionmentioning

confidence: 99%

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Palukuri

Marcotte

2021

PLoS ONE

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Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

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Section: Introductionmentioning

confidence: 99%

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Palukuri

Marcotte

2021

PLoS ONE

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show abstract

“…Existing community detection methods have primarily tried to optimize for high scores of community fitness functions, such as that of equation 1 [5]. These include unsupervised methods, such as implemented by MCL-Markov Clustering [6], MCODE -Molecular COmplex DEtection [7], CFinder [8], SCAN-Structural Clustering Algorithm for Networks [9], CMC -Clustering based on Maximal Cliques [10], COACH -COre-AttaCHment based method [11], GCE -Greedy Clique Expansion [5], and ClusterONE -clustering with overlapping neighborhood expansion [12], as well as semi-supervised machine learning algorithms such as COCDM -Constrained Overlapping Complex Detection Model [13].…”

Section: Introductionmentioning

confidence: 99%

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Palukuri

Marcotte

2021

Preprint

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Protein complexes can be computationally identified from protein-interaction networks with community detection methods, suggesting new multi-protein assemblies. Most community detection algorithms tend to be un- or semi-supervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and by using parallel algorithms, respectively. Here, we present Super.Complex, a distributed supervised machine learning pipeline for community detection in networks. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities with epsilon-greedy and pseudo-metropolis criteria, and an embarrassingly parallel implementation can be run on a computer cluster for scaling to large networks. In order to evaluate Super.Complex, we propose three new measures for the still outstanding issue of comparing sets of learned and known communities. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable and can be used in different applications of community detection, with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0 .

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Protein Complex Detection Based on Semi-Supervised Matrix Factorization

Cited by 2 publications

References 13 publications

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

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