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A two-layer integration framework for protein complex detection

Overview of attention for article published in BMC Bioinformatics, February 2016
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Title
A two-layer integration framework for protein complex detection
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0939-3
Pubmed ID
Authors

Le Ou-Yang, Min Wu, Xiao-Fei Zhang, Dao-Qing Dai, Xiao-Li Li, Hong Yan

Abstract

Protein complexes carry out nearly all signaling and functional processes within cells. The study of protein complexes is an effective strategy to analyze cellular functions and biological processes. With the increasing availability of proteomics data, various computational methods have recently been developed to predict protein complexes. However, different computational methods are based on their own assumptions and designed to work on different data sources, and various biological screening methods have their unique experiment conditions, and are often different in scale and noise level. Therefore, a single computational method on a specific data source is generally not able to generate comprehensive and reliable prediction results. In this paper, we develop a novel Two-layer INtegrative Complex Detection (TINCD) model to detect protein complexes, leveraging the information from both clustering results and raw data sources. In particular, we first integrate various clustering results to construct consensus matrices for proteins to measure their overall co-complex propensity. Second, we combine these consensus matrices with the co-complex score matrix derived from Tandem Affinity Purification/Mass Spectrometry (TAP) data and obtain an integrated co-complex similarity network via an unsupervised metric fusion method. Finally, a novel graph regularized doubly stochastic matrix decomposition model is proposed to detect overlapping protein complexes from the integrated similarity network. Extensive experimental results demonstrate that TINCD performs much better than 21 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Student > Master 2 17%
Researcher 2 17%
Student > Bachelor 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 2 17%
Readers by discipline Count As %
Computer Science 3 25%
Biochemistry, Genetics and Molecular Biology 2 17%
Mathematics 1 8%
Agricultural and Biological Sciences 1 8%
Nursing and Health Professions 1 8%
Other 0 0%
Unknown 4 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2016.
All research outputs
#6,230,252
of 8,617,579 outputs
Outputs from BMC Bioinformatics
#2,930
of 3,718 outputs
Outputs of similar age
#188,528
of 290,638 outputs
Outputs of similar age from BMC Bioinformatics
#128
of 148 outputs
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