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2017
Wang, Bo; Huang, Lin; Zhu, Yuke; Kundaje, Anshul; Batzoglou, Serafim; Goldenberg, Anna
Vicus: Exploiting local structures to improve network-based analysis of biological data Journal Article
In: PLOS Computational Biology, vol. 13, no. 10, pp. e1005621, 2017, ISSN: 1553-7358, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: Algebraic structures, Eigenvalues, Eigenvectors, Network analysis, Protein interaction networks, Protein structure comparison, Protein structure networks, Spectral clustering
@article{wang_vicus_2017,
title = {Vicus: Exploiting local structures to improve network-based analysis of biological data},
author = {Bo Wang and Lin Huang and Yuke Zhu and Anshul Kundaje and Serafim Batzoglou and Anna Goldenberg},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005621},
doi = {10.1371/journal.pcbi.1005621},
issn = {1553-7358},
year = {2017},
date = {2017-01-01},
urldate = {2021-05-19},
journal = {PLOS Computational Biology},
volume = {13},
number = {10},
pages = {e1005621},
abstract = {Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.},
note = {Publisher: Public Library of Science},
keywords = {Algebraic structures, Eigenvalues, Eigenvectors, Network analysis, Protein interaction networks, Protein structure comparison, Protein structure networks, Spectral clustering},
pubstate = {published},
tppubtype = {article}
}
Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.
Mezlini, Aziz M; Goldenberg, Anna
Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases Journal Article
In: PLOS Computational Biology, vol. 13, no. 10, pp. e1005580, 2017, ISSN: 1553-7358, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: Cancers and neoplasms, Gene prediction, Genetic networks, Mutation, Ovarian cancer, Protein interaction networks, Protein-protein interactions, Schizophrenia
@article{mezlini_incorporating_2017,
title = {Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases},
author = {Aziz M Mezlini and Anna Goldenberg},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005580},
doi = {10.1371/journal.pcbi.1005580},
issn = {1553-7358},
year = {2017},
date = {2017-01-01},
urldate = {2021-05-19},
journal = {PLOS Computational Biology},
volume = {13},
number = {10},
pages = {e1005580},
abstract = {Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.},
note = {Publisher: Public Library of Science},
keywords = {Cancers and neoplasms, Gene prediction, Genetic networks, Mutation, Ovarian cancer, Protein interaction networks, Protein-protein interactions, Schizophrenia},
pubstate = {published},
tppubtype = {article}
}
Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.