Exploiting ontology graph for predicting sparsely annotated gene function

Wang, Sheng; Cho, Hyunghoon; Zhai, Cheng Xiang; Berger, Bonnie; Peng, Jian

doi:10.1093/bioinformatics/btv260

Cited by 100 publications

(116 citation statements)

References 34 publications

(45 reference statements)

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“…For instance, we recently developed an improved protein function prediction algorithm based on Mashup that exploits the semantic similarity between different functional categories from the ontology hierarchy, which led to significantly better predictions in sparsely annotated GO categories (Wang et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Cho¹,

2016

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Summary The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating, diverse biological network data and can be broadly applied to other network science domains.

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Section: Discussionmentioning

confidence: 99%

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Cho¹,

2016

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“…A key computational contribution is that ProSNet obtains low-dimensional vectors through a fast online learning algorithm instead of the batch learning algorithm used by previous work. 23,32 In each iteration, ProSNet samples a path from the heterogeneous network and optimizes low-dimensional vectors based on this path instead of all pairs of nodes. Therefore, it can easily scale to large networks containing hundreds of thousands or even millions of edges and nodes.…”

Section: Methodsmentioning

confidence: 99%

“…22 Recently, this “guilt-by-association” principle has become the foundation of many network-based function prediction algorithms. 23–30 Among them, Gen-eMANIA 31 and clusDCA 32 are state-of-the-art network-based function prediction approaches. In addition to incorporating network topology, clusDCA also leverages the similarity between GO labels and obtains substantial improvement on sparsely annotated functions.…”

Section: Introductionmentioning

confidence: 99%

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Prosnet: Integrating Homology With Molecular Networks for Protein Function Prediction

Wang

Peng

2016

Biocomputing 2017

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Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches.

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“…Mashup has been demonstrated to achieve significantly improved prediction for protein function prediction, gene ontology reconstruction, genetic interaction prediction, and drug-target interaction prediction. [20][21][22][23] It takes one or more networks as input, performs random walk with restart (RWR) 24 and extracts topological information from the diffusion distributions using informative but low-dimensional vector representations of drugs.…”

Section: Integration Of Multi-omics Datamentioning

confidence: 99%

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

et al. 2017

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A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.

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Exploiting ontology graph for predicting sparsely annotated gene function

Cited by 100 publications

References 34 publications

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

Prosnet: Integrating Homology With Molecular Networks for Protein Function Prediction

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

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