EgoNet: identification of human disease ego-network modules

Yang, Rendong; Bai, Yun; Qin, Zhaohui S.; Yu, Tianwei

doi:10.1186/1471-2164-15-314

Cited by 30 publications

(31 citation statements)

References 62 publications

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“…18 An ego-network consists of a centroid node, referred as ego-node , and its neighborhood defined as a set of nodes within certain distance to the ego-node over the network. We refer to this distance as the ego-radius .…”

Section: Methodsmentioning

confidence: 99%

Network Marker Selection for Untargeted LC–MS Metabolomics Data

Cai¹,

Alvarez²,

Kang

et al. 2017

J. Proteome Res.

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Untargeted metabolomics using high-resolution liquid chromatography–mass spectrometry (LC–MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC–MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

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Section: Methodsmentioning

confidence: 99%

Network Marker Selection for Untargeted LC–MS Metabolomics Data

Cai¹,

Alvarez²,

Kang

et al. 2017

J. Proteome Res.

Self Cite

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“…Auto-encoder (GAE), where the model is trained on G 2 : A combination of the previous two models, where a dense auto-encoder is used to learn a compressed representation which is passed to stacked LSTM units for temporal learning. It requires a large decoder, with both dense and LSTM layers, to predict the next graph.…”

Section: • Gae [13]: a Non-probabilistic Graph Convolutionalmentioning

confidence: 99%

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Bonner

Atapour-Abarghouei

Jackson

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently disregarded during the learning process, resulting in suboptimal performance on certain temporal infernce tasks. To combat this, we introduce Temporal Neighbourhood Aggregation (TNA), a novel vertex representation model architecture designed to capture both topological and temporal information to directly predict future graph states. Our model exploits hierarchical recurrence at different depths within the graph to enable exploration of changes in temporal neighbourhoods, whilst requiring no additional features or labels to be present. The final vertex representations are created using variational sampling and are optimised to directly predict the next graph in the sequence. Our claims are reinforced by extensive experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, out-performing them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters.

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“…In an attempt to determine the influence of sevoflurane on postoperative recovery in patients undergoing CABG, a novel method named as EgoNet [9] on the basis of egocentric networkanalysis technique was developed in this study. This method searches significant sub-networks which are functionally related with diseases.…”

Section: Dcn Constructionmentioning

confidence: 99%

Sevoflurane-medicated the pathway of chemokine receptors bind chemokines in patients undergoing CABG

Kong

et al. 2017

Open Life Sciences

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Abstract:Background: We aim to identify sevofluraneinduced modules and pathways in patients following coronary artery bypass graft (CABG) surgery, and to further elucidate the molecular mechanisms of the cardioprotective effects of sevoflurane. Methods: Differential co-expression network (DCN) was constructed. Candidate modules were identified via three steps: selection of seed genes, search of modules using snowball sampling, and refinement of modules. Afterwards, the significance of the candidate modules was assessed. Ultimately, pathway analyses for genes in differential modules were implemented to illuminate the biological processes. Results: Overall, 122 genes were identified to serve as seed genes. From every seed gene, we extracted 122 modules and the mean node size in a module was 3. By setting the classification accuracy cutoff at 0.9 and the number of nodes in a module at 5, 7 candidate modules were identified, including module 80, 82, 82, 84, 85, 86 and 89. Based on the random permutation test, we found that these 7 candidate modules were all differential ones. Moreover, pathway analysis showed that genes in the differential modules 80, 82, and 85 were all enriched in the pathway of chemokine receptors bind chemokines. Conclusion: Sevoflurane might exert cardioprotective

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EgoNet: identification of human disease ego-network modules

Cited by 30 publications

References 62 publications

Network Marker Selection for Untargeted LC–MS Metabolomics Data

Network Marker Selection for Untargeted LC–MS Metabolomics Data

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Sevoflurane-medicated the pathway of chemokine receptors bind chemokines in patients undergoing CABG

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