Computational Approaches for Reconstruction of Time-Varying Biological Networks from Omics Data

2013 ◽  
pp. 209-239
Author(s):  
Vinay Jethava ◽  
Chiranjib Bhattacharyya ◽  
Devdatt Dubhashi
Keyword(s):  
Biological Networks ◽  
Time Varying ◽  
Omics Data ◽  
Computational Approaches

scholarly journals A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Omics Data ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

scholarly journals NetGestalt: integrating multidimensional omics data over biological networks

2013 ◽  
Vol 10 (7) ◽  
pp. 597-598 ◽  
Author(s):  
Zhiao Shi ◽  
Jing Wang ◽  
Bing Zhang
Keyword(s):  
Biological Networks ◽  
Omics Data

scholarly journals MOBN: an interactive database of multi-omics biological networks

2019 ◽  
Author(s):  
Cheng Zhang ◽  
Muhammad Arif ◽  
Xiangyu Li ◽  
Sunjae Lee ◽  
Abdellah Tebani ◽  
...  
Keyword(s):  
Biological Networks ◽  
Gut Microbiome ◽  
Clinical Chemistry ◽  
Omics Data ◽  
Plasma Proteome ◽  
Specific Context ◽  
Link Type ◽  
Single Feature ◽  
Plasma Metabolome ◽  
Female Specific

AbstractSummaryThe associations among different omics are essential to understand human wellness and disease. However, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present an interactive database of multi-omics biological networks (MOBN) and describe associations between clinical chemistry, anthropometrics, plasma proteome, plasma metabolome and gut microbiome obtained from the same individuals. MOBN allows the user to interactively explore the association of a single feature with other omics data and customize its specific context (e.g. male/female specific). MOBN is designed for users who may not have a formal bioinformatics background to facilitate research in human wellness and diseases.AvailabilityThe database is accessible at http://multiomics.inetmodels.com without any limitation.

Computational Approaches Leveraging Integrated Connections of Multi-Omic Data toward Clinical Applications

2021 ◽  
Author(s):  
Cansu H Demirel ◽  
Kaan M Arici ◽  
Nurcan Tuncbag
Keyword(s):  
High Throughput ◽  
Biological Systems ◽  
Clinical Applications ◽  
Data Type ◽  
Omics Data ◽  
Computational Approaches ◽  
Omic Data

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing...

Computational Approaches for Analyzing Information Flow in Biological Networks

2012 ◽  
Vol 5 (220) ◽  
pp. re1-re1 ◽  
Author(s):  
B. Kholodenko ◽  
M. B. Yaffe ◽  
W. Kolch
Keyword(s):  
Information Flow ◽  
Biological Networks ◽  
Computational Approaches

scholarly journals CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks

2019 ◽  
Vol 20 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Ehsan Pournoor ◽  
Naser Elmi ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Network Data ◽  
Omics Data ◽  
Network Analyzer ◽  
Cellular Interactions ◽  
Data Generation ◽  
Network Comparison ◽  
Holistic Approaches ◽  
Topological Features ◽  
Generation Network

Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions. Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.

scholarly journals Biological Networks for Cancer Candidate Biomarkers Discovery

2016 ◽  
Vol 15s3 ◽  
pp. CIN.S39458 ◽  
Author(s):  
Wenying Yan ◽  
Wenjin Xue ◽  
Jiajia Chen ◽  
Guang Hu
Keyword(s):  
Cancer Diagnosis ◽  
Biological Networks ◽  
Molecular Mechanisms ◽  
Outcome Prediction ◽  
Biomarker Discovery ◽  
Cancer Biomarkers ◽  
System Level ◽  
Omics Data ◽  
Model Case ◽  
Level Data

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

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