Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets

AbstractThe topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

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Identifying functional modules using energy minimization with graph cuts

Current Bioinformatics ◽

10.2174/2215083807666210702151919 ◽

2021 ◽

Vol 16 ◽

Author(s):

Yuanyuan Chen ◽

Xiaodan Fan ◽

Cong Pian

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Energy Minimization ◽

Expression Profiles ◽

Gene Interaction ◽

Graph Cuts ◽

Interaction Networks ◽

Functional Modules ◽

Expression Data ◽

Gene Interaction Networks

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Method: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.

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Correlated Discretized Expression Score: A Method for Identifying Gene Interaction Networks from Time Course Microarray Expression Data

2006 International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2006.4398786 ◽

2006 ◽

Author(s):

Peter Larsen ◽

Eyad Almasri ◽

Guanrao Chen ◽

Yang Dai

Keyword(s):

Time Course ◽

Gene Interaction ◽

Interaction Networks ◽

Expression Data ◽

Microarray Expression Data ◽

Gene Interaction Networks ◽

Microarray Expression ◽

Expression Score

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DISCOVERY OF HIGHLY DIFFERENTIATIVE GENE GROUPS FROM MICROARRAY GENE EXPRESSION DATA USING THE GENE CLUB APPROACH

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001545 ◽

2005 ◽

Vol 03 (06) ◽

pp. 1263-1280 ◽

Cited By ~ 15

Author(s):

SHIHONG MAO ◽

GUOZHU DONG

Keyword(s):

Gene Expression ◽

Gene Interaction ◽

Microarray Gene Expression Data ◽

Interaction Networks ◽

Microarray Gene Expression ◽

Normal Tissues ◽

Microarray Gene Expression Profile ◽

Gene Interaction Networks ◽

Microarray Gene ◽

Novel Concept

Motivation: It is commonly believed that suitable analysis of microarray gene expression profile data can lead to better understanding of diseases, and better ways to diagnose and treat diseases. To achieve those goals, it is of interest to discover the gene interaction networks, and perhaps even pathways, underlying given diseases from such data. In this paper, we consider methods for efficiently discovering highly differentiative gene groups (HDGG), which may provide insights on gene interaction networks. HDGGs are groups of genes which completely or nearly completely characterize the diseased or normal tissues. Discovering HDGGs is challenging, due to the high dimensionality of the data. Results: Our methods are based on the novel concept of gene clubs. A gene club consists of a set of genes having high potential to be interactive with each other. The methods can (i) efficiently discover signature HDGGs which completely characterize the diseased and the normal tissues respectively, (ii) find strongest or near strongest HDGGs containing any given gene, and (iii) find much stronger HDGGs than previous methods. As part of the experimental evaluation, the methods are applied to colon, prostate, ovarian, and breast cancer, and leukemia and so on. Some of the genes in the extracted signature HDGGs have known biological functions, and some have attracted little attention in biology and medicine. We hope that appropriate study on them can lead to medical breakthroughs. Some HDGGs for colon and prostate cancers are listed here. The website listed below contains HDGGs for the other cancers. Availability: HDGG is implemented in C++ and runs on Unix or Windows platform. The code is available at: .

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