Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability

Zhang, Jie; Lu, Kai; Xiang, Yang; Islam, Muhtadi M.; Kotian, Shweta; Kais, Zeina; Lee, Cindy; Arora, Mansi; Liu, Hui Wen; Parvin, Jeffrey D.; Huang, Kun

doi:10.1371/journal.pcbi.1002656

Cited by 81 publications

(100 citation statements)

References 75 publications

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“…Different results may indeed support the identification of key nodes, or conserved patterns, which may be cross confirmed in their co-expression by different approaches, supporting a result-based merging by suitable mining [63,64] or visualization tools [65,66].…”

Section: Comparative Meta-analysis Of Results From Different Platformsmentioning

confidence: 72%

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

et al. 2016

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Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

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Section: Comparative Meta-analysis Of Results From Different Platformsmentioning

confidence: 72%

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

et al. 2016

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“…There is no information sharing between these ontologies. A significant amount of work in biomedical research is to identify the associations between different entities, such as identifying disease genes [22], prioritizing disease genes [23], and mapping phenotypes to genotypes [24]. These works will not benefit from a single ontology.…”

Section: Systematic Methods For Heterogeneous Ontology Integration Andmentioning

confidence: 99%

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Hashemikhabir

Xia

Xiang

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) and Gene Ontologies (GO). This approach is one of the early attempts to quantify the associations among clinical terms (e.g. ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations' relevance by comparing them with two well-known disease-gene datasets (i.e. MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature.

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“…Gene module 9 was highly enriched with cell cycle and mitosis genes. In fact, genes in this module are frequently observed to coexpress in multiple types of cancers (37). High expression of this eigengene indicates that the tumor is more aggressive, and it was negatively related to patient prognosis (log-rank test P ¼ 1.19eÀ4).…”

Section: Survival-associated Image Features Correlate With Eigengenesmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

et al. 2017

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In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n ¼ 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. Ó2017 AACR.

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Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability

Cited by 81 publications

References 75 publications

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

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