scholarly journals Genome‐wide pathogenesis interpretation using a heat diffusion‐based systems genetics method and implications for gene function annotation

2020 ◽  
Vol 8 (10) ◽  
Author(s):  
Yuan Quan ◽  
Qing‐Ye Zhang ◽  
Bo‐Min Lv ◽  
Rui‐Feng Xu ◽  
Hong‐Yu Zhang
Keyword(s):  
Gene Function ◽  
Heat Diffusion ◽  
Systems Genetics ◽  
Function Annotation ◽  
Genome Wide ◽  
Gene Function Annotation

scholarly journals Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Renchu Guan ◽  
Xu Wang ◽  
Mary Qu Yang ◽  
Yu Zhang ◽  
Fengfeng Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gene Function ◽  
Function Annotation ◽  
Cancer Pathways ◽  
Gene Function Annotation

scholarly journals Functional Annotation of Caenorhabditis elegans Genes by Analysis of Gene Co-Expression Networks

Biomolecules ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 70 ◽  
Author(s):  
Wei Liu ◽  
Ling Li ◽  
Yiruo He ◽  
Sen Cai ◽  
Wenjie Zhao ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Gene Function ◽  
Large Scale ◽  
Gene List ◽  
Published Data ◽  
Experimental Conditions ◽  
Function Annotation ◽  
Development Stages ◽  
C Elegans ◽  
Gene Function Annotation

Caenorhabditis elegans (C. elegans) is a well-characterized metazoan, whose transcriptome has been profiled in different tissues, development stages, or other conditions. Large-scale transcriptomes can be reused for gene function annotation through systematic analysis of gene co-expression relationships. We collected 2101 microarray data from National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO), and identified 48 modules of co-expressed genes that correspond to tissues, development stages, and other experimental conditions. These modules provide an overview of the transcriptional organizations that may work under different conditions. By analyzing higher-order module networks, we found that nucleus and plasma membrane modules are more connected than other intracellular modules. Module-based gene function annotation may help to extend the candidate cuticle gene list. A comparison with other published data validates the credibility of our result. Our findings provide a new source for future gene discovery in C. elegans.

scholarly journals Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11427
Author(s):  
Conglin Ren ◽  
Mingshuang Li ◽  
Yang Zheng ◽  
Fengqing Wu ◽  
Weibin Du ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Western Blot ◽  
Experimental Verification ◽  
Gene Function ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Function Annotation ◽  
Gene Function Annotation

Background The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. Material and Methods We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot. Results A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant. Conclusion Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches.

A method of gene-function annotation based on variable precision rough sets

2007 ◽  
Vol 4 (3) ◽  
pp. 177-184 ◽  
Author(s):  
Zhi-li Pei ◽  
Xiao-hu Shi ◽  
Meng Niu ◽  
Xu-ning Tang ◽  
Li-sha Liu ◽  
...  
Keyword(s):  
Gene Function ◽  
Rough Sets ◽  
Function Annotation ◽  
Variable Precision ◽  
Gene Function Annotation ◽  
Variable Precision Rough Sets

Using Text Classification for Gene Function Annotation

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Maximum Entropy ◽  
Text Classification ◽  
Gene Function ◽  
Biological Function ◽  
Classification Algorithms ◽  
Function Annotation ◽  
Computational Approaches ◽  
Maximum Entropy Modeling ◽  
Biological Concepts ◽  
Gene Function Annotation

Recognizing specific biological concepts described in text is an important task that is receiving increasing attention in bioinformatics. To leverage the literature effectively, sophisticated data analysis algorithms must be able to identify key biological concepts and functions in text. However, biomedical text is complex and diverse in subject matter and lexicon. Very specialized vocabularies have been developed to describe biological complexity. In addition, using computational approaches to understand text in general has been a historically challenging subject (Rosenfeld 2000). In this chapter we will focus on the basics of understanding the content of biological text. We will describe common text classification algorithms. We demonstrate how these algorithms can be applied to the specific biological problem of gene annotation. But text classification is also potentially instrumental to many other areas of bioinformatics; we will see other applications in Chapter 10. There is great interest in assigning functional annotations to genes from the scientific literature. In one recent symposium 33 groups proposed and implemented classification algorithms to identify articles that were specifically relevant for gene function annotation (Hersh, Bhuporaju et al. 2004). In another recent symposium, seven groups competed to assign Gene Ontology function codes to genes from primary text (Valencia, Blaschke et al. 2004). In this chapter we assign biological function codes to genes automatically to investigate the extent to which computational approaches can be applied to identify relevant biological concepts in text about genes directly. Each code represents a specific biological function such as ‘‘signal transduction’’ or ‘‘cell cycle’’. The key concepts in this chapter are presented in the frame box. We introduce three text classification methods that can be used to associate functional codes to a set of literature abstracts. We describe and test maximum entropy modeling, naive Bayes classification, and nearest neighbor classification. Maximum entropy modeling outperforms the other methods, and assigns appropriate functions to articles with an accuracy of 72%. The maximum entropy method provides confidence measures that correlate well with performance.

scholarly journals Author Correction: Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Renchu Guan ◽  
Xu Wang ◽  
Mary Qu Yang ◽  
Yu Zhang ◽  
Fengfeng Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gene Function ◽  
Function Annotation ◽  
Cancer Pathways ◽  
Gene Function Annotation
Sign in / Sign up

Export Citation Format

Share Document