Cargando…

Identifying disease genes by integrating multiple data sources

BACKGROUND: Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data...

Descripción completa

Detalles Bibliográficos
Autores principales:	Chen, Bolin, Wang, Jianxin, Li, Min, Wu, Fang-Xiang
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2014
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243092/ https://www.ncbi.nlm.nih.gov/pubmed/25350511 http://dx.doi.org/10.1186/1755-8794-7-S2-S2

Ejemplares similares

A fast and high performance multiple data integration algorithm for identifying human disease genes
por: Chen, Bolin, et al.
Publicado: (2015)

In Silico Gene Prioritization by Integrating Multiple Data Sources
por: Chen, Yixuan, et al.
Publicado: (2011)

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks
por: Li, Min, et al.
Publicado: (2014)

Identifying cooperative transcription factors in yeast using multiple data sources
por: Lai, Fu-Jou, et al.
Publicado: (2014)

Integration of multiple data sources to prioritize candidate genes using discounted rating system
por: Li, Yongjin, et al.
Publicado: (2010)

Expanding Alternative Splicing Identification by Integrating Multiple Sources of Transcription Data in Tomato
por: Clark, Sarah, et al.
Publicado: (2019)

D3GRN: a data driven dynamic network construction method to infer gene regulatory networks
por: Chen, Xiang, et al.
Publicado: (2019)

Identifying Multiple Propagation Sources
por: Jiang, Jiaojiao, et al.
Publicado: (2018)

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes
por: Zhou, Kaiyue, et al.
Publicado: (2022)

Prediction of disease genes using tissue-specified gene-gene network
por: Ganegoda, Gamage Upeksha, et al.
Publicado: (2014)

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets
por: Aouiche, Chaima, et al.
Publicado: (2019)

Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm
por: Wei, Liang, et al.
Publicado: (2018)

Integration of Multiple Data Sources to Simulate the Dynamics of Land Systems
por: Deng, Xiangzheng, et al.
Publicado: (2008)

An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
por: Sun, Jian, et al.
Publicado: (2014)

Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data
por: Li, Min, et al.
Publicado: (2012)

Ensemble disease gene prediction by clinical sample-based networks
por: Luo, Ping, et al.
Publicado: (2020)

A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity
por: Liu, Li, et al.
Publicado: (2018)

Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge
por: Wang, Shu-Qiang, et al.
Publicado: (2012)

Integrated Analysis of Mutation Data from Various Sources Identifies Key Genes and Signaling Pathways in Hepatocellular Carcinoma
por: Zhang, Yuannv, et al.
Publicado: (2014)

Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation
por: Li, Min, et al.
Publicado: (2014)

Efficient algorithms for fast integration on large data sets from multiple sources
por: Mi, Tian, et al.
Publicado: (2012)

Integrating multiple sources of ecological data to unveil macroscale species abundance
por: Fukaya, Keiichi, et al.
Publicado: (2020)

Identifying Disease Related Genes by Network Representation and Convolutional Neural Network
por: Chen, Bolin, et al.
Publicado: (2021)

Identifying essential proteins from active PPI networks constructed with dynamic gene expression
por: Xiao, Qianghua, et al.
Publicado: (2015)

Integration of breast cancer gene signatures based on graph centrality
por: Wang, Jianxin, et al.
Publicado: (2011)

One potential biomarker for teratozoospermia identified by in-depth integrative analysis of multiple microarray data
por: Han, Baoquan, et al.
Publicado: (2021)

Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data
por: Jiang, Bing, et al.
Publicado: (2017)

Identification of Genes for Complex Diseases Using Integrated Analysis of Multiple Types of Genomic Data
por: Cao, Hongbao, et al.
Publicado: (2012)

Protein function prediction by massive integration of evolutionary analyses and multiple data sources
por: Cozzetto, Domenico, et al.
Publicado: (2013)

MS-kNN: protein function prediction by integrating multiple data sources
por: Lan, Liang, et al.
Publicado: (2013)

A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship
por: Chen, Bolin, et al.
Publicado: (2021)

A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data
por: Zhu, Xiaoshu, et al.
Publicado: (2019)

Integration of Multiple Genomic Data Sources in a Bayesian Cox Model for Variable Selection and Prediction
por: Treppmann, Tabea, et al.
Publicado: (2017)

Joint conditional Gaussian graphical models with multiple sources of genomic data
por: Chun, Hyonho, et al.
Publicado: (2013)

Identifying Thyroid Carcinoma-Related Genes by Integrating GWAS and eQTL Data
por: Shen, Fei, et al.
Publicado: (2021)

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data
por: Wu, Tao, et al.
Publicado: (2020)

Integrated Analysis of DNA Methylation and mRNA Expression Profiles Data to Identify Key Genes in Lung Adenocarcinoma
por: Jin, Xiang, et al.
Publicado: (2016)

Identifying potential cancer driver genes by genomic data integration
por: Chen, Yong, et al.
Publicado: (2013)

Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes
por: Kim, SungHwan, et al.
Publicado: (2015)

Invasive Pneumococcal Disease in Tuscany Region, Italy, 2016–2017: Integrating Multiple Data Sources to Investigate Underreporting
por: Quattrone, Filippo, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_skk5ctnuaf6018pilgj50sb79d