KEGG

KEGG: Kyoto Encyclopedia of Genes and Genomes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from genomic and molecular-level information. It is a computer representation of the biological system, consisting of molecular building blocks of genes and proteins (genomic information) and chemical substances (chemical information) that are integrated with the knowledge on molecular wiring diagrams of interaction, reaction and relation networks (systems information). It also contains disease and drug information (health information) as perturbations to the biological system.


References in zbMATH (referenced in 239 articles )

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  1. Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi: PolyLinkR: A linkage-sensitive gene set enrichment R package (2020) arXiv
  2. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  3. de Campos, Luis M.; Cano, Andrés; Castellano, Javier G.; Moral, Serafín: Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (2019)
  4. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  5. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  6. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  7. Nicholson, Daniel J.: Is the cell \textitreallya machine? (2019)
  8. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)
  9. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  10. Röhl, Annika; Bockmayr, Alexander: Finding MEMo: minimum sets of elementary flux modes (2019)
  11. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  12. Wang, Shulei; Yuan, Ming: Combined hypothesis testing on graphs with applications to gene set enrichment analysis (2019)
  13. Yu, Xinghao; Xiao, Lishun; Zeng, Ping; Huang, Shuiping: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information (2019)
  14. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  15. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  16. Latif, Majid jun.; May, Elebeoba E.: A multiscale agent-based model for the investigation of E. coli K12 metabolic response during biofilm formation (2018)
  17. Lee, JungJun; Kim, SungHwan; Jhong, Jae-Hwan; Koo, Ja-Yong: Variable selection and joint estimation of mean and covariance models with an application to eQTL data (2018)
  18. Li, Quefeng; Cheng, Guang; Fan, Jianqing; Wang, Yuyan: Embracing the blessing of dimensionality in factor models (2018)
  19. Luo, Xiangyu; Wei, Yingying: Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks (2018)
  20. Mukhopadhyay, Anirban: Incorporating gene ontology information in gene expression data clustering using multiobjective evolutionary optimization: application in yeast cell cycle data (2018)

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