The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. The DAVID Gene Functional Classification Tool http://david.abcc.ncifcrf.gov uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules. This organization is accomplished by mining the complex biological co-occurrences found in multiple sources of functional annotation. It is a powerful method to group functionally related genes and terms into a manageable number of biological modules for efficient interpretation of gene lists in a network context.

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  1. Chen, Guodong; Cao, Man; Yu, Jialin; Guo, Xinyun; Shi, Shaoping: Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC (2019)
  2. Lue, Heng-Hui: Pairwise directions estimation for multivariate response regression data (2019)
  3. Wang, Dingjie; Zou, Xiufen: A new centrality measure of nodes in multilayer networks under the framework of tensor computation (2018)
  4. Wu, Mengyun; Zhu, Liping; Feng, Xingdong: Network-based feature screening with applications to genome data (2018)
  5. Felici, Giovanni; Tripathi, Kumar Parijat; Evangelista, Daniela; Guarracino, Mario Rosario: A mixed integer programming-based global optimization framework for analyzing gene expression data (2017)
  6. Wang, Beilun; Singh, Ritambhara; Qi, Yanjun: A constrained (\ell1) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (2017)
  7. Wang, Gang; Li, Yuanyuan; Zou, Xiufen: Several indicators of critical transitions for complex diseases based on stochastic analysis (2017)
  8. Cassese, Alberto; Guindani, Michele; Vannucci, Marina: iBATCGH: integrative Bayesian analysis of transcriptomic and CGH data (2016)
  9. Chen, Ting-Huei; Sun, Wei; Fine, Jason P.: Designing penalty functions in high dimensional problems: the role of tuning parameters (2016)
  10. Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (2016)
  11. Li, Ying; He, Ye; Zhang, Yu: Analyzing gene expression time-courses based on multi-resolution shape mixture model (2016)
  12. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  13. Paul, Sushmita; Maji, Pradipta: Gene expression and protein-protein interaction data for identification of colon cancer related genes using (f)-information measures (2016)
  14. Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Lv, Yingli: Characterization of BioPlex network by topological properties (2016)
  15. Cui, Shiqi; Guha, Subharup; Ferreira, Marco A. R.; Tegge, Allison N.: HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data (2015)
  16. Hinow, Peter; Rietman, Edward A.; Omar, Sara Ibrahim; Tuszyński, Jack A.: Algebraic and topological indices of molecular pathway networks in human cancers (2015)
  17. Lin, Zhixiang; Sanders, Stephan J.; Li, Mingfeng; Sestan, Nenad; State, Matthew W.; Zhao, Hongyu: A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data (2015)
  18. Tenenhaus, Arthur; Philippe, Cathy; Frouin, Vincent: Kernel generalized canonical correlation analysis (2015)
  19. Xu, Yan; Ding, Ya-Xin; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang: Phogly-PseAAC: prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity (2015)
  20. Zhang, Yulin; Lv, Kebo; Wang, Shudong; Su, Jionglong; Meng, Dazhi: Modeling gene networks in \textitSaccharomycescerevisiae based on gene expression profiles (2015)

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