RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors
暂无分享,去创建一个
[1] Taeho Jo,et al. Improving protein fold recognition by random forest , 2014, BMC Bioinformatics.
[2] Vincent Laudet,et al. Principles for modulation of the nuclear receptor superfamily , 2004, Nature Reviews Drug Discovery.
[3] Kuo-Chen Chou,et al. NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features , 2011, PloS one.
[4] Manish Kumar,et al. NRfamPred: A proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families , 2014, Scientific Reports.
[5] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[6] Eleazar Eskin,et al. The Spectrum Kernel: A String Kernel for SVM Protein Classification , 2001, Pacific Symposium on Biocomputing.
[7] K. Umesono,et al. A Unified Nomenclature System for the Nuclear Receptor Superfamily , 1999, Cell.
[8] Dong-Sheng Cao,et al. propy: a tool to generate various modes of Chou's PseAAC , 2013, Bioinform..
[9] Xuan Xiao,et al. NRPred-FS: A Feature Selection based Two-level Predictor for NuclearReceptors , 2014 .
[10] Ying Gao,et al. Bioinformatics Applications Note Sequence Analysis Cd-hit Suite: a Web Server for Clustering and Comparing Biological Sequences , 2022 .
[11] David L. Wheeler,et al. GenBank , 2015, Nucleic Acids Res..
[12] V. Laudet,et al. Evolution of the nuclear receptor superfamily: early diversification from an ancestral orphan receptor. , 1997, Journal of molecular endocrinology.
[13] K. Chou. Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology , 2009 .
[14] Kuo-Chen Chou,et al. iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix , 2012, PloS one.
[15] Cheng Wu,et al. Prediction of nuclear receptors with optimal pseudo amino acid composition. , 2009, Analytical biochemistry.
[16] A. Hopkins,et al. The druggable genome , 2002, Nature Reviews Drug Discovery.
[17] Bas Vroling,et al. NucleaRDB: information system for nuclear receptors , 2011, Nucleic Acids Res..
[18] K. Nishikawa,et al. Classification of proteins into groups based on amino acid composition and other characters. II. Grouping into four types. , 1983, Journal of biochemistry.