Rule based functional description of genes – Estimation of the multicriteria rule interestingness measure by the UTA method
Tài liệu tham khảo
Baldi, 2002
Wang, 2009, RNA-Seq: a revolutionary tool for transcriptomics, Nat Rev Genet, 10, 57, 10.1038/nrg2484
Ashburner, 2000, Gene Ontology: tool for the unification of biology, Nat Genet, 25, 25, 10.1038/75556
Maere, 2005, BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks, Bioinformatics, 21, 3448, 10.1093/bioinformatics/bti551
Al-Shahrour, 2005, BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments, Nucleic Acids Res, 33, W460, 10.1093/nar/gki456
Khatri, 2005, Ontological analysis of gene expression data: current tools, limitations, and open problems, Bioinformatics, 21, 3587, 10.1093/bioinformatics/bti565
Hvidsten, 2003, Learning rule-based models of biological process from gene expression time profiles using gene ontology, Bioinformatics, 19, 1116, 10.1093/bioinformatics/btg047
Midelfart, 2005, Supervised learning in the gene ontology. Part I. A rough set framework, Trans Rough Sets IV, 3700, 98, 10.1007/11574798_6
Midelfart, 2005, Supervised learning in the gene ontology. Part II. A bottom-up algorithm, Trans Rough Sets IV, 3700, 69, 10.1007/11574798_5
Carmona-Saez, 2006, Integrated analysis of gene expression by association rules discovery, BMC Bioinformatics, 7, 54, 10.1186/1471-2105-7-54
Nogales-Cadenas, 2009, GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information, Nucleic Acids Res, 37, W317, 10.1093/nar/gkp416
Agrawal, 1994, 487
Hackenberg, 2008, Annotation-modules: a tool for finding significant combinations of multisource annotations for gene lists, Bioinformatics, 24, 1386, 10.1093/bioinformatics/btn178
Sikora, 2010, Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules, Appl Math Comput Sci, 20, 555
Sikora, 2011, Induction and selection of the most interesting gene ontology based multiattribute rules for descriptions of gene groups, Pattern Recogn Lett, 32, 258, 10.1016/j.patrec.2010.08.011
Geng, 2006, Interestingness measures for data mining: a survey, ACM Comput Surv, 38, 9, 10.1145/1132960.1132963
MacGarry, 2005, A survey of interestingness measures for knowledge discovery, Knowl Eng Rev, 20, 39, 10.1017/S0269888905000408
Gruca, 2011, RuleGO: a logical rules-based tool for description of gene groups by means of gene ontology, Nucleic Acids Res, 39,, W293, 10.1093/nar/gkr507
Jacquet-Lagrae, 1982, Assessing a set of additive utility functions for multicriteria decision making: the UTA method, Eur J Oper Res, 10, 151, 10.1016/0377-2217(82)90155-2
Siskos, 2005, UTA methods, vol. 78, 297
Andersen, 1995, NP-completeness of minimum rule sets, 411
Agotnes, 1999, Taming large rule models in rough set approaches, vol. 1704, 193
Fürnkranz, 1997, Pruning algorithms for rule learning, Mach Learn, 27, 139, 10.1023/A:1007329424533
Stańczyk, 2013, Decision rule length as a basic for evaluation of attribute relevance, J Intell Fuzzy Syst, 24, 429, 10.3233/IFS-2012-0564
Ishibuchi, 2003, Effect of three-objective genetic rule selection on the generalization ability of fuzzy rule-based systems, Lect Notes Comput Sci, 2632, 608, 10.1007/3-540-36970-8_43
Sikora, 2010, Decision rule-based data models using TRS and NetTRS—methods and algorithms, Trans Rough Sets, 11, 130
Sikora, 2011, Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm, vol. 6743, 278
Gamberger, 2000, Confirmation rule sets, 34
Gupta, 1999, Distance based clustering of association rules, 759
Tsumoto, 2003, Visualization of Rule's similarity using multidimensional scaling, 339
Bayardo, 1999, Mining the most interesting rules, 145
Brzezińska, 2007, Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support, Eng Appl Artif Intell, 20, 587, 10.1016/j.engappai.2006.11.015
Abe, 2007, Evaluation learning algorithms to construct rule evaluation models based on objective rule evaluation indices, IEEE Comput Soc, 212
Abe, 2008, Comparing accuracies of rule evaluation models to determine human criteria on evaluated rule sets, IEEE Comput Soc, 1
Lenca, 2004
Brans, 2005, Promethee methods, vol. 78, 163
Stefanowski, 2001, Induction of decision rules in classification and discovery-oriented perspectives, Int J Intell Syst, 16, 13, 10.1002/1098-111X(200101)16:1<13::AID-INT3>3.0.CO;2-M
An, 2001, Rule quality measures for rule induction systems: description and evaluation, Comput Intell, 17, 409, 10.1111/0824-7935.00154
Sikora, 2006, Rule quality measures in creation and reduction of data role models, Lect Notes Artif Intell, 4259, 716
Fürnkranz, 2005, ROC ‘n’ rule learning—towards a better understanding of covering algorithms, Mach Learn, 58, 39, 10.1007/s10994-005-5011-x
Guillet, 2007
Kavsek, 2006, APRIORI-SD: adapting association rule learning to subgroup discovery, Appl Artif Intell, 20, 543, 10.1080/08839510600779688
Gruca, 2009
Blanchard, 2009, Semantic-based classification of rule interestingness measures, 56
Eisen, 1998, Cluster analysis and display of genome-wide expression patterns, Proc Natl Acad Sci USA, 95, 14863, 10.1073/pnas.95.25.14863
Iyer, 1999, The transcriptional program in the response of human fibroblasts to serum, Science, 283, 83, 10.1126/science.283.5398.83
Cho, 2001, Transcriptional regulation and function during the human cell cycle, Nat Genet, 27, 48, 10.1038/83751