A primer to frequent itemset mining for bioinformatics
暂无分享,去创建一个
Bart Goethals | Wout Bittremieux | Kris Laukens | Pieter Meysman | Stefan Naulaerts | Wim Vanden Berghe | Trung-Nghia Vu | W. V. Berghe | Bart Goethals | W. Bittremieux | K. Laukens | P. Meysman | T. Vu | Stefan Naulaerts | Wout Bittremieux
[1] Gary Geunbae Lee,et al. Subcellular Localization Prediction through Boosting Association Rules , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[2] Ujjwal Maulik,et al. A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions , 2012, PloS one.
[3] Y. Benjamini,et al. A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence , 1999 .
[4] Bart Goethals,et al. Frequent Set Mining , 2010, Data Mining and Knowledge Discovery Handbook.
[5] Rakesh Agarwal,et al. Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.
[6] Jilles Vreeken,et al. Unraveling tobacco BY-2 protein complexes with BN PAGE/LC-MS/MS and clustering methods. , 2011, Journal of proteomics.
[7] José A. Reyes,et al. Prediction of protein-protein interaction types using association rule based classification , 2009, BMC Bioinformatics.
[8] Mikhail S. Gelfand,et al. Mining sequence annotation databanks for association patterns , 2005, Bioinform..
[9] Francisco-Javier Lopez,et al. Fuzzy association rules for biological data analysis: A case study on yeast , 2008, BMC Bioinformatics.
[10] Dr. Hui Xiong. Association Analysis: Basic Concepts and Algorithms , 2005 .
[11] Anthony K. H. Tung,et al. COBBLER: combining column and row enumeration for closed pattern discovery , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..
[12] J. van Leeuwen,et al. Intelligent Data Engineering and Automated Learning , 2003, Lecture Notes in Computer Science.
[13] Gediminas Adomavicius,et al. Handling very large numbers of association rules in the analysis of microarray data , 2002, KDD.
[14] Boris Cule,et al. Mining spatially cohesive itemsets in protein molecular structures , 2013, BioKDD '13.
[15] Ruichu Cai,et al. Two novel interestingness measures for gene association rule mining , 2012, Neural Computing and Applications.
[16] Martin Vingron,et al. DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach , 2011, Algorithms for Molecular Biology.
[17] Anthony K. H. Tung,et al. Mining top-K covering rule groups for gene expression data , 2005, SIGMOD '05.
[18] Mohammed J. Zaki,et al. Mining residue contacts in proteins using local structure predictions , 2000, Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering.
[19] Stefan Kramer,et al. Analyzing microarray data using quantitative association rules , 2005, ECCB/JBI.
[20] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[21] Anne M. Denton,et al. Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks , 2004, BIOKDD.
[22] Vincent S. Tseng,et al. Efficient mining of multilevel gene association rules from microarray and gene ontology , 2009, Inf. Syst. Frontiers.
[23] Geoffrey I. Webb. Discovering Significant Patterns , 2007, Machine Learning.
[24] Wojciech Szpankowski,et al. Detecting Conserved Interaction Patterns in Biological Networks , 2006, J. Comput. Biol..
[25] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[26] Ping Luo,et al. Incorporating occupancy into frequent pattern mining for high quality pattern recommendation , 2012, CIKM.
[27] Vipin Kumar,et al. Association analysis-based transformations for protein interaction networks: a function prediction case study , 2007, KDD '07.
[28] Jinyan Li,et al. Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.
[29] Patrik D'haeseleer,et al. Microbial genotype–phenotype mapping by class association rule mining , 2008, Bioinform..
[30] Jiawei Han,et al. CPAR: Classification based on Predictive Association Rules , 2003, SDM.
[31] Jiawei Han,et al. CloseGraph: mining closed frequent graph patterns , 2003, KDD '03.
[32] Hasan H. Otu,et al. Prediction of peptides binding to MHC class I and II alleles by temporal motif mining , 2013, BMC Bioinformatics.
[33] Lior Rokach,et al. Data Mining And Knowledge Discovery Handbook , 2005 .
[34] Siu-Ming Yiu,et al. A data-mining approach for multiple structural alignment of proteins , 2010, Bioinformation.
[35] Xiaoyun Chen,et al. Emerging Patterns and Classification Algorithms for DNA Sequence , 2011, J. Softw..
[36] Kenji Satou,et al. Extraction of knowledge on protein-protein interaction by association rule discovery , 2002, Bioinform..
[37] Heikki Mannila,et al. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining , 1997 .
[38] Maria-Luiza Antonie,et al. Classifying microarray data with association rules , 2011, SAC.
[39] C. Becquet,et al. Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data , 2002, Genome Biology.
[40] Dino Pedreschi,et al. Knowledge Discovery in Databases: PKDD 2004 , 2004, Lecture Notes in Computer Science.
[41] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[42] Ricardo Martínez,et al. Mining Association Rule Bases from Integrated Genomic Data and Annotations , 2008, CIBB.
[43] George Karypis,et al. Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[44] Finn Verner Jensen,et al. Bayesian networks , 1998, Data Mining and Knowledge Discovery Handbook.
[45] Susan M. Bridges,et al. Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology , 2012, PloS one.
[46] Ramakrishnan Srikant,et al. Fast algorithms for mining association rules , 1998, VLDB 1998.
[47] Srinivasan Parthasarathy,et al. New Algorithms for Fast Discovery of Association Rules , 1997, KDD.
[48] Damian Szklarczyk,et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..
[49] Hongyan Liu,et al. Top-Down Mining of Interesting Patterns from Very High Dimensional Data , 2006, 22nd International Conference on Data Engineering (ICDE'06).
[50] Kurt Hornik,et al. The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets , 2011, J. Mach. Learn. Res..
[51] Fabrice Guillet,et al. Quality Measures in Data Mining , 2009, Studies in Computational Intelligence.
[52] Jian Pei,et al. CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[53] HornikKurt,et al. The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets , 2011 .
[54] Kwong-Sak Leung,et al. Discovering protein–DNA binding sequence patterns using association rule mining , 2010, Nucleic acids research.
[55] Ingo Mierswa,et al. YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.
[56] Jilles Vreeken,et al. Krimp: mining itemsets that compress , 2011, Data Mining and Knowledge Discovery.
[57] Yanqing Zhang,et al. Granular support vector machines with association rules mining for protein homology prediction , 2005, Artif. Intell. Medicine.
[58] Jiawei Han,et al. gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[59] José María Carazo,et al. BMC Bioinformatics BioMed Central Methodology article Integrated analysis of gene expression by association rules discovery , 2022 .
[60] Johannes Gehrke,et al. MAFIA: a maximal frequent itemset algorithm for transactional databases , 2001, Proceedings 17th International Conference on Data Engineering.
[61] Dimitrios Gunopulos,et al. Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.
[62] Kian-Lee Tan,et al. Automatic protein structure classification through structural fingerprinting , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.
[63] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[64] James Bailey,et al. Fast Algorithms for Mining Emerging Patterns , 2002, PKDD.
[65] Wynne Hsu,et al. Integrating Classification and Association Rule Mining , 1998, KDD.
[66] Jesús S. Aguilar-Ruiz,et al. Gene association analysis: a survey of frequent pattern mining from gene expression data , 2010, Briefings Bioinform..
[67] Bart Goethals,et al. MIME: a framework for interactive visual pattern mining , 2011, KDD.
[68] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[69] Guimei Liu,et al. FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium , 2010, BMC Bioinformatics.
[70] Pan e Panov,et al. Inductive Databases and Constraint-Based Data Mining , 2010 .
[71] Hiroyuki Ogata,et al. KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..
[72] Ronen Feldman,et al. The Data Mining and Knowledge Discovery Handbook , 2005 .
[73] Christian Panse,et al. Identification of Combinatorial Patterns of Post-Translational Modifications on Individual Histones in the Mouse Brain , 2012, PloS one.
[74] Christian Borgelt,et al. Frequent item set mining , 2012, WIREs Data Mining Knowl. Discov..
[75] Anthony K. H. Tung,et al. FARMER: finding interesting rule groups in microarray datasets , 2004, SIGMOD '04.
[76] Carolina Ruiz,et al. Distance-enhanced association rules for gene expression , 2003, BIOKDD.
[77] Jihye Kim,et al. Finding association rules of cis-regulatory elements involved in alternative splicing , 2007, ACM-SE 45.
[78] Wen Wen,et al. Kernel based gene expression pattern discovery and its application on cancer classification , 2010, Neurocomputing.
[79] T. Mcintosh,et al. High Confidence Rule Mining for Microarray Analysis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[80] Ingrid Lohmann,et al. COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets , 2012, PloS one.
[81] Carson Kai-Sang Leung,et al. FpViz: a visualizer for frequent pattern mining , 2009, VAKD '09.
[82] Osmar R. Zaïane,et al. Mining Positive and Negative Association Rules: An Approach for Confined Rules , 2004, PKDD.
[83] M. Cevdet Ince,et al. An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..
[84] Thorsten Meinl,et al. KNIME - the Konstanz information miner: version 2.0 and beyond , 2009, SKDD.
[85] Nikolaj Tatti,et al. Using background knowledge to rank itemsets , 2010, Data Mining and Knowledge Discovery.
[86] Edward C. Uberbacher,et al. Analyzing large biological datasets with association networks , 2012, Nucleic acids research.
[87] Anthony K. H. Tung,et al. Carpenter: finding closed patterns in long biological datasets , 2003, KDD '03.
[88] Jean-François Boulicaut,et al. Generalizing Itemset Mining in a Constraint Programming Setting , 2010, Inductive Databases and Constraint-Based Data Mining.
[89] Mohammed J. Zaki,et al. GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets , 2005, Data Mining and Knowledge Discovery.
[90] Jian Pei,et al. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).
[91] Blaz Zupan,et al. Orange: From Experimental Machine Learning to Interactive Data Mining , 2004, PKDD.
[92] Li Ma,et al. An “almost exhaustive” search‐based sequential permutation method for detecting epistasis in disease association studies , 2010, Genetic epidemiology.
[93] Guimei Liu,et al. Controlling False Positives in Association Rule Mining , 2011, Proc. VLDB Endow..
[94] Dan A. Simovici,et al. Generating an informative cover for association rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[95] Jiawei Han,et al. Mining coherent dense subgraphs across massive biological networks for functional discovery , 2005, ISMB.
[96] Vincent S. Tseng,et al. Discovering relational-based association rules with multiple minimum supports on microarray datasets , 2011, Bioinform..
[97] Takashi Washio,et al. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.
[98] Roberto J. Bayardo,et al. Efficiently mining long patterns from databases , 1998, SIGMOD '98.
[99] Jaideep Srivastava,et al. Selecting the right interestingness measure for association patterns , 2002, KDD.
[100] Olivier Teytaud,et al. Association Rule Interestingness: Measure and Statistical Validation , 2007, Quality Measures in Data Mining.
[101] Siu Cheung Hui,et al. Exploring ant-based algorithms for gene expression data analysis , 2009, Artif. Intell. Medicine.
[102] Jiong Yang,et al. PathFinder: mining signal transduction pathway segments from protein-protein interaction networks , 2007, BMC Bioinformatics.
[103] Vipin Kumar,et al. Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.
[104] Yi Pan,et al. Rule Extraction from SVM for Protein Structure Prediction , 2008, Rule Extraction from Support Vector Machines.
[105] M. Steinbach,et al. High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions , 2012, PloS one.
[106] Pourang Irani,et al. WiFIsViz: Effective Visualization of Frequent Itemsets , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[107] Sai-Ping Li,et al. A guided Monte Carlo approach to optimization problems , 2003 .
[108] Chad Creighton,et al. Mining gene expression databases for association rules , 2003, Bioinform..
[109] Alfredo Ferro,et al. MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals , 2013, PloS one.
[110] Joost N. Kok,et al. The Gaston Tool for Frequent Subgraph Mining , 2005, GraBaTs.
[111] William C. Chu,et al. Proceedings of the 2011 ACM Symposium on Applied Computing (SAC), TaiChung, Taiwan, March 21 - 24, 2011 , 2011, SAC.
[112] Siegfried Nijssen,et al. What Is Frequent in a Single Graph? , 2007, PAKDD.
[113] Carolina Ruiz,et al. Association Rule Mining Algorithms for Set-Valued Data , 2003, IDEAL.
[114] Song Liu,et al. FUSIM: a software tool for simulating fusion transcripts , 2013, BMC Bioinformatics.
[115] Pu-Jen Cheng,et al. Visualizing timelines: evolutionary summarization via iterative reinforcement between text and image streams , 2012, CIKM.
[116] Mohammed J. Zaki,et al. Mining residue contacts in proteins using local structure predictions , 2003, IEEE Trans. Syst. Man Cybern. Part B.
[117] Kathleen Marchal,et al. The Condition‐Dependent Transcriptional Network in Escherichia coli , 2009, Annals of the New York Academy of Sciences.