Hierarchical Ensemble Methods for Protein Function Prediction
暂无分享,去创建一个
[1] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[2] Giorgio Valentini,et al. Scalable Network-based Learning Methods for Automated Function Prediction based on the Neo 4 j Graph-database , 2013 .
[3] S. Kasif,et al. Whole-genome annotation by using evidence integration in functional-linkage networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[4] Alexander J. Smola,et al. Kernels and Regularization on Graphs , 2003, COLT.
[5] Giorgio Valentini,et al. Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction , 2010, MLSB.
[6] A. Sokolov. A Structured-Outputs Method for Prediction of Protein Function , 2008 .
[7] Peter Bühlmann,et al. Boosting for Tumor Classification with Gene Expression Data , 2003, Bioinform..
[8] Yong Zhang,et al. A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets , 2013 .
[9] E. Marcotte,et al. Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana , 2010, Nature Biotechnology.
[10] David Manset,et al. XML-based approaches for the integration of heterogeneous bio-molecular data , 2009, BMC Bioinformatics.
[11] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[12] Karin M. Verspoor,et al. A categorization approach to automated ontological function annotation , 2006, Protein science : a publication of the Protein Society.
[13] Nello Cristianini,et al. A statistical framework for genomic data fusion , 2004, Bioinform..
[14] Giorgio Valentini,et al. A Novel Ensemble Technique for Protein Subcellular Location Prediction , 2011, Ensembles in Machine Learning Applications.
[15] Alex Alves Freitas,et al. A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms , 2009, EvoBIO.
[16] Giorgio Valentini,et al. Ensembles of Learning Machines , 2002, WIRN.
[17] Claudio Gentile,et al. Incremental Algorithms for Hierarchical Classification , 2004, J. Mach. Learn. Res..
[18] Kara Dolinski,et al. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms , 2004, Nucleic Acids Res..
[19] Juho Rousu,et al. Towards structured output prediction of enzyme function , 2008, BMC proceedings.
[20] Juho Rousu,et al. Kernel-Based Learning of Hierarchical Multilabel Classification Models , 2006, J. Mach. Learn. Res..
[21] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[22] Giorgio Valentini,et al. Cancer module genes ranking using kernelized score functions , 2012, BMC Bioinformatics.
[23] Yousef Saad,et al. Iterative methods for sparse linear systems , 2003 .
[24] Alessandro Vespignani,et al. Global protein function prediction from protein-protein interaction networks , 2003, Nature Biotechnology.
[25] Claudio Gentile,et al. Hierarchical classification: combining Bayes with SVM , 2006, ICML.
[26] André Carlos Ponce de Leon Ferreira de Carvalho,et al. New top-down methods using SVMs for Hierarchical Multilabel Classification problems , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[27] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Dmitrij Frishman,et al. MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..
[29] G. Valentini,et al. Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction , 2009 .
[30] Christopher DeCoro,et al. Hierarchical Shape Classification Using Bayesian Aggregation , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).
[31] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[32] Teuvo Kohonen,et al. The self-organizing map , 1990, Neurocomputing.
[33] Yiannis Kourmpetis,et al. Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data , 2010, PloS one.
[34] D. Eisenberg,et al. A combined algorithm for genome-wide prediction of protein function , 1999, Nature.
[35] Zheng Guo,et al. Broadly predicting specific gene functions with expression similarity and taxonomy similarity. , 2005, Gene.
[36] Anil K. Jain,et al. Clustering ensembles: models of consensus and weak partitions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Ram Samudrala,et al. Functional annotation from predicted protein interaction networks , 2005, Bioinform..
[38] Dao-Qing Dai,et al. A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[39] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Hierarchical Multilabel Protein Function Prediction Using Local Neural Networks , 2011, BSB.
[40] Joseph Gonzalez,et al. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs , 2012, OSDI.
[41] Zhiwen Yu,et al. Protein Function Prediction with Incomplete Annotations , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[42] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[43] Nicolò Cesa-Bianchi,et al. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference , 2012, Machine Learning.
[44] Giorgio Valentini,et al. A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[45] Giorgio Valentini,et al. Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories , 2012, ISBRA.
[46] Olga G. Troyanskaya,et al. BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm332 Data and text mining , 2022 .
[47] Yoram Singer,et al. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..
[48] Benhui Chen,et al. Hierarchical multi‐label classification based on over‐sampling and hierarchy constraint for gene function prediction , 2012 .
[49] Herna L. Viktor,et al. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.
[50] Giorgio Valentini,et al. COSNet: A Cost Sensitive Neural Network for Semi-supervised Learning in Graphs , 2011, ECML/PKDD.
[51] Jon Atli Benediktsson,et al. Multiple Classifier Systems , 2015, Lecture Notes in Computer Science.
[52] Weidong Tian,et al. Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function , 2008, Genome Biology.
[53] Giorgio Valentini,et al. A neural network algorithm for semi-supervised node label learning from unbalanced data , 2013, Neural Networks.
[54] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[55] Asa Ben-Hur,et al. Hierarchical Classification of Gene Ontology Terms Using the Gostruct Method , 2010, J. Bioinform. Comput. Biol..
[56] E. Myers,et al. Basic local alignment search tool. , 1990, Journal of molecular biology.
[57] Alex Alves Freitas,et al. A grammatical evolution algorithm for generation of Hierarchical Multi-Label Classification rules , 2013, 2013 IEEE Congress on Evolutionary Computation.
[58] María S. Pérez-Hernández,et al. Bayesian network multi-classifiers for protein secondary structure prediction , 2004, Artif. Intell. Medicine.
[59] Alex Alves Freitas,et al. On the Importance of Comprehensible Classification Models for Protein Function Prediction , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[60] Michael I. Jordan,et al. Consistent probabilistic outputs for protein function prediction , 2008, Genome Biology.
[61] Christian Schaefer,et al. Homology-based inference sets the bar high for protein function prediction , 2013, BMC Bioinformatics.
[62] Giorgio Valentini. Mosclust: a software library for discovering significant structures in bio-molecular data , 2007, Bioinform..
[63] Saso Dzeroski,et al. Tree ensembles for predicting structured outputs , 2013, Pattern Recognit..
[64] Giorgio Valentini,et al. Network-Based Drug Ranking and Repositioning with Respect to DrugBank Therapeutic Categories , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[65] A. Valencia. Automatic annotation of protein function. , 2005, Current opinion in structural biology.
[66] Jason Weston,et al. Mismatch String Kernels for SVM Protein Classification , 2002, NIPS.
[67] Daniel W. A. Buchan,et al. A large-scale evaluation of computational protein function prediction , 2013, Nature Methods.
[68] Ni Li,et al. Gene Ontology Annotations and Resources , 2012, Nucleic Acids Res..
[69] Grigorios Tsoumakas,et al. Multi-Label Classification of Music into Emotions , 2008, ISMIR.
[70] William Stafford Noble,et al. Choosing negative examples for the prediction of protein-protein interactions , 2006, BMC Bioinformatics.
[71] K. Bretonnel Cohen,et al. Ontology quality assurance through analysis of term transformations , 2009, Bioinform..
[72] D. Botstein,et al. Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.
[73] Farshad Fotouhi,et al. Exploiting Label Dependency for Hierarchical Multi-label Classification , 2012, PAKDD.
[74] Giorgio Valentini,et al. Ensemble methods : a review , 2012 .
[75] S. Y. Sohn,et al. Experimental study for the comparison of classifier combination methods , 2007, Pattern Recognit..
[76] Bernhard Schölkopf,et al. Fast protein classification with multiple networks , 2005, ECCB/JBI.
[77] B. Snel,et al. Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.
[78] Vipin Kumar,et al. Incorporating functional inter-relationships into protein function prediction algorithms , 2009, BMC Bioinformatics.
[79] Prabhakar Raghavan,et al. Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies , 1998, The VLDB Journal.
[80] Michelangelo Ceci,et al. Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction , 2013, BMC Bioinformatics.
[81] Zili Zhang,et al. Protein Function Prediction by Integrating Multiple Kernels , 2013, IJCAI.
[82] Júlio C. Nievola,et al. Hierarchical classification using a Competitive Neural Network , 2012, 2012 8th International Conference on Natural Computation.
[83] K. Dembczynski,et al. On Label Dependence in Multi-Label Classification , 2010 .
[84] Karin M. Verspoor,et al. Combining heterogeneous data sources for accurate functional annotation of proteins , 2013, BMC Bioinformatics.
[85] ZhouZhi-Hua,et al. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006 .
[86] Paolo Fontana,et al. Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms , 2012, BMC Bioinformatics.
[87] Giorgio Valentini,et al. Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms , 2005, WIRN/NAIS.
[88] Giorgio Valentini,et al. Ensemble Based Data Fusion for Gene Function Prediction , 2009, MCS.
[89] H. Mewes,et al. Overview of the yeast genome. , 1997, Nature.
[90] Gunnar Rätsch,et al. Multitask Learning in Computational Biology , 2012, ICML Unsupervised and Transfer Learning.
[91] Limsoon Wong,et al. An efficient strategy for extensive integration of diverse biological data for protein function prediction , 2007, Bioinform..
[92] Robert E. Schapire,et al. Hierarchical multi-label prediction of gene function , 2006, Bioinform..
[93] Luis Enrique Sucar,et al. A Hybrid Global-Local Approach for Hierarchical Classification , 2013, FLAIRS.
[94] Zhi-Hua Zhou,et al. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.
[95] J. Friedman,et al. On bagging and nonlinear estimation , 2007 .
[96] Alexander Lerch,et al. A HIERARCHICAL APPROACH TO AUTOMATIC MUSICAL GENRE CLASSIFICATION , 2003 .
[97] Gökhan BakIr,et al. Predicting Structured Data , 2008 .
[98] Olga G. Troyanskaya,et al. The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction , 2009, Bioinform..
[99] William Stafford Noble,et al. Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure , 2006, Bioinform..
[100] Dennis Shasha,et al. Parametric Bayesian priors and better choice of negative examples improve protein function prediction , 2013, Bioinform..
[101] Giorgio Valentini,et al. Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.
[102] H. D. Brunk,et al. The Isotonic Regression Problem and its Dual , 1972 .
[103] Giorgio Valentini,et al. Ensembles in Machine Learning Applications , 2011, Studies in Computational Intelligence.
[104] Purvesh Khatri,et al. Predicting Novel Human Gene Ontology Annotations Using Semantic Analysis , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[105] Giorgio Valentini,et al. True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[106] Yoram Singer,et al. Large margin hierarchical classification , 2004, ICML.
[107] Noel E. Sharkey,et al. The "Test and Select" Approach to Ensemble Combination , 2000, Multiple Classifier Systems.
[108] Jinglu Hu,et al. Composite kernel based SVM for hierarchical multi-label gene function classification , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[109] Robert D. Finn,et al. Integrating sequence and structural biology with DAS , 2007, BMC Bioinformatics.
[110] Iddo Friedberg,et al. Automated protein function predictionçthe genomic challenge , 2006 .
[111] Gunnar Rätsch,et al. Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..
[112] Giorgio Valentini,et al. Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction , 2010, J. Integr. Bioinform..
[113] James C. Bezdek,et al. Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..
[114] Zhiwen Yu,et al. Transductive multi-label ensemble classification for protein function prediction , 2012, KDD.
[115] Stan Matwin,et al. Hierarchical Text Categorization as a Tool of Associating Genes with Gene Ontology Codes , 2004 .
[116] M. Tress,et al. Sequence-based feature prediction and annotation of proteins , 2009, Genome Biology.
[117] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[118] Yves Chauvin,et al. Backpropagation: the basic theory , 1995 .
[119] Lawrence O. Hall,et al. A Comparison of Decision Tree Ensemble Creation Techniques , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[120] Eugene M. Kleinberg,et al. On the Algorithmic Implementation of Stochastic Discrimination , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[121] Søren Brunak,et al. Prediction of human protein function according to Gene Ontology categories , 2003, Bioinform..
[122] Philip S. Yu,et al. A new method to measure the semantic similarity of GO terms , 2007, Bioinform..
[123] Giorgio Valentini,et al. Discovering multi–level structures in bio-molecular data through the Bernstein inequality , 2008, BMC Bioinformatics.
[124] E. Birney,et al. Pfam: the protein families database , 2013, Nucleic Acids Res..
[125] H. Mewes,et al. The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. , 2004, Nucleic acids research.
[126] Nicolò Cesa-Bianchi,et al. Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction , 2009, MLSB.
[127] L. Holm,et al. The Pfam protein families database , 2005, Nucleic Acids Res..
[128] W. Kim,et al. Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy , 2008, Genome Biology.
[129] David Warde-Farley,et al. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function , 2008, Genome Biology.
[130] Giorgio Valentini,et al. True Path Rule Hierarchical Ensembles , 2009, MCS.
[131] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[132] Duane Szafron,et al. Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology , 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.
[133] Quaid Morris,et al. Using the Gene Ontology Hierarchy when Predicting Gene Function , 2009, UAI.
[134] Dmitrij Frishman,et al. MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..
[135] Wei Pan,et al. Large Margin Hierarchical Classification with Mutually Exclusive Class Membership , 2011, J. Mach. Learn. Res..
[136] O. Troyanskaya,et al. Predicting gene function in a hierarchical context with an ensemble of classifiers , 2008, Genome Biology.
[137] Eyke Hüllermeier,et al. On label dependence in multilabel classification , 2010, ICML 2010.
[138] Yoram Singer,et al. BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.
[139] Grigorios Tsoumakas,et al. Random K-labelsets for Multilabel Classification , 2022 .
[140] Babak Shahbaba,et al. Gene function classification using Bayesian models with hierarchy-based priors , 2006, BMC Bioinformatics.
[141] Alexander Zien,et al. Label Propagation and Quadratic Criterion , 2006 .
[142] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[143] Marcel Dettling,et al. BagBoosting for tumor classification with gene expression data , 2004, Bioinform..
[144] Giorgio Valentini,et al. Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources , 2009, Applications of Supervised and Unsupervised Ensemble Methods.
[145] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[146] Saso Dzeroski,et al. Hierarchical multilabel classification trees for gene function prediction (Extended abstract) , 2006 .
[147] Joydeep Ghosh,et al. Enhanced hierarchical classification via isotonic smoothing , 2008, WWW.
[148] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Hierarchical Multilabel Classification Using Top-Down Label Combination and Artificial Neural Networks , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.
[149] Yinghui Li,et al. Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration , 2006, BMC Bioinformatics.
[150] William Stafford Noble,et al. Integrating Information for Protein Function Prediction , 2008 .
[151] Oleg Burdakov,et al. An O(n2) algorithm for isotonic regression , 2006 .
[152] D. Titterington,et al. Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients , 1981 .
[153] Ashok N. Srivastava,et al. Advances in Machine Learning and Data Mining for Astronomy , 2012 .
[154] Leo Breiman,et al. Bias, Variance , And Arcing Classifiers , 1996 .
[155] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[156] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[157] Quaid Morris,et al. Fast integration of heterogeneous data sources for predicting gene function with limited annotation , 2010, Bioinform..
[158] Amanda Clare,et al. The utility of different representations of protein sequence for predicting functional class , 2001, Bioinform..
[159] Xiaoyu Jiang,et al. Integration of relational and hierarchical network information for protein function prediction , 2008, BMC Bioinformatics.
[160] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[161] Daniel W. A. Buchan,et al. Protein function prediction by massive integration of evolutionary analyses and multiple data sources , 2013, BMC Bioinformatics.
[162] Saso Dzeroski,et al. Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics , 2006, PKDD.
[163] Weiqiang Wang,et al. Predicting Gene Ontology functions based on support vector machines and statistical significance estimation , 2007, Neurocomputing.
[164] Saso Dzeroski,et al. Decision trees for hierarchical multi-label classification , 2008, Machine Learning.
[165] Christophe Dessimoz,et al. Quality of Computationally Inferred Gene Ontology Annotations , 2012, PLoS Comput. Biol..
[166] Slobodan Vucetic,et al. MS-kNN: protein function prediction by integrating multiple data sources , 2013, BMC Bioinformatics.
[167] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[168] Juan Miguel García-Gómez,et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research , 2005, Bioinform..
[169] Amanda Clare,et al. Predicting gene function in Saccharomyces cerevisiae , 2003, ECCB.
[170] Jason Weston,et al. Learning Gene Functional Classifications from Multiple Data Types , 2002, J. Comput. Biol..
[171] Nello Cristianini,et al. Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.
[172] Michal Linial,et al. The Advantage of Functional Prediction Based on Clustering of Yeast Genes and Its Correlation with Non-Sequence Based Classifications , 2002, J. Comput. Biol..
[173] M. Riley,et al. Functions of the gene products of Escherichia coli , 1993, Microbiological reviews.
[174] Saso Dzeroski,et al. Predicting gene function using hierarchical multi-label decision tree ensembles , 2010, BMC Bioinformatics.
[175] Yang Wang,et al. Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..
[176] Yiannis Kompatsiaris,et al. An Empirical Study of Multi-label Learning Methods for Video Annotation , 2009, 2009 Seventh International Workshop on Content-Based Multimedia Indexing.
[177] Cajo J. F. ter Braak,et al. Gene Ontology consistent protein function prediction: the FALCON algorithm applied to six eukaryotic genomes , 2013, Algorithms for Molecular Biology.
[178] G. Valentini,et al. Functional Inference in FunCat through the Combination of Hierarchical Ensembles with Data Fusion Methods , 2010 .
[179] Giorgio Valentini,et al. An Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction , 2010, MCS.
[180] Mona Singh,et al. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps , 2005, ISMB.
[181] Alex A. Freitas,et al. A survey of hierarchical classification across different application domains , 2010, Data Mining and Knowledge Discovery.
[182] Nicolas de Condorcet. Essai Sur L'Application de L'Analyse a la Probabilite Des Decisions Rendues a la Pluralite Des Voix , 2009 .
[183] Luc De Raedt,et al. Top-Down Induction of Clustering Trees , 1998, ICML.
[184] Carol Friedman,et al. Information theory applied to the sparse gene ontology annotation network to predict novel gene function , 2007, ISMB/ECCB.
[185] Michael I. Jordan,et al. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence , 2008, Genome Biology.
[186] Haixuan Yang,et al. Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty , 2012, Bioinform..
[187] Mike Tyers,et al. BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..
[188] Michelangelo Ceci,et al. Classifying web documents in a hierarchy of categories: a comprehensive study , 2007, Journal of Intelligent Information Systems.
[189] Ambuj K. Singh,et al. Molecular Function Prediction Using Neighborhood Features , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[190] Daphne Koller,et al. Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.
[191] Robert Stevens,et al. Gene Ontology Consortium , 2014 .
[192] Claudio Gentile,et al. Random Spanning Trees and the Prediction of Weighted Graphs , 2010, ICML.
[193] Christoph H. Lampert,et al. Structured prediction by joint kernel support estimation , 2009, Machine Learning.
[194] Thibault Helleputte,et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods , 2010, Bioinform..
[195] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[196] Nicolò Cesa-Bianchi,et al. HCGene: a software tool to support the hierarchical classification of genes , 2008, Bioinform..
[197] C. Orengo,et al. Protein function annotation by homology-based inference , 2009, Genome Biology.
[198] Ting Chen,et al. An integrated probabilistic model for functional prediction of proteins , 2003, RECOMB '03.
[199] Yves Grandvalet,et al. More efficiency in multiple kernel learning , 2007, ICML '07.
[200] Peter D. Karp,et al. Prediction of Enzyme Classification from Protein Sequence without the Use of Sequence Similarity , 1997, ISMB.
[201] Giorgio Valentini,et al. Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines , 2010, Neurocomputing.
[202] A. Owen,et al. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[203] J. Jośe. A HIERARCHICAL APPROACH TO AUTOMATIC MUSICAL GENRE CLASSIFICATION , 2003 .
[204] Alex Alves Freitas,et al. Improving Local Per Level Hierarchical Classification , 2012, J. Inf. Data Manag..
[205] Jan Komorowski,et al. Predicting gene ontology biological process from temporal gene expression patterns. , 2003, Genome research.
[206] Christopher DeCoro,et al. Bayesian Aggregation for Hierarchical Genre Classification , 2007, ISMIR.
[207] R. Sharan,et al. Network-based prediction of protein function , 2007, Molecular systems biology.