Fast Construction of Relational Features for Machine Learning

[1]  Huan Liu,et al.  Chi2: feature selection and discretization of numeric attributes , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[2]  Tobias Scheffer,et al.  Unbiased assessment of learning algorithms , 1997, IJCAI 1997.

[3]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[4]  Ondrej Kuzelka,et al.  Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[5]  Kristian Kersting,et al.  Gradient-based boosting for statistical relational learning: The relational dependency network case , 2011, Machine Learning.

[6]  Ondrej Kuzelka,et al.  Block-wise construction of tree-like relational features with monotone reducibility and redundancy , 2011, Machine Learning.

[7]  Arne Koopman.  Characteristic relational patterns , 2009, KDD.

[8]  Kristian Kersting,et al.  Multi-Relational Learning with Gaussian Processes , 2009, IJCAI.

[9]  Ashwin Srinivasan,et al.  The Predictive Toxicology Challenge 2000-2001 , 2001, Bioinform..

[10]  Gordon Plotkin,et al.  A Note on Inductive Generalization , 2008 .

[11]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.

[12]  Filip Železný,et al.  Prediction of DNA-binding proteins from relational features , 2012, Proteome Science.

[13]  Filip Zelezný,et al.  Integrating Multiple-Platform Expression Data through Gene Set Features , 2009, ISBRA.

[14]  Michael R. Yeaman,et al.  Mechanisms of Antimicrobial Peptide Action and Resistance , 2003, Pharmacological Reviews.

[15]  Stephen Muggleton,et al.  Inverse entailment and progol , 1995, New Generation Computing.

[16]  Guy Nimrod,et al.  Identification of DNA-binding proteins using structural, electrostatic and evolutionary features. , 2009, Journal of molecular biology.

[17]  Rina Dechter,et al.  Constraint Processing , 1995, Lecture Notes in Computer Science.

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Robert E W Hancock,et al.  Role of membranes in the activities of antimicrobial cationic peptides. , 2002, FEMS microbiology letters.

[20]  Wei Chu,et al.  Relational Learning with Gaussian Processes , 2006, NIPS.

[21]  Artem Cherkasov,et al.  Evaluating Different Descriptors for Model Design of Antimicrobial Peptides with Enhanced Activity Toward P. aeruginosa , 2007, Chemical biology & drug design.

[22]  Olivier Taboureau,et al.  Design of Novispirin Antimicrobial Peptides by Quantitative Structure–Activity Relationship , 2006, Chemical biology & drug design.

[23]  Stefan Wrobel,et al.  Transformation-Based Learning Using Multirelational Aggregation , 2001, ILP.

[24]  Mark E. Shirtliff,et al.  Antimicrobial Peptides: Primeval Molecules or Future Drugs? , 2010, PLoS pathogens.

[25]  Akinori Sarai,et al.  Moment-based prediction of DNA-binding proteins. , 2004, Journal of molecular biology.

[26]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[27]  Toby Walsh,et al.  Singleton Consistencies , 2000, CP.

[28]  Marc Torrent,et al.  Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model , 2011, PloS one.

[29]  Luc De Raedt,et al.  Effective feature construction by maximum common subgraph sampling , 2010, Machine Learning.

[30]  Hannu Toivonen,et al.  Discovery of frequent DATALOG patterns , 1999, Data Mining and Knowledge Discovery.

[31]  Huan Liu,et al.  Feature Selection: An Ever Evolving Frontier in Data Mining , 2010, FSDM.

[32]  Sitao Wu,et al.  LOMETS: A local meta-threading-server for protein structure prediction , 2007, Nucleic acids research.

[33]  J. Growdon,et al.  Molecular markers of early Parkinson's disease based on gene expression in blood , 2007, Proceedings of the National Academy of Sciences.

[34]  Mihalis Yannakakis,et al.  Algorithms for Acyclic Database Schemes , 1981, VLDB.

[35]  Ondrej Kuzelka,et al.  Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples , 2010, ILP.

[36]  Pedro M. Domingos,et al.  Hybrid Markov Logic Networks , 2008, AAAI.

[37]  Marc Torrent,et al.  A theoretical approach to spot active regions in antimicrobial proteins , 2009, BMC Bioinformatics.

[38]  T. Auton,et al.  Design of active analogues of a 15-residue peptide using D-optimal design, QSAR and a combinatorial search algorithm. , 2009, The journal of peptide research : official journal of the American Peptide Society.

[39]  Thorsten Meinl,et al.  A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston , 2005, PKDD.

[40]  Nada Lavrac,et al.  Relational Data Mining Applied to Virtual Engineering of Product Designs , 2006, ILP.

[41]  Ondrej Kuzelka,et al.  Bounded Least General Generalization , 2012, ILP.

[42]  Yaoqi Zhou,et al.  Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function , 2010, Bioinform..

[43]  Gajendra P. S. Raghava,et al.  AntiBP2: improved version of antibacterial peptide prediction , 2010, BMC Bioinformatics.

[44]  Catriel Beeri,et al.  On the Desirability of Acyclic Database Schemes , 1983, JACM.

[45]  Luc De Raedt,et al.  kFOIL: Learning Simple Relational Kernels , 2006, AAAI.

[46]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[47]  Ondrej Kuzelka,et al.  Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses , 2012, NFMCP.

[48]  Pierre Baldi,et al.  Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity , 2005, ISMB.

[49]  Giorgio Gambosi,et al.  Complexity and approximation: combinatorial optimization problems and their approximability properties , 1999 .

[50]  James Theiler,et al.  Online Feature Selection using Grafting , 2003, ICML.

[51]  Sanjeev Arora,et al.  Computational Complexity: A Modern Approach , 2009 .

[52]  Stephen Muggleton,et al.  Subsumer: A Prolog theta-subsumption engine , 2010, ICLP.

[53]  Stephen Muggleton,et al.  The Application of Inductive Logic Programming to Finite Element Mesh Design , 2007 .

[54]  Nada Lavrac,et al.  Propositionalization-based relational subgroup discovery with RSD , 2006, Machine Learning.

[55]  Michel Liquiere.  Arc Consistency Projection: A New Generalization Relation for Graphs , 2007, ICCS.

[56]  Ondrej Kuzelka,et al.  Fast estimation of first-order clause coverage through randomization and maximum likelihood , 2008, ICML '08.

[57]  Ondrej Kuzelka,et al.  Prediction of antimicrobial activity of peptides using relational machine learning , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops.

[58]  Stefan Wrobel,et al.  A Logic-Based Approach to Relation Extraction from Texts , 2009, ILP.

[59]  Yajun Yi,et al.  Molecular Alterations in Primary Prostate Cancer after Androgen Ablation Therapy , 2005, Clinical Cancer Research.

[60]  Luc De Raedt,et al.  Don't Be Afraid of Simpler Patterns , 2006, PKDD.

[61]  Peter A. Flach,et al.  Comparative Evaluation of Approaches to Propositionalization , 2003, ILP.

[62]  Joost N. Kok,et al.  The Gaston Tool for Frequent Subgraph Mining , 2005, GraBaTs.

[63]  Jan Ramon,et al.  Efficient frequent connected subgraph mining in graphs of bounded tree-width , 2010, LWA.

[64]  Stuart M. Brown,et al.  Selection and validation of differentially expressed genes in head and neck cancer , 2004, Cellular and Molecular Life Sciences CMLS.

[65]  Sandro Santagata,et al.  A HIF1α Regulatory Loop Links Hypoxia and Mitochondrial Signals in Pheochromocytomas , 2005, PLoS genetics.

[66]  Ronald Fagin,et al.  Degrees of acyclicity for hypergraphs and relational database schemes , 1983, JACM.

[67]  Stephen Muggleton,et al.  Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study , 2012, BMC Bioinformatics.

[68]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[69]  Jilles Vreeken,et al.  Compression Picks Item Sets That Matter , 2006, PKDD.

[70]  Nada Lavrac,et al.  A Study of Relevance for Learning in Deductive Databases , 1999, J. Log. Program..

[71]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[72]  Eugene C. Freuder.  Complexity of K-Tree Structured Constraint Satisfaction Problems , 1990, AAAI.

[73]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[74]  Kristian Kersting,et al.  An inductive logic programming approach to statistical relational learning: Thesis , 2006 .

[75]  Ondrej Kuzelka,et al.  Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search , 2011, BMC Bioinformatics.

[76]  Michèle Sebag,et al.  Fast Theta-Subsumption with Constraint Satisfaction Algorithms , 2004, Machine Learning.

[77]  Tomás Feder,et al.  The Computational Structure of Monotone Monadic SNP and Constraint Satisfaction: A Study through Datalog and Group Theory , 1999, SIAM J. Comput..

[78]  Xuding Zhu,et al.  Duality and Polynomial Testing of Tree Homomorphisms , 1996 .

[79]  Peter A. Flach,et al.  An extended transformation approach to inductive logic programming , 2001, ACM Trans. Comput. Log..

[80]  David J. Hill,et al.  Lifted Inference for Relational Continuous Models , 2010, Statistical Relational Artificial Intelligence.

[81]  R. Grobholz,et al.  Gene signatures of testicular seminoma with emphasis on expression of ets variant gene 4 , 2005, Cellular and Molecular Life Sciences CMLS.

[82]  Elizabeth Burnside,et al.  Learning Bayesian networks of rules with SAYU , 2005, MRDM '05.

[83]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[84]  Z. Voburka,et al.  Lasioglossins: Three Novel Antimicrobial Peptides from the Venom of the Eusocial Bee Lasioglossum laticeps (Hymenoptera: Halictidae) , 2009, Chembiochem : a European journal of chemical biology.

[85]  Jeffrey Skolnick,et al.  DBD-Hunter: a knowledge-based method for the prediction of DNA–protein interactions , 2008, Nucleic acids research.

[86]  Alan K. Mackworth.  Consistency in Networks of Relations , 1977, Artif. Intell..

[87]  Ondrej Kuzelka,et al.  Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties , 2009, ICML '09.

[88]  Reto Stöcklin,et al.  Anti‐microbial peptides: from invertebrates to vertebrates , 2004, Immunological reviews.

[89]  Z. Voburka,et al.  Novel antimicrobial peptides from the venom of the eusocial bee Halictus sexcinctus (Hymenoptera: Halictidae) and their analogs , 2010, Amino Acids.

[90]  Bruno Courcelle,et al.  The Monadic Second-Order Logic of Graphs. I. Recognizable Sets of Finite Graphs , 1990, Inf. Comput..

[91]  Ondrej Kuzelka,et al.  Relational Learning with Polynomials , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[92]  Vladimir Frecer,et al.  QSAR analysis of antimicrobial and haemolytic effects of cyclic cationic antimicrobial peptides derived from protegrin-1. , 2006, Bioorganic & medicinal chemistry.

[93]  Ashwin Srinivasan,et al.  Carcinogenesis Predictions Using ILP , 1997, ILP.

[94]  Yael Mandel-Gutfreund,et al.  Annotating nucleic acid-binding function based on protein structure. , 2003, Journal of molecular biology.

[95]  Andrei A. Bulatov,et al.  On the Power of k -Consistency , 2007, ICALP.

[96]  Jeffrey Skolnick,et al.  A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome , 2009, PLoS Comput. Biol..

[97]  B. Rost,et al.  Improved prediction of protein secondary structure by use of sequence profiles and neural networks. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[98]  Jeffrey Skolnick,et al.  Efficient prediction of nucleic acid binding function from low-resolution protein structures. , 2006, Journal of molecular biology.

[99]  Luc De Raedt,et al.  Extending ProbLog with Continuous Distributions , 2010, ILP.

[100]  Shreyas Karnik,et al.  CAMP: a useful resource for research on antimicrobial peptides , 2009, Nucleic Acids Res..

[101]  Z. Voburka,et al.  Melectin: A Novel Antimicrobial Peptide from the Venom of the Cleptoparasitic Bee Melecta albifrons , 2008, Chembiochem : a European journal of chemical biology.

[102]  Rolf H. Möhring,et al.  The Pathwidth and Treewidth of Cographs , 1993, SIAM J. Discret. Math..

[103]  Qing Zhang,et al.  The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications , 2005, BMC Bioinformatics.

[104]  Saso Dzeroski,et al.  First Order Random Forests with Complex Aggregates , 2004, ILP.

[105]  Raymond J. Mooney,et al.  Max-Margin Weight Learning for Markov Logic Networks , 2009, ECML/PKDD.