Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
C4.5: programs for machine learning
C4.5: programs for machine learning
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Genome scale prediction of protein functional class from sequence using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Applying hybrid reasoning to mine for associative features in biological data
Journal of Biomedical Informatics
Parallel ILP for distributed-memory architectures
Machine Learning
DNA Replication as a Model for Computational Linguistics
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Pertinent background knowledge for learning protein grammars
ECML'06 Proceedings of the 17th European conference on Machine Learning
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One of the fastest advancing areas of modern science is functional genomics. This science seeks to understand how the complete complement of molecular components of living organisms (nucleic acid, protein, small molecules and so on) interact together to form living organisms. Functional genomics is of interest to AI because the relationship between machines and living organisms is central to AI and because the field is an instructive and fun domain to apply and sharpen AI tools and ideas, requiring complex knowledge representation, reasoning, learning, and so on. This article describes two machine learning (inductive logic programming [ILP])-based approaches to the bioinformatic problem of predicting protein function from amino acid sequence. The first approach is based on using ILP as a way of bootstrapping from conventional sequence-based homology methods. The second approach used protein-functional ontologies to provide function classes and a hybrid ILP method to predict function directly from sequence. Both ILP approaches were successful in producing accurate prediction rules that could biologically be interpreted. The work was also of interest to machine learning research because it highlighted the flexibility of ILP systems in dealing with heterogeneous data, the importance of problems where classes are related hierarchically, and problems where examples have more than one functional class.