A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Machine Learning
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Frequent pattern discovery in first-order logic
AI Communications
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The identification of evolutionary related (homologous) proteins is a key problem in molecular biology. Here we present a inductive logic programming based method, Homology Induction (HI), which acts as a filter for existing sequence similarity searches to improve their performance in the detection of remote protein homologies. HI performs a PSI-BLAST search to generate positive, negative, and uncertain examples, and collects descriptions of these examples. It then learns rules to discriminate the positive and negative examples. The rules are used to filter the uncertain examples in the "twilight zone". HI uses a multitable database of 51,430,710 pre-fabricated facts from a variety of biological sources, and the inductive logic programming system Aleph to induce rules. Hi was tested on an independent set of protein sequences with equal or less than 40 per cent sequence similarity (PDB40D). ROC analysis is performed showing that HI can significantly improve existing similarity searches. The method is automated and can be used via a web/mail interface.