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Data and task parallelism in ILP using MapReduce

Nearly two decades of research in the area of Inductive Logic Programming (ILP) have seen steady progress in clarifying its theoretical foundations and regular demonstrations of its applicability to complex problems in very diverse domains. These results are necessary, but not sufficient, for ILP to be adopted as a tool for data analysis in an era of very large machine-generated...

ILP turns 20

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the...

Extracting Context-Sensitive Models in Inductive Logic Programming

ASHWIN SRINIVASAN 0 0 Oxford University Computing Laboratory , Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom Given domain-specific background knowledge and data in the form of

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)

Background It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans...

Parallel ILP for distributed-memory architectures

The growth of machine-generated relational databases, both in the sciences and in industry, is rapidly outpacing our ability to extract useful information from them by manual means. This has brought into focus machine learning techniques like Inductive Logic Programming (ILP) that are able to extract human-comprehensible models for complex relational data. The price to pay is...

An investigation into feature construction to assist word sense disambiguation

concerned with tasks other than WSD, while outside the scope of this paper, has obvious wider interest. Acknowledgements This work was begun when Lucia Specia visited Ashwin Srinivasan at the IBM Research

Randomised restarted search in ILP

Recent statistical performance studies of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a “heavy tail”), restarts can reduce the...

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the...

Statistical evaluation of the Predictive Toxicology Challenge 2000–2001

Motivation: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. Results: Fourteen machine learning groups generated...

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL

Computer Science, University of York , Heslington, York YO1 5DD, U.K 2 PAUL FINN Computational Chemistry, Pfizer Central Research , Ramsgate Road, Sandwich, Kent CT13 9NJ, U.K 3 ASHWIN SRINIVASAN Oxford