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Publications

Publications by Rui Camacho

2000

A Note on Two Simple Transformations for Improving the Efficiency of an ILP System

Authors
Costa, VS; Srinivasan, A; Camacho, R;

Publication
Inductive Logic Programming, 10th International Conference, ILP 2000, London, UK, July 24-27, 2000, Proceedings

Abstract

2012

Conceptual clustering of multi-relational data

Authors
Fonseca, NA; Santos Costa, V; Camacho, R;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k?-?medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters. © 2012 Springer-Verlag Berlin Heidelberg.

2011

Assessing the Effect of 2D Fingerprint Filtering on ILP-Based Structure-Activity Relationships Toxicity Studies in Drug Design

Authors
Camacho, R; Pereira, M; Costa, VS; Fonseca, NA; Simoes, CJV; Brito, RMM;

Publication
5TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS (PACBB 2011)

Abstract
The rational development of new drugs is a complex and expensive process. A myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognised as the major hurdle behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship studies, using relational Machine Learning algorithms, proved already to be very useful in the complex process of rational drug design. However, a typical problem with those studies concerns the use of available repositories of previously studied molecules. It is quite often the case that those repositories are highly biased since they contain lots of molecules that are similar to each other. This results from the common practice where an expert chemist starts off with a lead molecule, presumed to have some potential, and then introduces small modifications to produce a set of similar molecules. Thus, the resulting sets have a kind of similarity bias. In this paper we assess the advantages of filtering out similar molecules in order to improve the application of relational learners in Structure-Activity Relationship (SAR) problems to predict toxicity. Furthermore, we also assess the advantage of using a relational learner to construct comprehensible models that may be quite valuable to bring insights into the workings of toxicity.

2010

Predicting the Start of Protein alpha-Helices Using Machine Learning Algorithms

Authors
Camacho, R; Ferreira, R; Rosa, N; Guimaraes, V; Fonseca, NA; Costa, VS; de Sousa, M; Magalhaes, A;

Publication
ADVANCES IN BIOINFORMATICS

Abstract

2009

Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

Authors
Pereira, M; Costa, VS; Camacho, R; Fonseca, NA; Simoes, C; Brito, RMM;

Publication
ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, PROCEEDINGS

Abstract
The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.

2009

Visually Guiding and Controlling the Search While Mining Chemical Structures

Authors
Pereira, M; Costa, VS; Camacho, R; Fonseca, NA;

Publication
DISTRIBUTED COMPUTING, ARTIFICIAL INTELLIGENCE, BIOINFORMATICS, SOFT COMPUTING, AND AMBIENT ASSISTED LIVING, PT II, PROCEEDINGS

Abstract
In this paper we present the work in progress on LogCHEM, an ILP based tool for discriminative interactive mining of chemical fragments. In particular, we describe the integration with a molecule visualisation software that allows the chemist to graphically control the search for interesting patterns in chemical fragments. Furthermore, we show how structured information, such as rings, functional groups like carboxyl, amine, methyl, ester, etc are integrated and exploited in LogCHEM.

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