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Publications

Publications by Rui Camacho

2016

Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems

Authors
Camacho, R; Barbosa, JG; Sampaio, AM; Ladeiras, J; Fonseca, NA; Costa, VS;

Publication
Resource Management for Big Data Platforms - Algorithms, Modelling, and High-Performance Computing Techniques

Abstract

2015

Predicting Drugs Adverse Side-Effects Using a Recommender-System

Authors
Pinto, D; Costa, P; Camacho, R; Costa, VS;

Publication
DISCOVERY SCIENCE, DS 2015

Abstract
Adverse Drug Events (ADEs) are a major health problem, and developing accurate prediction methods may have a significant impact in public health. Ideally, we would like to have predictive methods, that could pinpoint possible ADRs during the drug development process. Unfortunately, most relevant information on possible ADRs is only available after the drug is commercially available. As a first step, we propose using prior information on existing interactions through recommendation systems algorithms. We have evaluated our proposal using data from the ADReCS database with promising results.

2013

Boosting the Detection of Transposable Elements Using Machine Learning

Authors
Loureiro, T; Camacho, R; Vieira, J; Fonseca, NA;

Publication
Advances in Intelligent Systems and Computing

Abstract
Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.

2013

Improving the performance of Transposable Elements detection tools

Authors
Loureiro, T; Camacho, R; Vieira, J; Fonseca, NA;

Publication
J. Integrative Bioinformatics

Abstract

2013

Inferring UI Patterns with Inductive Logic Programming

Authors
Nabuco, M; Paiva, ACR; Camacho, R; Faria, JP;

Publication
PROCEEDINGS OF THE 2013 8TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2013)

Abstract
This paper presents an approach to infer UI patterns existent in a web application. This reverse engineering process is performed in two steps. First, execution traces are collected from user interactions using the Selenium software. Second, the existing UI patterns within those traces are identified using Machine Learning inference with the Aleph ILP system. The paper describes and illustrates the proposed methodology on a case study over the Amazon web site.

2013

Integrative functional statistics in logic programming

Authors
Angelopoulos, N; Santos Costa, V; Azevedo, J; Wielemaker, J; Camacho, R; Wessels, L;

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

Abstract
We present r..eal , a library that integrates the R statistical environment with Prolog. Due to R's functional programming affinity the interface introduced has a minimalistic feel. Programs utilising the library syntax are elegant and succinct with intuitive semantics and clear integration. In effect, the library enhances logic programming with the ability to tap into the vast wealth of statistical and probabilistic reasoning available in R. The software is a useful addition to the efforts towards the integration of statistical reasoning and knowledge representation within an AI context. Furthermore it can be used to open up new application areas for logic programming and AI techniques such as bioinformatics, computational biology, text mining, psychology and neuro sciences, where R has particularly strong presence. © 2013 Springer-Verlag.

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