The scientist’s investigation covers issues in Data mining, Machine learning, Artificial intelligence, Algorithm and Sequential logic. His work on Sequential Pattern Mining as part of general Data mining study is frequently connected to Biclustering and Process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Arlindo L. Oliveira has included themes like Classifier and Categorization in his Machine learning study.
His work on Question answering, Latent semantic analysis and Segmentation as part of general Artificial intelligence study is frequently linked to Nearest neighbour and Brain tumor segmentation, bridging the gap between disciplines. In the field of Algorithm, his study on Time complexity overlaps with subjects such as Overall survival, Brain tumor and Accurate segmentation. His research integrates issues of Graph, Theoretical computer science, Logic synthesis, Finite-state machine and Digital watermarking in his study of Sequential logic.
Algorithm, Artificial intelligence, Data mining, Theoretical computer science and Machine learning are his primary areas of study. The study incorporates disciplines such as Set, Data structure and Compressed suffix array in addition to Algorithm. His work on Classifier and Text categorization as part of general Artificial intelligence research is often related to Overall survival and Brain tumor segmentation, thus linking different fields of science.
His Data mining research is multidisciplinary, incorporating elements of Regularization, Expression and Constraint. His Theoretical computer science research incorporates elements of Approximate string matching, Integer programming, String searching algorithm and Computation. His Machine learning study combines topics in areas such as Tumor progression and Glioma.
Arlindo L. Oliveira spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Data mining and Gene regulatory network. His research in the fields of Segmentation overlaps with other disciplines such as Area under the roc curve and Ischemic stroke. In general Machine learning study, his work on Deep learning often relates to the realm of Overall survival, Work, Treatment decision making and Outcome, thereby connecting several areas of interest.
Arlindo L. Oliveira combines subjects such as Tumor progression and Glioma with his study of Algorithm. His studies deal with areas such as Regularization, Hybrid genome assembly, Reference genome and Search engine indexing as well as Data mining. His Gene regulatory network study combines topics in areas such as Estimation theory and Experimental data.
His primary areas of investigation include Artificial intelligence, Machine learning, Tumor progression, Magnetic resonance imaging and Algorithm. Arlindo L. Oliveira combines subjects such as Instrumentation, Biotechnology, Data integration and Key with his study of Artificial intelligence. His studies link Text mining with Machine learning.
His Glioma research extends to Tumor progression, which is thematically connected. Among his Magnetic resonance imaging studies, you can observe a synthesis of other disciplines of science such as Accurate segmentation and Segmentation.
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Biclustering Algorithms for Biological Data Analysis: A Survey
Sara C. Madeira;Arlindo L. Oliveira.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2004)
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
Unknown Journal (2018)
The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae
Miguel C. Teixeira;Pedro T. Monteiro;Pooja Jain;Sandra Tenreiro.
Nucleic Acids Research (2006)
Temporal Data Mining: an overview
Cláudia M. Antunes;Arlindo L. Oliveira.
The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae
Miguel Cacho Teixeira;Pedro Tiago Monteiro;Joana Fernandes Guerreiro;Joana Pinho Gonçalves.
Nucleic Acids Research (2014)
YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface
Dário Abdulrehman;Pedro Tiago Monteiro;Miguel Cacho Teixeira;Nuno Pereira Mira.
Nucleic Acids Research (2011)
YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae
Pedro T. Monteiro;Nuno D. Mendes;Miguel C. Teixeira;Sofia d’Orey.
Nucleic Acids Research (2007)
Techniques for the creation of digital watermarks in sequential circuit designs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2001)
Identification of Regulatory Modules in Time Series Gene Expression Data Using a Linear Time Biclustering Algorithm
Sara C. Madeira;Miguel C. Teixeira;Isabel Sa-Correia;Arlindo L. Oliveira.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2010)
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
Cláudia Antunes;Arlindo L. Oliveira.
machine learning and data mining in pattern recognition (2003)
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