The scientist’s investigation covers issues in Artificial intelligence, Breast cancer, Data mining, Gene expression profiling and Lazy learning. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Statistics and Pattern recognition. His primary area of study in Breast cancer is in the field of Estrogen receptor.
His biological study spans a wide range of topics, including Microarray analysis techniques and Mutual information. His Gene expression profiling study incorporates themes from Microarray and Survival analysis. His Lazy learning research integrates issues from Algorithm, Instance-based learning, Model selection and Time series.
Gianluca Bontempi focuses on Artificial intelligence, Machine learning, Data mining, Lazy learning and Inference. His Artificial intelligence study combines topics from a wide range of disciplines, such as Credit card fraud and Pattern recognition. In general Machine learning, his work in Time series, Supervised learning, Multivariate statistics and Active learning is often linked to Context linking many areas of study.
His study explores the link between Data mining and topics such as Relevance that cross with problems in Redundancy. In Inference, Gianluca Bontempi works on issues like Gene regulatory network, which are connected to Computational biology. His study on Computational biology also encompasses disciplines like
His primary scientific interests are in Artificial intelligence, Machine learning, Big data, Credit card fraud and Context. His Artificial intelligence study frequently draws connections between adjacent fields such as Causal inference. His work carried out in the field of Machine learning brings together such families of science as Volatility, Dynamic factor and Random variate.
His Credit card fraud research includes elements of Computer security and Active learning. As part of one scientific family, he deals mainly with the area of Data mining, narrowing it down to issues related to the Reverse engineering, and often Inference. His Range research incorporates themes from Bioconductor and Genomics.
His main research concerns Credit card fraud, Bioconductor, Computational biology, Artificial intelligence and Genomics. His Bioconductor research is multidisciplinary, incorporating elements of Genomic data, Open data, World Wide Web and Bioinformatics. His Computational biology study combines topics in areas such as Pan cancer, DNA microarray, Network data and Cancer gene.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Terminal and Machine learning. His studies deal with areas such as Task and Outlier as well as Machine learning. Gianluca Bontempi has researched Genomics in several fields, including Epigenomics, Human genetics and Copy-number variation.
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TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.
Antonio Colaprico;Tiago C. Silva;Catharina Olsen;Luciano Garofano.
Nucleic Acids Research (2016)
Definition of Clinically Distinct Molecular Subtypes in Estrogen Receptor–Positive Breast Carcinomas Through Genomic Grade
Sherene Loi;Benjamin Haibe-Kains;Christine Desmedt;Françoise Lallemand.
Journal of Clinical Oncology (2007)
Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes.
Christine Desmedt;Benjamin Haibe-Kains;Pratyaksha Wirapati;Marc Buyse.
Clinical Cancer Research (2008)
Determinants of community structure in the global plankton interactome
Gipsi Lima-Mendez;Karoline Faust;Nicolas Henry;Johan Decelle.
minet : A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
Patrick E Meyer;Frédéric Lafitte;Gianluca Bontempi.
BMC Bioinformatics (2008)
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Souhaib Ben Taieb;Gianluca Bontempi;Amir F. Atiya;Antti Sorjamaa.
Expert Systems With Applications (2012)
Information-theoretic inference of large transcriptional regulatory networks
Patrick E. Meyer;Kevin Kontos;Frederic Lafitte;Gianluca Bontempi.
Eurasip Journal on Bioinformatics and Systems Biology (2007)
Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.
Sherene Loi;Benjamin Haibe-Kains;Christine Desmedt;Pratyaksha Wirapati.
BMC Genomics (2008)
Learned lessons in credit card fraud detection from a practitioner perspective
Andrea Dal Pozzolo;Olivier Caelen;Yann-Aël Le Borgne;Serge Waterschoot.
Expert Systems With Applications (2014)
Machine learning strategies for time series forecasting
Gianluca Bontempi;Souhaib Ben Taieb;Yann-Aël Le Borgne.
ULB Institutional Repository (2013)
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