What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
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.
His most cited work include:
- TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. (797 citations)
- Definition of Clinically Distinct Molecular Subtypes in Estrogen Receptor–Positive Breast Carcinomas Through Genomic Grade (690 citations)
- Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. (644 citations)
What are the main themes of his work throughout his whole career to date?
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
- Bioconductor and related Bioinformatics,
- Cancer and related Gene.
He most often published in these fields:
- Artificial intelligence (35.23%)
- Machine learning (28.52%)
- Data mining (18.79%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (35.23%)
- Machine learning (28.52%)
- Big data (3.36%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (101 citations)
- New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEX (86 citations)
- CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization. (62 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Gene
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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