What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Ioannis Tsamardinos mainly focuses on Artificial intelligence, Machine learning, Feature selection, Data mining and Markov blanket.
The subject of his Feature selection research is within the realm of Pattern recognition.
His studies in Data mining integrate themes in fields like Biomarker, Cancer, Probabilistic logic and Backpropagation.
Markov blanket and Algorithm are frequently intertwined in his study.
His Algorithm research is multidisciplinary, incorporating perspectives in Causal Markov condition and Markov chain.
In his study, which falls under the umbrella issue of Bayesian network, Hill climbing, Graphical model, Dependency and Greedy algorithm is strongly linked to Equivalence.
His most cited work include:
- The max-min hill-climbing Bayesian network structure learning algorithm (1032 citations)
- A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (713 citations)
- Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (384 citations)
What are the main themes of his work throughout his whole career to date?
Ioannis Tsamardinos mostly deals with Artificial intelligence, Machine learning, Feature selection, Data mining and Algorithm.
As part of one scientific family, Ioannis Tsamardinos deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Pattern recognition, and often Feature.
His Bayesian network study, which is part of a larger body of work in Machine learning, is frequently linked to Function, bridging the gap between disciplines.
The concepts of his Feature selection study are interwoven with issues in Markov blanket, Feature, Lasso and Equivalence.
Ioannis Tsamardinos interconnects Cancer, Microarray analysis techniques, Statistical power, DNA microarray and Estimator in the investigation of issues within Data mining.
His Algorithm research integrates issues from Bayesian probability, Mathematical optimization, Conditional independence and Categorical variable.
He most often published in these fields:
- Artificial intelligence (43.37%)
- Machine learning (27.11%)
- Feature selection (25.30%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (43.37%)
- Feature selection (25.30%)
- Algorithm (22.89%)
In recent papers he was focusing on the following fields of study:
Ioannis Tsamardinos mainly investigates Artificial intelligence, Feature selection, Algorithm, Machine learning and Multi omics.
His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Artificial neural network.
His Feature selection study combines topics from a wide range of disciplines, such as Equivalence, Lasso, Knowledge extraction and Taxonomy.
His Algorithm research includes elements of Signal transduction, Conditional independence and Bayesian network.
His Machine learning study integrates concerns from other disciplines, such as Range, Monte Carlo method and Disease.
His Multi omics research is multidisciplinary, incorporating elements of Data integration, Computational biology and Parametric statistics.
Between 2018 and 2021, his most popular works were:
- Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers. (49 citations)
- A greedy feature selection algorithm for Big Data of high dimensionality. (20 citations)
- Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression. (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Algorithm, Feature selection, Artificial intelligence, Machine learning and Dynamical systems theory.
His Feature selection research incorporates elements of Bayesian network and Conditional independence.
His Bayesian network study incorporates themes from Markov blanket, Lasso, Selection and Heuristic.
His research combines Energy and Artificial intelligence.
His Machine learning study frequently draws parallels with other fields, such as Monte Carlo method.
His studies deal with areas such as Dynamical system, Sparse approximation, Inference and Signal transduction as well as Dynamical systems theory.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
