What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- Statistics
Genetics, Gene, Phylogenetics, Theoretical computer science and Phylogenetic tree are his primary areas of study.
He combines subjects such as Human evolution, Computational biology and Population genetics with his study of Genetics.
He interconnects Cluster of differentiation and Single-cell analysis in the investigation of issues within Gene.
His studies deal with areas such as Artificial neural network, Set, Resolution, Graph and Optimization algorithm as well as Theoretical computer science.
His Artificial neural network research is classified as research in Artificial intelligence.
Pietro Liò is interested in Graph classification, which is a branch of Graph.
His most cited work include:
- Graph Attention Networks (2210 citations)
- Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair (1360 citations)
- Graph Attention Networks (699 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Computational biology, Genetics and Gene.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Graph and Pattern recognition.
His study on Machine learning is mostly dedicated to connecting different topics, such as Graph.
Pietro Liò works in the field of Computational biology, focusing on Systems biology in particular.
His research in Genome and Phylogenetic tree are components of Genetics.
He mostly deals with Gene expression in his studies of Gene.
He most often published in these fields:
- Artificial intelligence (24.25%)
- Machine learning (14.95%)
- Computational biology (13.95%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (24.25%)
- Machine learning (14.95%)
- Graph (5.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Graph, Deep learning and Graph.
He works mostly in the field of Artificial intelligence, limiting it down to concerns involving Pattern recognition and, occasionally, Image and Superresolution.
His work deals with themes such as Range, Variety, Key and Neuroimaging, which intersect with Machine learning.
The concepts of his Graph study are interwoven with issues in Algorithm, Theoretical computer science and Feature learning.
His Theoretical computer science research is multidisciplinary, relying on both Pooling, Visual reasoning, Graph classification, Random walk and Visualization.
Pietro Liò specializes in Graph, namely Graph neural networks.
Between 2018 and 2021, his most popular works were:
- A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. (81 citations)
- A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients (28 citations)
- Predicting factors for survival of breast cancer patients using machine learning techniques (25 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- Statistics
Pietro Liò mainly focuses on Artificial intelligence, Machine learning, Graph, Theoretical computer science and Deep learning.
In his research, Superresolution and Image is intimately related to Pattern recognition, which falls under the overarching field of Artificial intelligence.
His Machine learning research includes elements of Variety, Clinical decision support system and Generative grammar.
His Graph classification and Graph neural networks study in the realm of Graph interacts with subjects such as Syllogism.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Graph, Visual reasoning, Raven's Progressive Matrices and Euler diagram, Diagrammatic reasoning.
His Deep learning research integrates issues from Sequence, Mutual information, Feature learning and Maximization.
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