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 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.
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.
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.
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.
Graph Attention Networks
Petar Veličković;Guillem Cucurull;Arantxa Casanova;Adriana Romero.
international conference on learning representations (2018)
Hematopoietic Stem Cells Reversibly Switch from Dormancy to Self-Renewal during Homeostasis and Repair
Anne Wilson;Elisa Laurenti;Gabriela Oser;Richard C. van der Wath.
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)
Periodic gene expression program of the fission yeast cell cycle
Gabriella Rustici;Juan Mata;Katja Kivinen;Pietro Lió.
Nature Genetics (2004)
The BioMart community portal: an innovative alternative to large, centralized data repositories
Damian Smedley;Syed Haider;Steffen Durinck;Luca Pandini.
Nucleic Acids Research (2015)
Molecular phylogenetics: state-of-the-art methods for looking into the past.
Simon Whelan;Pietro Liò;Nick Goldman.
Trends in Genetics (2001)
Models of Molecular Evolution and Phylogeny
Pietro Liò;Nick Goldman.
Genome Research (1998)
Towards real-time community detection in large networks.
Ian X. Y. Leung;Pan Hui;Pietro Liò;Jon Crowcroft.
Physical Review E (2009)
Histidine biosynthetic pathway and genes: structure, regulation, and evolution.
P Alifano;R Fani;P Liò;A Lazcano.
Microbiological Research (1996)
Biometric evidence that sexual selection has shaped the hominin face.
Eleanor M. Weston;Adrian E. Friday;Pietro Liò.
PLOS ONE (2007)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: