Fabio Fabris

Orcid: 0000-0001-7159-4668

According to our database1, Fabio Fabris authored at least 13 papers between 2014 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2021
A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021

2020
Using deep learning to associate human genes with age-related diseases.
Bioinform., 2020

Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes.
Briefings Bioinform., 2020

2019
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool.
Proceedings of the Machine Learning, Optimization, and Data Science, 2019

2018
A new approach for interpreting Random Forest models and its application to the biology of ageing.
Bioinform., 2018

Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem.
Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017
New probabilistic graphical models and meta-learning approaches for hierarchical classification, with applications in bioinformatics and ageing.
PhD thesis, 2017

A Situation-Aware Fear Learning (SAFEL) model for robots.
Neurocomputing, 2017

2016
An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes.
IEEE ACM Trans. Comput. Biol. Bioinform., 2016

New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins.
Bioinform., 2016

2015
A Novel Extended Hierarchical Dependence Network Method Based on Non-hierarchical Predictive Classes and Applications to Ageing-Related Data.
Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015

2014
An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions.
Proceedings of the 25th International Workshop on Database and Expert Systems Applications, 2014

Dependency network methods for Hierarchical Multi-label Classification of gene functions.
Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining, 2014


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