Ricardo Cerri
CV Lattes
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Universidade Federal de São Carlos (UFSCAR). Centro de Ciências Exatas e de Tecnologia (CCET) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Graduated in Bachelor in Computer Science from São Paulo State University Julio de Mesquita Filho, and M.Sc and P.hD in Computer Science and Computational Mathematics at the University of São Paulo (ICMC/USP). During his PhD, he was a visiting researcher at University of Surrey and University of Kent, both in United Kingdom. Has experience in Computer Science, working mainly with the themes Bioinformatics, Machine Learning and Advanced Techniques for Data Classification and Regression, with special interest in multi-output learning (multi-label / multi-target / hierarchical prediction). He is currently an Assistant Professor at the Department of Computer Science in Federal University of São Carlos, supervising graduate and bachelor students. (Source: Lattes Curriculum)
- IEEE World Congress on Computational Intelligence, AR.BR
- Multi-objective evolutionary methods for hierarchical and multi-label classification, AP.R
Abstract
In conventional classification, an instance is classified in just one among two or more classes. These problems are called single-label classification problems. However, there are more complex problems in which an instance can be classified in two or more classes simultaneously. These are known in the Machine Learning literature as multi-label classification problems. When the classes i...
- Hierarchical classification of transposable elements using machine learning, AP.R
Abstract
Transposable Elements (TEs) are DNA sequences which can move from one place to another inside the genome of a cell. These elements contribute to the genetic diversity of species, and their transposition mechanisms may affect the functionality of genes. The correct identification and classification of these elements is useful for the comprehension of their effects in the genomes evolutio...
(Only some records are available in English at this moment)
- Multi-label classification in data-streams, BP.MS
Abstract
Data Streams are unlimited data sequences, continuously generated, non-stationary, and in many cases arriving in high speed. Novelty Detection is a classification task that verifies if an example, or a set of examples, differs significantly from the previously seen examples. This is an important task for Data Stream Classification, since new concepts may appear, disappear, or evolve ove...
- Multi-label machine learning for protein subcellular localization, BP.IC
Abstract
Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein's functions. As there are a lot of proteins that resides at the same time in two or more locations in a cell or move between locations, this can be an arduous task. In Machine Learning, this kind of classification task is named m...
- Hierarchical classification of transposable elements and protein functions making use of machine learning, BP.IC
Abstract
Proteins are macromolecules responsible for almost every task necessary for the maintenance of cells, playing an essential role in the behavior and regulation of organisms. Advances in Molecular Biology have allowed an almost complete listing of the proteins that make up the organisms. However, there are a large number of proteins whose function is still unknown, opening space for a new...
(Only some records are available in English at this moment)
- Multi-output learning for data streams classification, BE.PQ
Abstract
Data streams (DS) are unlimited, continuously generated, non-stationary, and in many cases high-speed data. Many real-world applications generate large amounts of data in a continuous stream, and with the evolution of Information Technology, more data will be generated and collected constantly. This highlights the relevance and necessity for developing algorithms capable of extracting r...
- Evaluation of novelty detection algorithms for multi-label data streams classification, BE.EP.MS
Abstract
Data streams (DS) are potentially unlimited sequences, generated continuously, non-stationary and, in many cases, at high speed. There are several studies currently investigating the application of Novelty Detection (ND) techniques in DS. This is because ND is an important task, mainly because new concepts may appear, disappear or evolve over time. Most of the work found in the novelty ...
- Active learning for protein subcellular localization, BE.EP.IC
Abstract
Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein's functions. As there are a lot of proteins that reside at the same time in two or more locations in a cell or move between locations, usually supervised multi-label classification (MLC) methods are designed to attack this proble...
- Active learning in hierarchical classification of transposable elements, BE.EP.MS
Abstract
Tranposable Elements (TEs) are DNA sequences capable of moving within a cell's genome. Such movement causes genetic variability, and changes in gene's functionality. Usually TEs classification is performed using homology tools. Homology tries to find similar sequences by matching then in a string like fashion, however, such method ignores many biochemical and hierarchical properties. No...
(Only some records are available in English at this moment)
| 4 / 4 | Completed research grants |
| 7 / 6 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 5 / 5 | Completed scholarships abroad |
| 17 / 16 | All research grants and scholarships |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 3 |
| Citations | 17 |
| Cit./Article | 5.7 |
| Data from Web of Science | |
SCHIETGAT, LEANDER; VENS, CELINE; CERRI, RICARDO; FISCHER, CARLOS N.; COSTA, EDUARDO; RAMON, JAN; CARARETO, CLAUDIA M. A.; BLOCKEEL, HENDRIK. A machine learning based framework to identify and classify long terminal repeat retrotransposons. PLOS COMPUTATIONAL BIOLOGY, v. 14, n. 4, APR 2018. Web of Science Citations: 2.
CERRI, RICARDO; BASGALUPP, MARCIO P.; BARROS, RODRIGO C.; DE CARVALHO, ANDRE C. P. L. F.. Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms. APPLIED SOFT COMPUTING, v. 77, p. 584-604, APR 2019. Web of Science Citations: 1.
CERRI, RICARDO; BARROS, RODRIGO C.; DE CARVALHO, ANDRE C. P. L. F.; JIN, YAOCHU. Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC Bioinformatics, v. 17, SEP 15 2016. Web of Science Citations: 14.
(References retrieved automatically from State of São Paulo Research Institutions)
CERRI, Ricardo. Técnicas de classificação hierárquica multirrótulo. 2010. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CERRI, Ricardo. Redes neurais e algoritmos genéticos para problemas de classificação hierárquica multirrótulo. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.