Hierarchical classification of transposable elements using machine learning

• Grant number: 15/14300-1
• Support Opportunities: Regular Research Grants
• Start date: March 01, 2016
• End date: February 28, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Ricardo Cerri
• Grantee: Ricardo Cerri
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated researchers: André Carlos Ponce de Leon Ferreira de Carvalho ; Carlos Norberto Fischer ; Márcio Porto Basgalupp
• Associated research grant(s): 16/50457-5 - Multi-objective evolutionary methods for hierarchical and multi-label classification, AP.R SPRINT

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 evolutionary process. TEs are organized in a hierarchical taxonomy, having different families and superfamilies of elements. Usually, the identification and classification of these elements is performed using Bioinformatics tools which use homology, comparing a new sequence with a dataset of many sequences which have previously identified TEs. Although this method is very used, it presents disadvantages, because homology between sequences ignores their many biochemical properties, and also the relationships between the different TE families and superfamilies. Thus, this project will investigate and propose different hierarchical classification methods for TEs using Machine Learning (ML) techniques. Different datasets will be constructed nucleotide and amino acid sequences with already previously identified TEs. For the construction of these datasets, Bioinformatics tools designed to extract biochemical characteristics from sequences will be used. Different strategies to convert sequences into attribute values adequate to be used in ML techniques will also be investigated. The datasets will then be hierarchically structured according to the TEs families and superfamilies which they belong to. The different classification methods proposed will be compared with existing literature methods, and evaluated using evaluation measures specifically proposed to hierarchical classification problems. (AU)

Scientific publications

(12)

(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)

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. (15/14300-1)

DE ABREU, IURI BONNA M.; MANTOVANI, RAFAEL G.; CERRI, RICARDO; IEEE. Incorporating Instance Correlations in Multi-label Classification via Label-Space. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2017-01-01. (15/14300-1, 12/23114-9)

COLOMBINI, GUSTAVO G.; DE ABREU, IURI BONNA M.; CERRI, RICARDO; IEEE. A Self-Organizing Map-based Method for Multi-Label Classification. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2017-01-01. (15/14300-1)

NAKANO, FELIPE KENJI; MASTELINI, SAULO MARTIELLO; BARBON, SYLVIO, JR.; CERRI, RICARDO; IEEE. Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (16/12489-2, 15/14300-1, 17/19264-9)

CERRI, RICARDO; MANTOVANI, RAFAEL G.; BASGALUPP, MARCIO P.; DE CARVALHO, ANDRE C. P. L. F.; IEEE. Multi-label Feature Selection Techniques for Hierarchical Multi-label Protein Function Prediction. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 7-pg., 2018-01-01. (13/07375-0, 15/14300-1, 12/23114-9)

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. (15/14300-1, 13/15070-4, 12/24774-2)

PEREIRA, GEAN TRINDADE; SANTOS, BRUNA ZAMITH; CERRI, RICARDO; IEEE. A Genetic Algorithm for Transposable Elements Hierarchical Classification Rule Induction. 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., 2018-01-01. (15/14300-1, 16/25078-0)

PEREIRA, GEAN TRINDADE; GABRIEL, PAULO. H. R.; CERRI, RICARDO; LOPEZIBANEZ, M. A Lexicographic Genetic Algorithm for Hierarchical Classification Rule Induction. PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19), v. N/A, p. 9-pg., 2019-01-01. (15/14300-1, 16/50457-5)

NAKANO, FELIPE KENJI; PINTO, WALTER JOSE; PAPPA, GISELE LOBO; CERRI, RICARDO; IEEE. Top-down Strategies for Hierarchical Classification of Transposable Elements with Neural Networks. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2017-01-01. (15/14300-1)

NAKANO, FELIPE KENJI; MASTELINI, SAULO MARTIELLO; BARBON, SYLVIO, JR.; CERRI, RICARDO; CHEN, X; LUO, B; LUO, F; PALADE, V; WANI, MA. Stacking Methods for Hierarchical Classification. 2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), v. N/A, p. 8-pg., 2017-01-01. (15/14300-1, 16/12489-2)

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. (16/50457-5, 15/14300-1)

PEREIRA, GEAN TRINDADE; GABRIEL, PAULO H. R.; CERRI, RICARDO; OLIVEIRA, PM; NOVAIS, P; REIS, LP. Hierarchical Classification of Transposable Elements with a Weighted Genetic Algorithm. PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2019, PT I, v. 11804, p. 13-pg., 2019-01-01. (16/50457-5, 15/14300-1)