activity
20112022
most citedBASiNETEntropy: an alignment-free method for classification of biological sequences through complex networks and entropy maximization

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.MN20223 cited

BASiNETEntropy: an alignment-free method for classification of biological sequences through complex networks and entropy maximization

Murilo Montanini Breve, Matheus Henrique Pimenta-Zanon, Fabrício Martins Lopes

The discovery of nucleic acids and the structure of DNA have brought considerable advances in the understanding of life. The development of next-generation sequencing technologies…

eess.AS20212 cited

Complex Network-Based Approach for Feature Extraction and Classification of Musical Genres

Matheus Henrique Pimenta-Zanon, Glaucia Maria Bressan, Fabrício Martins Lopes

Musical genre's classification has been a relevant research topic. The association between music and genres is fundamental for the media industry, which manages musical recommendat…

q-bio.GN20211 cited

Computational methods for differentially expressed gene analysis from RNA-Seq: an overview

Juliana Costa-Silva, Douglas S. Domingues, David Menotti +2

The analysis of differential gene expression from RNA-Seq data has become a standard for several research areas mainly involving bioinformatics. The steps for the computational ana…

q-bio.MN20121 cited

A Monte Carlo Approach to Measure the Robustness of Boolean Networks

Vitor H. P. Louzada, Fabrício M. Lopes, Ronaldo F. Hashimoto

Emergence of robustness in biological networks is a paramount feature of evolving organisms, but a study of this property in vivo, for any level of representation such as Genetic,…

cs.CV20111 cited

An iterative feature selection method for GRNs inference by exploring topological properties

Fabrício Martins Lopes, David C. Martins-Jr, Junior Barrera +1

An important problem in bioinformatics is the inference of gene regulatory networks (GRN) from temporal expression profiles. In general, the main limitations faced by GRN inference…