A5046373884- Genomics and Chromatin Dynamics
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Algorithms and Data Compression
- Gene expression and cancer classification
- Single-cell and spatial transcriptomics
- Advanced Text Analysis Techniques
- Evolutionary Algorithms and Applications
- Genetic and phenotypic traits in livestock
- Text and Document Classification Technologies
- Data Mining Algorithms and Applications
- Metaheuristic Optimization Algorithms Research
- Genetics, Bioinformatics, and Biomedical Research
- Advanced Multi-Objective Optimization Algorithms
Universidade Federal do Paraná
2022-2024
Universidade Federal do Pará
2022
Universidade Tecnológica Federal do Paraná
2016-2018
De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it an important problem in Bioinformatics. The correct recognition TFBS plays role understanding the mechanisms gene regulation helps to develop new drugs.We here present Memetic Framework for Motif Discovery (MFMD), algorithm that uses semi-greedy constructive heuristics as local optimizer. In addition, we used hybridization classic genetic global optimizer refine solutions...
Abstract Background Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers , making the counting of these elements critical aspect for ensuring not only accuracy but also efficiency analytical process. This is particularly useful scenarios involving large data volumes, such those generated by ChIP-seq protocol. Against this backdrop, we introduce biomapp::chip tool specifically designed to...
Finding transcription factor binding sites plays an important role inside bioinformatics. Its correct identification in the promoter regions of co-expressed genes is a crucial step for understanding gene expression mechanisms and creating new drugs vaccines. The problem finding motifs consists seeking conserved patterns biological datasets sequences, through using unsupervised learning algorithms. This considered one open problems computational biology, which its simplest formulation has...
The identification of transcription factors binding sites (TFBS) - also called motifs in DNA sequences is the first step to understanding how works gene regulation. Recognizing these patterns promoter regions co-expressed genes a determining key for this. Although there are several algorithms this purpose, problem still far from being solved because great diversity expression and low specificity. State art have limitations, such as high number false positives accuracy Identifying weak...
Abstract Motivation The search for conserved motifs in DNA sequences is an important problem bioinformatics. growing availability of large-scale genomic data poses significant challenges computational biology, particularly terms efficiency analysis, kmer identification, and noise presence. detection patterns crucial understanding gene functions regulations. Therefore, it essential to develop a structure that can handle these large volumes information provide accurate fast results. Results We...
Abstract Background Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers , making the counting of these elEMents critical aspect for ensuring not only accuracy but also efficiency analytical process. This is particularly useful scenarios involving large data volumes, such those generated by ChIP-seq protocol. Against this backdrop, we introduce biomapp ::chip tool specifically designed to...