A5088676977- Genomics and Phylogenetic Studies
- Optimal Power Flow Distribution
- RNA and protein synthesis mechanisms
- Microbial Community Ecology and Physiology
- Machine Learning in Bioinformatics
- Environmental DNA in Biodiversity Studies
- VLSI and FPGA Design Techniques
- Microgrid Control and Optimization
- Metaheuristic Optimization Algorithms Research
- Cancer-related molecular mechanisms research
- Vehicle Routing Optimization Methods
- Scheduling and Optimization Algorithms
- Advanced Multi-Objective Optimization Algorithms
- Genomics and Chromatin Dynamics
- Advanced Manufacturing and Logistics Optimization
- Evolutionary Algorithms and Applications
- Power System Optimization and Stability
- Bacteriophages and microbial interactions
- Assembly Line Balancing Optimization
- Electric Power System Optimization
- Plant-Microbe Interactions and Immunity
- Sports Performance and Training
- Data Mining Algorithms and Applications
- Sports Analytics and Performance
- Algorithms and Data Compression
Universidade Tecnológica Federal do Paraná
2015-2024
Universidade Estadual de Londrina
2022
Universidade de São Paulo
2011-2015
University of Twente
2013
Universidade Federal de São Carlos
2007-2009
One of the main challenges in applying machine learning algorithms to biological sequence data is how numerically represent a numeric input vector. Feature extraction techniques capable extracting numerical information from sequences have been reported literature. However, many these are not available existing packages, such as mathematical descriptors. This paper presents new package, MathFeature, which implements descriptors able extract relevant sequences, i.e. DNA, RNA and proteins...
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced genome annotation and analysis, a fundamental aspect genomics that facilitates understanding ncRNA functions regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep algorithms provided advancements classification. This study presents BioDeepFuse,...
As consequence of the various genomic sequencing projects, an increasing volume biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number sequence-related problems, results are largely affected by type and features extracted. This effect has motivated new pipeline proposals, mainly involving feature extraction in which extracting significant discriminatory information from set challenging. Considering this, our work...
CRISPR-Cas systems are adaptive immune in prokaryotes, providing resistance against invading viruses and plasmids. The identification of CRISPR loci is currently a non-standardized, ambiguous process, requiring the manual combination multiple tools, where existing tools detect only parts CRISPR-systems, lack quality control, annotation assessment capabilities detected loci. Our CRISPRloci server provides first resource for prediction all possible integrates series advanced Machine Learning...
Recent technological advances have led to an exponential expansion of biological sequence data and extraction meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding mechanisms related several fatal diseases, e.g. Cancer coronavirus disease 2019, helping develop innovative solutions, such as CRISPR-based gene editing, vaccine precision medicine. These benefit our society economy, directly impacting people's lives in various areas, health...
The development of bio-based products has increased in recent years, and species the Bacillus genus have been widely used for product due to their elevated production antimicrobial molecules resistance extreme environmental conditions through endospore formation. In this context, antifungal potential velezensis CMRP 4489 was investigated using silico predictions secondary metabolites its genome vitro tests against following phytopathogenic fungi: Sclerotinia sclerotiorum, Macrophomina...
Abstract Background Metagenomics is an expanding field within microbial ecology, microbiology, and related disciplines. The number of metagenomes deposited in major public repositories such as Sequence Read Archive (SRA) Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) rising exponentially. However, data mining interpretation can be challenging due to mis-annotated misleading metadata entries. In this study, we describe the Marine Metagenome Metadata Database...
We hypothesize that sample species abundance, sequencing depth, and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG in three silico microbial communities composed 42 with same richness but different distribution profiles using pipelines for recovery. The pipeline developed by Parks colleagues (8K) generated highest number MAGs lowest true positives per community profile. Karst (DT) showed most accurate results (~...
The best known exact solver for generating provably optimal solutions to the Traveling Salesman Problem (TSP) is Concorde algorithm. uses a branch and bound search strategy, as well cutting planes reduce space. first step in using obtain good initial solution. A solution can be generated heuristic outside of Concorde, or generate its own Chained Lin Kernighan (LK) In this paper, we speed up by improving produced LK Partition Crossover. Crossover powerful deterministic recombination operator...
Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data techniques have attracted the attention information industry and society as whole, due large amount imminent need turn it into useful knowledge. However, effective use some areas is still under development, case sports, which recent years, has presented slight growth; consequently, many sports organizations begun see that there wealth unexplored knowledge extracted by...
Machine learning algorithms have been applied to numerous transcript datasets identify Long non-coding RNAs (lncRNAs). Nevertheless, before these are RNA data, features must be extracted from the original sequences. As many of can redundant or irrelevant, predictive performance improved by performing feature selection. However, most current approaches usually select independently, ignoring possible relations. In this paper, we propose a new model, which identifies best subsets, removing...
A genetic algorithm using Edge Assemble Crossover (EAX) is one of the best heuristic solvers for large instances Traveling Salesman Problem. We propose Partition to recombine solutions produced by EAX. a powerful deterministic recombination that highly exploitive. When decomposes two parents into q recombining components, partition crossover returns 2q reachable offspring. If are locally optimal, all offspring also optimal in hyperplane subspace contains parents. One disadvantage Crossover,...
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 Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over past years, generation metagenomics has increased exponentially, so availability reusability present in public repositories. However, identifying which datasets associated metadata are available is not straightforward. We created Animal-Associated Metagenome Metadata Database (AnimalAssociatedMetagenomeDB - AAMDB) to facilitate identification reuse...
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...