RubenVG02

AntiBioGen

This Python project leverages neural networks to design antibiotic compounds with high affinity for specific targets, based on the IC50 values derived from FASTA target sequences and the generated SMILES representations of compounds.

BioInsectiNet

This Python project uses neural networks and genetic algorithms to design bioinsecticide compounds targeting specific proteins. By leveraging IC50 values from FASTA sequences, it generates and optimizes SMILES representations, enhancing the efficacy and specificity of bioinsecticides.

N-End-Rule

Python project to predict protein lifetime depending on the organism

NonModelGOToolkit

This project features a pipeline for Gene Ontology (GO) enrichment analysis on candidate genes with functional transcriptome annotations. It identifies enriched Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF), using R and Python to create 2D treemaps and interactive 3D visualizations of GO terms.

RubenVG02

RubenVG02/BioInsectiNet

This Python project uses neural networks and genetic algorithms to design bioinsecticide compounds targeting specific proteins. By leveraging IC50 values from FASTA sequences, it generates and optimizes SMILES representations, enhancing the efficacy and specificity of bioinsecticides.

RubenVG02/AntiBioGen

This Python project leverages neural networks to design antibiotic compounds with high affinity for specific targets, based on the IC50 values derived from FASTA target sequences and the generated SMILES representations of compounds.

RubenVG02/N-End-Rule

Python project to predict protein lifetime depending on the organism

RubenVG02/NonModelGOToolkit

This project features a pipeline for Gene Ontology (GO) enrichment analysis on candidate genes with functional transcriptome annotations. It identifies enriched Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF), using R and Python to create 2D treemaps and interactive 3D visualizations of GO terms.

RubenVG02/RubenVG02