Sabrina de Azevedo Silveira

NoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets

NoiTox presents a multi-model approach for peptide toxicity prediction, revealing label noise in datasets and showing that physicochemical descriptors are informative.

Protein novelty, sequence similarity, and negative sampling controls reveal benchmark specific discrimination in protein protein interactionprediction

The paper evaluates PPI predictors using controlled benchmarks and finds that performance varies with protein novelty and negative set characteristics, not generalizable to sequence-based models.

Mitigating Class Imbalance in Graph-Structured Data via Hierarchical Learning: Insights from Protein Binding Site Prediction

The paper introduces CLARA, a hierarchical learning framework to mitigate class imbalance in graph data, achieving high F1-score in protein binding site prediction.

BENDER DB: a database of protein binding sites across neglected disease proteomes

BENDER DB compiles predicted protein binding sites from neglected disease pathogens using AlphaFold structures and multiple predictors, offering a consensus and visualization tools to aid drug discovery.

PRIorI: a graph-based mining of structural arrangements in protein-protein interfaces

PRIorI identifies conserved structural motifs in protein-protein interfaces using graph isomorphism, demonstrating its utility in analyzing the SARS-CoV-2 Spike-ACE2 interface.

predPPI-GReMLIN: prediction of protein-protein interactions through mining of conserved bipartite graphs

predPPI-GReMLIN predicts protein-protein interactions by analyzing conserved bipartite graphs, achieving high accuracy across multiple datasets.

PERSEUcpp: A machine learning strategy to predictcell-penetrating peptides and their uptake efficiency

PERSEUcpp predicts cell-penetrating peptides and their uptake efficiency using machine learning, achieving high accuracy and interpretability.

Protein Abundance Prediction Through Machine Learning Methods

This study predicts protein abundance using machine learning based on codon usage bias, achieving high accuracy and validating predictions in metabolic models.

GAPIN: Grouped and Aligned Protein Interface Networks

GAPIN is a web tool for analyzing structural interaction networks of PDB molecules, focusing on graph clustering to identify ligand candidates and druggable spots.

Vermont: a multi-perspective visual interactiveplatform for mutational analysis

VERMONT 2.0 is a visual interactive platform that helps analyze the impact of protein point mutations by integrating sequence and structural data.