David Medina-Ortiz

PhageLysData is a curated, AI-ready dataset of phage lytic enzymes and depolymerases, providing annotated sequences, structural data, and embeddings for machine learning applications.

SilkRoute provides a reproducible framework for constructing multi-source biomolecular datasets through descriptor-driven workflows.

The paper evaluates how data preprocessing steps affect protein function prediction performance using antioxidant protein classification.

MAOMAO integrates and harmonizes peptide toxicity data from 54 sources to create a FAIR resource with standardized terminology and benchmark partitions for toxicity research.

The paper identifies autonomic phenotypes in older adults from a high-southern-latitude population using integrated HRV and physiological data, revealing a multidimensional structure of autonomic heterogeneity.

The paper introduces SelectZyme, a platform using protein language models and dimensionality reduction to visualize and explore protein sequence space for enzyme discovery, enabling candidate selection without relying on sequence identity or annotations.

This paper uses AI to discover and design plastic-degrading enzymes, achieving 89% accuracy in classification and generating new enzyme sequences with improved properties for PET degradation.

This paper explores integrating explainable AI techniques into machine learning models for predicting protein properties, demonstrating their effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness.

RUDEUS is a machine learning system that identifies DNA-binding proteins and classifies their DNA interaction type, achieving high precision in both tasks.

Peptipedia v2.0 expands its peptide database by 45% and integrates machine learning models to annotate peptide sequences with biological activities.