Francisco J Azuaje

DrDimont: Explainable drug response predictionfrom differential analysis of multi-omics networks

DrDimont predicts differential drug response by integrating multi-omics networks and provides explainable results, outperforming methods based on differential protein expression or PageRank in breast cancer data.

BioNE: Integration of network embeddings for supervised learning

BioNE integrates network embeddings for supervised learning tasks, enabling comparison of different embedding methods in biological contexts.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

This paper develops a deep neural network approach for predicting neuroblastoma patient outcomes using graph-based feature extraction, achieving higher accuracy than traditional models.

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

The paper uses machine learning to connect proteomics data with histopathology images in kidney cancer, finding correlations between proteins and imaging-based predictions.

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

The paper uses independent component analysis to analyze melanoma transcriptomic data, distinguishing biological signals from technical biases and identifying clinically relevant patient characteristics.

Dr.Paso: Drug response prediction and analysis system for oncology research

The paper introduces a drug sensitivity prediction system using a 47-gene expression profile derived from transcriptomic analysis, validated with independent datasets and showing high concordance with observed drug responses.