Piotr Grabowski

This paper introduces GIM-CNNs, a framework using gene interaction matrices and convolutional neural networks to detect biological entities, outperforming standard methods in identifying disease-relevant factors beyond individual genes.

The paper uses deep learning with t-SNE to analyze large datasets and overcome size limitations in bioinformatics.

The paper constructs a co-regulation map of the human proteome using mass spectrometry data and machine learning to identify functional relationships between proteins.

The paper shows treeClust, an unsupervised machine-learning algorithm, outperforms correlation-based methods in detecting protein co-regulation in proteomics data due to its robustness to outliers and focus on close linear relationships.