### Antimicrobial Resistance Prediction

This work revolved around the use of multimodal learning to jointly combine clinical proteomics (MALDI-TOF) with chemical features to enhance the prediction of antimicrobial resistance.

This page contains links to some of my projects that showcase my interests and competences in machine learning and bioinformatics. The projects are grouped in:

The following projects some of the coding work for my research work.

This work revolved around the use of multimodal learning to jointly combine clinical proteomics (MALDI-TOF) with chemical features to enhance the prediction of antimicrobial resistance.

The research project on large-scale imputation of reference human epigenomes culminated in the application of the eDICE model to impute individual-specific epigenetic patterns, a case study which is one of the first applications of deep learning to personalized epigenomics.

The use of information diffusion methodologies enhances the statistical power of GWA studies by including domain knowledge in the form of molecular networks.

The repository contains the results of some research work that aimed to learn meaningful representations for individual profiles of cancer somatic mutations.

The following projects span a wide variety of machine learning topics, and are available on github as jupyter notebooks.

This notebook explores the use of seriation to find an ordering of the features of a dataset that highlights a block struckture in the correlation matrix (blockmodeling). The approach shown here is based on the Pearson Correlation Coefficient, but can be taken as a basis in general for other correlation measures (e.g. distance correlation), or simply to reorder a distance matrix.

Variational Autoencoders (VAEs) are among the most prominent generative models in the machine learning literature. This notebook explores and simulates the core mechanism behind VAEs, variational inference, and implements a VAE example.

The following projects span a wide variety of machine learning topics, and are available on github as jupyter notebooks.