GusmaoLab Is Born

Welcome to the official GusmaoLab website.

This blog will cover bioinformatics, AI, personal projects, literary experiments, and occasional mathematical puns. This website is made in python/Pelican and source is publicly available at GitHub.

I am a computational biologist with a strong focus on the integration of machine/deep learning, probabilistic modeling, and high-dimensional multi-omics biological data to elucidate the molecular and chromatin-level mechanisms of aging. My primary research interest lies in the study of chromatin conformation and its role in gene regulation, with the goal of developing Chromatin Architecture Biomarkers (CABs) that can serve as predictive markers for aging and age-related diseases. This work bridges computational and experimental approaches to investigate the intricate relationships between chromatin structure, epigenetic regulation, and gene expression in both normal and pathological cellular states.

To this end, I aim to advance methods that leverage hypergraph-based and deep probabilistic learning techniques to capture and model the spatial organization of chromatin. My vision is to establish a novel class of CABs that integrate insights from multi-omics datasets, enabling precise biomarker identification for aging interventions and immuno-celular therapies. With a foundation in both computational biology and molecular biology, and a growing expertise in laboratory techniques, I am well-positioned to develop innovative frameworks that unify large-scale multi-omics data, chromatin architecture studies, and precision medicine approaches to advance the understanding of cellular aging and to foster translational applications in therapeutic and pharmacogenomics research.

Fell free to contact me!