In BEND we explore the biocomputing area applying pattern recognition techniques in large amounts of data to address theoretical and experimental challenges in biological systems. We have worked in genomic modeling for predicting chemosensitivity, cancer cell tracking in microscope video for representation of cytotoxicity, tracking microorganisms in real time video generated by electron microscopy where tracking continuously makes it possible to evaluate the effect of different types of therapies and estimate what may work best in personalized therapies.
Prediction of chemosensitivity
The goal is to provide personalized therapy to cancer patients by determining which chemotherapy is ideal for their profile based on their genetic code.
Through temporal and spatial segmentation, it separates, identifies and diagnoses the behavior of cells affected by a disease and to which has been applied some treatment to determine its effectiveness.
Using pattern recognition and nonlinear spectroscopy generated through a doped optical fiber device, we aim to diagnose Zika, Dengue and Chincungunya viruses in blood samples.