My portfolio includes projects Bioinformatics, machine learning, web development, and online educational resources I have collaborated with.
This project was about creating a database of herbarium records from tropical forest species of the Americas and the development of machine learning classifiers to predict the phenological stages of these species using the herbarium records.
This project was about the exploration of the antiparasitic peptides’ chemical space and prediction of new potential compounds through a new approach based on network science and similarity searching.
This project summarizes the weekly reports of SARS-CoV2 variants in European countries since 2020 until April 2022 using the data from the GISAID database.
I created machine learning classifiers to predict the antimicrobial activity of peptides. It was an end-to-end project in which I did the data pre-processing, feature engineering, training, hyperparameter tuning, and deployment of the best model as a web application with Streamlit.
This project aimed to create an open-source educational resource of computational tools (e.g., GNU/Linux, terminal usage, Bash-AWK scripting, etc) for Bioinformatics enthusiasts written in Spanish.
In this project, we created a plant disease detector based on convolutional neural networks, trained to recognize two types of maize infectious diseases - Common rust of corn and Northern corn leaf blight.
This project is a computational study of the structural changes of two Cereblon protein mutations using Molecular Dynamics and Quantum Mechanics simulations.
In this project, we understood the impact of horizontal gene transfer in the genome of Streptomyces clavuligerus using Phylogenetics, RNAseq data, and other bioinformatics tools.
In this project, we created a webpage for the Innovation challenge For more data on labor informality hackathon. This digital solution aimed to gather information from workers in labor informality and employers, creating a community where all people could interact with each other and create suitable work opportunities.
In this project, I created machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) to evaluate the activity of molecules that have been experimentally tested to bind or not bind to Beta-Lactamases. Also, I implemented a web service with the best model.
In this project, we captured sentiments of replies to tweets from two Ecuadorian presidential candidates in the 2021 elections (@LassoGuillermo and @ecuarauz). A model of artificial neural networks was used to classify tweets considering their sentiment as positive, negative, or neutral.
In this project, we identified binding specificity between repressor proteins and a transcriptional factor associated with the Jasmonic Acid pathway in Arabidopsis thaliana through Molecular Dynamics simulations and Machine Learning algorithms.