Mathematics and Bioinformatics

🧬 Applied Statistics and Machine Learning in Life Sciences

Javier Blecua

✉ [email protected] ✉ [email protected]

https://orcid.org/0000-0002-2418-0451

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Bayesian Model of COVID-19 Transmission Dynamics

COVID-19 Transmission Model

Bayesian Statistics Epidemiology COVID-19 Markov Chain Monte Carlo Mechanistic Model

Mechanistic models and Bayesian statistics applied to analyze and forecast the spread of the COVID-19 virus, offering insights into effective control measures.

Protein Mutations - Regression and Classification Models

Protein Mutations

Machine Learning Bioinformatics Protein Mutations

Using machine learning to predict the functional and structural impact of protein mutations, crucial for drug design and understanding genetic diseases.

Risk Assessment of Tick Presence in Urban Parks

Tick Presence in Urban Parks

Machine Learning Risk Prediction Tick-Borne Diseases Urban Parks Public Health Surveillance

A geographical and environmental data-driven model using Machine Learning to identify and predict high-risk areas for tick presence in urban green spaces.

Machine Learning Classification of Rioja Wines

Wine Classification

Machine Learning Spectroscopy Wine Classification

Applying advanced Machine Learning techniques to chemical sensor data (voltammetry/spectroscopy) for highly accurate classification of Rioja wine based on origin and ageing period.

Vineyard plants affected by Armillaria fungus

Longitudinal Analysis of Armillaria Disease Progression

Armillaria Disease in Vineyard Plants

Statistical Analysis Survival Analysis Time-to-Event Modeling Plant Pathology Armillaria

Statistical and longitudinal analysis of disease progression caused by the Armillaria fungus in vineyards to support early diagnosis and management strategies.

Nutrient Concentrations in Leaf Blades - Regression Models

Leaf Blades - Nutrient Analysis

Machine Learning Regression Agriculture Leaf Blades Nutrients

Developing robust regression models to non-destructively estimate key nutrient concentrations in leaf tissue using spectral data.

Stem Water Potential in Leaf Blades - Regression Models

Leaf Blades - Stem Water Potential

Machine Learning Regression Agriculture Leaf Blades Stem Water Potential

Utilizing advanced physiological monitoring and predictive modeling to non-destructively quantify stem water potential and optimize plant hydration status.

Soil Nutrient Analysis - Regression Models

Machine Learning Regression Agriculture Soil Nutrients

Leveraging machine learning and proximal sensing to quantify essential soil macro and micronutrients. This project focuses on developing high-throughput predictive models to replace traditional, labor-intensive chemical analysis, facilitating precision fertilization and sustainable land management.

Yeast Population Dynamics during the Fermentation Process

Yeast Population Dynamics

Mathematical Modeling Agent Based Models Microbiology Yeast Fermentation

Modeling the competitive and cooperative dynamics of mixed yeast cultures during industrial fermentation to optimize end-product quality and yield.