Alejandro Leonardo García Navarro

Alejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián et al.

Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visualization. However, certain libraries have become outdated, limiting their functionality and performance. Users can use Python's advanced machine learning and AI capabilities alongside R's robust statistical packages by combining these two programming languages. This paper explores using R's reticulate package to call Python from R, providing practical examples and highlighting scenarios where this integration enhances productivity and analytical capabilities. With a few hello-world code snippets, we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.

Alejandro Leonardo García Navarro, Javier Cachón Ortiz, Javier González Colsa et al.

Cancer is one of the leading causes of death worldwide, making the development of rapid, minimally invasive, label-free and scalable diagnostic strategies a major challenge in modern oncology. In this context, spectroscopic liquid biopsy has emerged as a promising alternative, as it enables the holistic characterization of biochemical alterations in biological fluids. In this work, we propose a multimodal spectroscopic liquid biopsy framework for multicancer detection based on the combination of Fourier Transform Infrared (FTIR) spectroscopy, Raman spectroscopy, and Excitation-Emission Matrix (EEM) fluorescence spectroscopy together with Machine Learning (ML) methodologies. Serum samples from breast cancer patients, colorectal cancer patients, and healthy controls were analyzed through the three spectroscopic modalities. After modality-specific preprocessing, low-level data fusion (LLDF) was employed to integrate the complementary biochemical information encoded within the different spectroscopic measurements, and classification was performed using XGBoost models. Seven experimental configurations were evaluated, including the three unimodal approaches, all pairwise bimodal configurations, and the full multimodal approach of FTIR, Raman, and EEM fluorescence. The results show that although several individual modalities achieved high discrimination performance, the multimodal fusion provided the most balanced overall results, reaching a ROC-AUC of 0.997 for breast cancer and 0.994 for colorectal cancer, together with highly balanced sensitivity and specificity values.

Alejandro Leonardo García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián et al.

In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions to evaluate how their behaviors influence the outcomes. However, the traditional implementation of ABM can be demanding and complex. Generative Agent-Based Modeling (GABM) offers a solution by enabling scholars to create simulations where AI-driven agents can generate complex behaviors based on underlying rules and interactions. This paper introduces a framework for designing reliable experiments using GABM, making sophisticated simulation techniques more accessible to researchers across various fields. We provide a step-by-step guide for selecting appropriate tools, designing the model, establishing experimentation protocols, and validating results.