Una Nueva Metodología para la identificación de Patrones de Biomarcación aplicados al Estudio, Prevención y Tratamiento Temprano de Enfermedades Crónicas
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Resumen
Resumen: Hoy en día, la aplicación de métodos matemáticos y computacionales han permitido revolucionar las investigaciones relacionadas con la medicina, quimiometria, proteómica y genomica. Algoritmos matemáticos de minería de datos y aprendizaje de maquina están abriendo las pruebas a nuevas esperanzas en la lucha contra las enfermedades crónicas como el cáncer, la diabetes, el alzheimer, cirrosis, enfermedades cardiovasculares y enfermedades suprarrenales. El presente trabajo presenta una nuevo método de selección y validación de patrones de biomarcadores aplicados al tratamiento temprano de enfermedades crónicas. La metodología propuesta en este trabajo aborda el análisis de datos procedentes directamente de los instrumentos de medición, hasta la definición de los patrones de biomarcación. Ésta basa su funcionamiento en la combinación de t-test y Mann-Whitney U test en un filtro estadístico, el cual define zonas de interés en los espectros de mediciones, luego estas zonas de interés son agrupadas y reducidas a las características cercanas a la media de cada una de estas, eliminando así la información redundante. La contribución de este trabajo radica en la estructura de este filtro estadístico, el cual posee una enorme capacidad de extracción de información a través de cálculos sencillos, en comparación a complejos algoritmos presentados en trabajos similares. Finalmente las características seleccionadas por este filtro son validadas usando clasificadores de Adaboost, TotalBoost y LPBoost probados con validación cruzada y pruebas con muestras externas. Los resultados obtenidos reflejan un rendimiento superior al 95%, además gran robustez en contra del sobreentrenamiento( Overfitting) e infraentrenamiento(Underfitting).
Abstract: Today, the application of mathematical and computational methods have revolutionized research related to medicine, chemometrics, proteomics and genomics. Mathematical algorithms for data mining and machine learning are opening the doors to new hopes in the fight against chronic diseases like cancer, diabetes, Alzheimer’s disease, cirrhosis, cardiovascular disease and adrenal diseases. This paper presents a new method for selection and validation of biomarker patterns applied to early treatment of chronic diseases. The proposed methodology deals with the analysis of data obtained directly from measuring instruments to defining patterns. It is based on the combination of t - test and Mann - Whitney U test in a statistical filter. These tests define areas of interest in the spectra of measurements, and then these areas of interest are grouped and reduced the closest feature to the average of each group of these features, thereby eliminating redundant information. The contribution of this work lies in the structure of the statistical filter, which has an enormous capacity for extracting information through simple calculations compared to complex algorithms presented in similar articles. Finally, the features selected by this methodology are validated using Adaboost, TotalBoost and LPTBoost classifiers tested using cross-validation and testing with external samples. The obtained results reflect an efficiency greater than 95%, furthermore robustness against overfitting and underfitting.
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