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7: Modelos ocultos de Markov I

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    Los modelos ocultos de Markov (HMM) son una herramienta fundamental del aprendizaje automático que es ampliamente utilizada en biología computacional. Usando HMM, podemos explorar la estructura subyacente de secuencias de ADN o polipéptidos, detectando regiones de especial interés. Por ejemplo, podemos identificar subsecuencias conservadas o descubrir regiones con diferentes distribuciones de nucleótidos o aminoácidos tales como regiones promotoras e islas CpG. Mediante este modelo probabilístico, podemos iluminar las propiedades y componentes estructurales de las secuencias y localizar genes y otros elementos funcionales.


    This page titled 7: Modelos ocultos de Markov I is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform.