Disponible en Dadun la tesis «From transcriptomics to proteomics: Unraveling biological knowledge via Machine Learning», de Guillermo Serrano Sanz.

Resumen: We start by highlighting basic concepts of both molecular biology and machine learning. This overview focuses on the key ideas that are required to comprehend the rest of the work, and thus, it does not attempt at providing a comprehensive review. We start with the basis of DNA and RNA, the genetic building bricks, until the formation of the proteins, the final actors of the genetic machinery. We also explore state-of-the-art technologies to measure those processes along with their limitations. After introducing the basic biological concepts, we will discuss the basics of machine learning methodologies and some of the most important models used in recent years to solve many biological problems.

La tesis fue defendida en diciembre de 2022 y fue dirigida por Mikel Hernaez y Elizabeth Guruceaga, del Departamento de Biología Computacional.