From transcriptomics to proteomics: Unraveling biological knowledge via Machine Learning

  1. Serrano-Sanz, Guillermo
Dirigée par:
  1. Mikel Hernáez Directeur
  2. Elizabeth Guruceaga Directrice

Université de défendre: Universidad de Navarra

Fecha de defensa: 20 décembre 2022

Jury:
  1. Antonio Pineda Lucena President
  2. Silvestre Vicent Cambra Secrétaire
  3. Enrique Santamaría Martínez Rapporteur
  4. Ian Michael Traniello Rapporteur
  5. David Gómez Cabrero Rapporteur

Type: Thèses

Teseo: 783274 DIALNET lock_openDadun editor

Résumé

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.