Description du projet
Le traitement automatique du langage naturel pour lire le génome humain
Les modèles non supervisés de traitement automatique du langage naturel (TALN) peuvent accomplir des progrès révolutionnaires en apprenant la structure du langage. Une compréhension plus approfondie des aspects linguistiques de notre génome demeure toutefois indispensable. Le projet GROVER, financé par l’UE, exploite les techniques de TALN pour analyser le génome humain, en le traitant comme une séquence de texte. Il emploie la tokenisation des paires d’octets pour créer un vocabulaire à partir des séquences d’ADN et examine les cartes d’attention pour discerner les relations de formation entre les différents «mots» au sein du génome. Le projet explore les règles du langage à l’aide de méthodes de linguistique de corpus. GROVER combine diverses techniques pour étudier la grammaire et la syntaxe du génome, accomplir des tâches de prédiction biologique avec des modèles finement ajustés et déployer des méthodes d’apprentissage interprétables. Il fait également appel à diverses stratégies d’atténuation des biais ethniques, déterminé à révolutionner l’analyse des données génomiques.
Objectif
Natural language processing (NLP) models trained on text without explicit supervision can have groundbreaking performance. They can develop a notion for grammar, syntax, and semantics, thus learning the structure of language. However, while we have defined the rules in our language, we only have a basic understanding about the linguistics of our genome. In this project, our goal is to treat the human genome as a sequence of text and apply NLP techniques to the human DNA sequence. We will establish byte-pair tokenization to generate vocabulary from DNA sequence and analyse attention maps to see the training relationship between different “words” of the genome. We will then further investigate the language rules using methods from corpus linguistics. Together, this will allow us to explore the grammar, syntax, and semantics hidden in the genome and capture their biological meaning. For proof-of-principle, we will perform several biological prediction tasks with fine-tuning models, built on top of the pretrained model. First, we will take popular genomic prediction tasks to benchmark our approach, such as predicting genome elements, transcription, and precision of genome editing. Then we will add some novel tasks around genome stability using available multi-omics data. Throughout the project we will implement techniques for interpretable learning and strategies to observe, control, and prevent ethnic biases in our approach.
We expect for large language models to change how we, as a scientific field, approach genomics data analysis and expect our models to establish how these techniques can be applied efficiently, transparently, and in a bias-reduced way. In addition to general understanding of genome biology, we plan to use our models in the future for technical improvements of data analysis, population genetics, and for translational uses with applications in cancer genomics and genome editing.
Champ scientifique
- humanitieslanguages and literaturelinguistics
- medical and health sciencesmedical biotechnologygenetic engineeringgene therapy
- natural sciencesbiological sciencesgeneticsDNA
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- natural sciencesbiological sciencesgeneticsgenomes
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
01069 Dresden
Allemagne