Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana
2024
Raimondi, Daniele | Passemiers, Antoine | Verplaetse, Nora | Corso, Massimiliano | Ferrero-Serrano, Ángel | Nazzicari, Nelson | Biscarini, Filippo | Fariselli, Piero | Moreau, Yves | Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven) | Institut Jean-Pierre Bourgin - Sciences du végétal (IJPB) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Pennsylvania State University (Penn State) ; Penn State System | Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA) | Consiglio Nazionale delle Ricerche [Milano] (CNR) | Università degli studi di Torino = University of Turin (UNITO) | Fonds Wetenschappelijk Onderzoek (FWO) senior post-doctoral fellowship (grant number 12Y5623N)
International audience
اظهر المزيد [+] اقل [-]إنجليزي. Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana , obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson’s Paradox, and (2) model generalization limitations.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique