{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nonlinear-network-based-quantitative-trait","title":"Nonlinear network-based quantitative trait prediction from transcriptomic data","arxiv_id":"1701.07899","date":"2017-01-26","proceeding":null,"authors":["Emilie Devijver","Mélina Gallopin","Emeline Perthame"],"abstract":"Quantitatively predicting phenotype variables by the expression changes in a\nset of candidate genes is of great interest in molecular biology but it is also\na challenging task for several reasons. First, the collected biological\nobservations might be heterogeneous and correspond to different biological\nmechanisms. Secondly, the gene expression variables used to predict the\nphenotype are potentially highly correlated since genes interact though unknown\nregulatory networks. In this paper, we present a novel approach designed to\npredict quantitative trait from transcriptomic data, taking into account the\nheterogeneity in biological samples and the hidden gene regulatory networks\nunderlying different biological mechanisms. The proposed model performs well on\nprediction but it is also fully parametric, which facilitates the downstream\nbiological interpretation. The model provides clusters of individuals based on\nthe relation between gene expression data and the phenotype, and also leads to\ninfer a gene regulatory network specific for each cluster of individuals. We\nperform numerical simulations to demonstrate that our model is competitive with\nother prediction models, and we demonstrate the predictive performance and the\ninterpretability of our model to predict alcohol sensitivity from\ntranscriptomic data on real data from Drosophila Melanogaster Genetic Reference\nPanel (DGRP).","url_abs":"http://arxiv.org/abs/1701.07899v5","url_pdf":"http://arxiv.org/pdf/1701.07899v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nonlinear-network-based-quantitative-trait","repo_url":"https://github.com/epertham/xLLiM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}