{"id":2238,"date":"2026-01-05T11:46:14","date_gmt":"2026-01-05T16:46:14","guid":{"rendered":"https:\/\/wp.well-e.org\/publication\/methodes-dapprentissage-machine-pour-lestimation-de-leffet-de-la-genetique-sur-la-production-laitiere\/"},"modified":"2026-05-02T11:41:16","modified_gmt":"2026-05-02T15:41:16","slug":"methodes-dapprentissage-machine-pour-lestimation-de-leffet-de-la-genetique-sur-la-production-laitiere","status":"publish","type":"publication","link":"https:\/\/wp.well-e.org\/en\/publication\/methodes-dapprentissage-machine-pour-lestimation-de-leffet-de-la-genetique-sur-la-production-laitiere\/","title":{"rendered":"M\u00e9thodes d\u2019apprentissage machine pour l\u2019estimation de l\u2019effet de la g\u00e9n\u00e9tique sur la production laiti\u00e8re"},"content":{"rendered":"<p><strong>Authors:<\/strong> Awa Samak\u00e9<\/p>\n<p><strong>Date:<\/strong> 2025-01-01<\/p>\n<p><strong>Status:<\/strong> Published<\/p>\n<hr>\n<p>La g\u00e9n\u00e9tique animale joue un r\u00f4le central dans l&#039;am\u00e9lioration des performances des troupeaux laitiers en permettant la s\u00e9lection des reproducteurs les plus performants ainsi que la pr\u00e9vision des maladies.<br \/>\nDepuis plusieurs d\u00e9cennies, l&#039;\u00e9valuation g\u00e9n\u00e9tique attire de grands int\u00e9r\u00eats pour la communaut\u00e9 en recherche et en industrie.<br \/>\nL\u2019\u00e9valuation g\u00e9n\u00e9tique vise \u00e0 estimer les valeurs g\u00e9n\u00e9tiques (EBV, GEBV) des animaux \u00e0 partir de donn\u00e9es de production, de p\u00e9digr\u00e9e ou de s\u00e9quen\u00e7age g\u00e9nomique, afin de pr\u00e9dire les performances futures et d\u2019optimiser les d\u00e9cisions de s\u00e9lection.<br \/>\nL&#039;\u00e9valuation g\u00e9n\u00e9tique passe par une estimation du patrimoine g\u00e9n\u00e9tique ainsi que son effet sur les g\u00e9n\u00e9rations futures. L&#039;analyse de cet effet peut se traduire de diff\u00e9rentes mani\u00e8res d\u00e9pendamment du secteur, par exemple, la pr\u00e9disposition \u00e0 certaines maladies en sant\u00e9, la production en industrie animali\u00e8re, etc.<br \/>\nCependant, la complexit\u00e9 des donn\u00e9es (grande dimension des SNPs, structures de p\u00e9digr\u00e9e profondes, s\u00e9ries temporelles de production) et la variabilit\u00e9 environnementale constituent des d\u00e9fis majeurs pour les m\u00e9thodes classiques telles que le BLUP.<br \/>\nNous r\u00e9pondrons aux questions suivantes: comment estimer l&#039;effet de la g\u00e9n\u00e9tique sur le profit dans la production laiti\u00e8re \u00e0 partir des donn\u00e9es de s\u00e9quen\u00e7ages g\u00e9nomiques?; comment estimer les futures productions laiti\u00e8res en int\u00e9grant les caract\u00e8res g\u00e9n\u00e9tiques sur le moyen\/long terme?<br \/>\nCette th\u00e8se propose plusieurs contributions m\u00e9thodologiques reposant sur l\u2019apprentissage automatique et profond pour am\u00e9liorer l\u2019estimation et la pr\u00e9vision des performances laiti\u00e8res.<br \/>\nNous utilisons deux paradigmes d&#039;apprentissage: la notion d&#039;informations privil\u00e9gi\u00e9es et celle de variables exog\u00e8nes. \u00c0 savoir qu&#039;\u00e0 notre connaissance, la notion d&#039;informations privil\u00e9gi\u00e9es n&#039;est pas encore appliqu\u00e9e dans le domaine de l&#039;agriculture, plus pr\u00e9cis\u00e9ment la production laiti\u00e8re.<br \/>\nLSTMDropout, un mod\u00e8le r\u00e9current int\u00e9grant un m\u00e9canisme de dropout h\u00e9t\u00e9rosc\u00e9dastique et l\u2019information privil\u00e9gi\u00e9e, permettant de pr\u00e9dire les EBV, GEBV, ainsi que les composantes et la valeur \u00e9conomique du lait, avec une robustesse accrue face aux donn\u00e9es bruit\u00e9es.<br \/>\nAgriGen, un mod\u00e8le de r\u00e9f\u00e9rence combinant donn\u00e9es g\u00e9n\u00e9tiques, ph\u00e9notypiques et environnementales pour l\u2019\u00e9valuation g\u00e9n\u00e9tique multi-traits et la prise de d\u00e9cision.<br \/>\nDairyLuPTS, une architecture optimis\u00e9e pour la mod\u00e9lisation de s\u00e9ries temporelles de production laiti\u00e8re. %, adapt\u00e9e \u00e0 des sc\u00e9narios sans traits de conformation; DairyBLUP mod\u00e8le mixte bas\u00e9 sur le pedigree. %servant de benchmark pour quantifier les gains apport\u00e9s par les approches neuronales;<br \/>\nInformation Dropout Adapt\u00e9e (IDA), une \u00e9n\u00e9ralisation de l\u2019Information Dropout, qui int\u00e8gre des informations privil\u00e9gi\u00e9es pour r\u00e9gulariser les repr\u00e9sentations latentes et am\u00e9liorer les performances de pr\u00e9vision.<br \/>\nLes r\u00e9sultats exp\u00e9rimentaux montrent que l\u2019int\u00e9gration d\u2019informations privil\u00e9gi\u00e9es et de donn\u00e9es multi-sources permet d\u2019am\u00e9liorer significativement la pr\u00e9cision des pr\u00e9dictions, tout en r\u00e9duisant le co\u00fbt computationnel par rapport aux m\u00e9thodes classiques. LSTMDropout et DairyLuPTS se distinguent en particulier par leurs performances en pr\u00e9vision multi-sorties et leur capacit\u00e9 \u00e0 capturer les relations complexes entre les traits g\u00e9n\u00e9tiques et les rendements laitiers.<br \/>\nLes perspectives de recherche incluent l\u2019extension de LSTMDropout \u00e0 d\u2019autres types de s\u00e9ries temporelles, l\u2019\u00e9tude des interactions textbackslashtextitg\u00e9notype $textbackslashtimes$ environnement, ainsi que l\u2019analyse de l\u2019effet des variants et des loci sur la sant\u00e9 et la productivit\u00e9.<br \/>\nL\u2019exploration de nouvelles strat\u00e9gies d\u2019augmentation de donn\u00e9es g\u00e9nomiques et de mod\u00e9lisation math\u00e9matique des informations privil\u00e9gi\u00e9es constitue \u00e9galement un axe prometteur pour renforcer la robustesse des mod\u00e8les et optimiser la s\u00e9lection g\u00e9n\u00e9tique<\/p>","protected":false},"template":"","meta":{"_acf_changed":false,"_crdt_document":""},"class_list":["post-2238","publication","type-publication","status-publish","hentry"],"blocksy_meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>M\u00e9thodes d\u2019apprentissage machine pour l\u2019estimation de l\u2019effet de la g\u00e9n\u00e9tique sur la production laiti\u00e8re - WELL-E<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wp.well-e.org\/en\/publication\/methodes-dapprentissage-machine-pour-lestimation-de-leffet-de-la-genetique-sur-la-production-laitiere\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"M\u00e9thodes d\u2019apprentissage machine pour l\u2019estimation de l\u2019effet de la g\u00e9n\u00e9tique sur la production laiti\u00e8re - WELL-E\" \/>\n<meta property=\"og:description\" content=\"Authors: Awa Samak\u00e9 Date: 2025-01-01 Status: Published La g\u00e9n\u00e9tique animale joue un r\u00f4le central dans l&#039;am\u00e9lioration des performances des troupeaux laitiers en permettant la s\u00e9lection des reproducteurs les plus performants ainsi que la pr\u00e9vision des maladies. 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