[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ background_color=”#072c72″ border_color_all=”#3255c9″ text_orientation=”right” background_layout=”dark” custom_padding=”20px|15px|15px|” z_index_tablet=”500″]<\/p>\n
2024 <\/em><\/p>\n Master’s Projects<\/p>\n @Mathematics\/Statistics<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n #Mendelian Randomization<\/p>\n #Deep Learning<\/p>\n #Double Machine Learning<\/p>\n #Genomics<\/p>\n #Pleiotropy<\/p>\n \u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n Mendelian Randomization is a method that infers the causality between risk factors and diseases using genetic variants as instrumental variables. However this method suffers from major biases such as pleiotropy where a single variant influences multiple traits. To address these limitations we propose a novel approach called Triple Machine Learning-MR which leverages the inherent capabilities of AI by utilizing extensive genome-wide and multi-omic data. This strategy aims to 1) predict causal effects 2) provide a robust estimator and 3) refine models by selecting the most relevant genetic variants. Ultimately this comprehensive approach will offer greater precision in understanding the causality between traits and will be used to uncover the complex relationship between the immune system and cancer.<\/p>\n <\/p>\n Professor (Junior Professor Chair) [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ admin_label=”Row” locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”35″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″ locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2024 Master’s Projects@Mathematics\/Statistics #Mendelian Randomization#Deep Learning#Double Machine Learning#Genomics#Pleiotropy\u00a0 Project Summary Mendelian Randomization is a method that infers the causality between risk factors and diseases using genetic variants as instrumental variables. However this method suffers from major biases such as pleiotropy where a single variant influences multiple traits. To address these limitations we propose a novel… Lire la suite Deep Mendelian Randomization: explaining causality between different hereditary traits at genome-wide scale<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[51,1,29,35],"tags":[],"class_list":["post-1534","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-51","category-diip","category-masters-internship","category-mathematics-statistics"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1534","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1534"}],"version-history":[{"count":4,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1534\/revisions"}],"predecessor-version":[{"id":2431,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1534\/revisions\/2431"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2263"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1534"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1534"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1534"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
Marie Verbanck
<\/strong>marie.verbanck@wordpress-test.app.u-pariscite.fr<\/h3>\n
– Inserm U900, Institut Curie, PSL Research University.
– BioSTM, University Paris Cit\u00e9.<\/p>\nProjects in the same discipline
<\/span><\/h2>\n