BIFI-TALK: Xavier de la Cruz Monserrat. Vall d’Hebron Institute of Research, Barcelona<\/p>\n
Title<\/strong>:\u00a0Cracking the Hereditary Disease Code: Revisiting Predictive Properties with a Probabilistic Touch<\/p>\n Abstract<\/strong><\/p>\n As genomic sequencing becomes widespread in clinical settings, in silico pathogenicity predictions are increasingly vital for precision medicine. However, despite advances in machine learning-based classifiers, existing approaches are reaching a performance plateau while still facing interpretation challenges. In response, a paradigm shift has emerged in recent years, moving from binary classification toward modeling the quantitative impact of genetic variants. Friday, 14 February 2025, 12:30 h <\/p>\n","protected":false},"excerpt":{"rendered":" BIFI-TALK: Xavier de la Cruz Monserrat. Vall d’Hebron Institute of Research, Barcelona Title:\u00a0Cracking the Hereditary Disease Code: Revisiting Predictive Properties with a Probabilistic Touch Abstract As genomic sequencing becomes widespread […]<\/p>\n","protected":false},"author":8,"featured_media":0,"template":"","meta":{"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[33],"class_list":["post-8558","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-eventos","cat_eventos"],"_links":{"self":[{"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/tribe_events\/8558","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/users\/8"}],"version-history":[{"count":0,"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/tribe_events\/8558\/revisions"}],"wp:attachment":[{"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/media?parent=8558"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/tags?post=8558"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/bifi.es\/es\/wp-json\/wp\/v2\/tribe_events_cat?post=8558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nOur work in this area has shown good performance, as demonstrated by our contributions to the CAGI5 and CAGI6 challenges, yet there is still room for improvement. In this talk, I will focus on a key aspect of pathogenicity prediction: the extraction of new discriminant features from traditional predictive properties, using a simple probabilistic framework. In particular, I will share insights from our approach and present preliminary results that highlight its potential impact on predictive models.<\/p>\n
\nConference Room. Edificio I+D<\/p>\n