{"id":1115,"date":"2023-07-26T10:40:41","date_gmt":"2023-07-26T13:40:41","guid":{"rendered":"https:\/\/datamining.dc.uba.ar\/datamining\/?p=1115"},"modified":"2023-07-27T16:45:17","modified_gmt":"2023-07-27T19:45:17","slug":"defensa-de-tesis-miguel-angel-barros-25-07-2023-15-hs","status":"publish","type":"post","link":"https:\/\/datamininguba.com.ar\/datamining\/2023\/07\/26\/defensa-de-tesis-miguel-angel-barros-25-07-2023-15-hs\/","title":{"rendered":"Defensa de Tesis \u2013 Miguel Angel Barros \u2013 25\/07\/2023 \u2013 15 hs."},"content":{"rendered":"<p>El Martes 25\/07 se realiz\u00f3 la Defensa de Tesis de Miguel Angel Barros titulada &#8220;Predicci\u00f3n de secuencias de inserci\u00f3n en genomas bacterianos utilizando algoritmos de Machine Learning&#8221;. Cont\u00f3 con la direcci\u00f3n del Dr. German M. Traglia y con la co-direcci\u00f3n del Dr. Andr\u00e9s Iriarte. Los jurados fueron la Dra. Sonia G\u00f3mez, Dr. Diego Faccone, Dr. Marcelo Soria. La defensa se realiz\u00f3 de manera online. La misma puede verse a trav\u00e9s del siguiente link: <a href=\"https:\/\/youtu.be\/-iNZRcABxQk?t=25\" target=\"_blank\" rel=\"noopener\">https:\/\/youtu.be\/-iNZRcABxQk?t=25<\/a><\/p>\n<p>Resumen:<\/p>\n<p><span id=\"more-1055\"><\/span>Las secuencias de inserci\u00f3n (IS) son elementos gen\u00e9ticos m\u00f3viles que tienen la capacidad de desplazarse desde una determinada regi\u00f3n del genoma hacia otra. Las IS son una fuente de variabilidad gen\u00e9tica que podr\u00edan brindar rasgos adaptativos a distintas especies bacterianas,<br \/>\npor ejemplo: resistencia antibi\u00f3tica. Sin embargo, la identificaci\u00f3n de las IS no es una tarea simple dado los rasgos gen\u00e9ticos variables entre los distintos tipos existentes.<\/p>\n<p>El objetivo del presente trabajo fue desarrollar un software basado en algoritmos de aprendizaje automatizado que permita identificar IS sobre diferentes especies de genomas bacterianos. Para lo cual, para entrenar los diferentes algoritmos de clasificaci\u00f3n, se trabaj\u00f3 con un dataset inicial<br \/>\ncompuesto por 8.223 de secuencias aminoac\u00eddicas de IS y 8.223 secuencias aminoac\u00eddicas pertenecientes a otro tipo de estructura proteica (las cuales se denominaron non-IS). Las primeras secuencias se obtuvieron de la bases de datos espec\u00edficas de IS, ISFinder, en tanto<br \/>\nque el resto de las secuencias fueron descargadas de la base PDB, Protein Data Bank.<\/p>\n<p>Los clasificadores evaluados fueron seis: Regresi\u00f3n Log\u00edstica, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Xtreme Gradient Boosting (XGBoost), Random Forest y Light Gradient Boosting Machine (LGBM). Para validar el rendimiento del modelo se incluyo una<br \/>\netapa adicional, validaci\u00f3n, en la cual se ejecutaron a los algoritmos sobre datos que a los que no fue expuesto con anterioridad. Estos datos correspondieron a cinco genomas bacterianos de referencia: Escherichia coli K-12, Salmonella enterica serovar Typhi CT18 , Acinetobacter baumannii AYE , Staphylococcus aureus Newman y Pseudomonas aeruginosa. El clasificador que mostr\u00f3 el mejor rendimiento fue XGBoost, el cual obtuvo valores de 93.9% en Sensitividad, 94.1% en Especificidad y 94% en Accuracy en la etapa de testing, demandando 15 5 segundos de tiempo de c\u00f3mputo en un ordenador port\u00e1til. El posterior an\u00e1lisis, mediante BLAST, sobre los falsos positivos producidos durante la clasificaci\u00f3n, demostraron que el modelo desarrollado fue capaz de identificar nuevas IS con un elevado nivel de precisi\u00f3n.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>El Martes 25\/07 se realiz\u00f3 la Defensa de Tesis de [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1118,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1115","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Defensa de Tesis \u2013 Miguel Angel Barros \u2013 25\/07\/2023 \u2013 15 hs. - Maestr\u00eda en Data Mining<\/title>\n<meta name=\"robots\" content=\"index, follow, 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