{"id":75447,"date":"2025-07-22T11:02:06","date_gmt":"2025-07-22T09:02:06","guid":{"rendered":"https:\/\/www.embl.org\/news\/?p=75447"},"modified":"2025-09-05T11:32:18","modified_gmt":"2025-09-05T09:32:18","slug":"corneto-machine-learning-to-decode-complex-omics-data","status":"publish","type":"post","link":"https:\/\/www.embl.org\/news\/science\/corneto-machine-learning-to-decode-complex-omics-data\/","title":{"rendered":"CORNETO: machine learning to decode complex omics data"},"content":{"rendered":"\n<article class=\"vf-card vf-card--brand vf-card--bordered vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Summary    <\/h3>\n                <p class=\"vf-card__text\"><ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">CORNETO is a new computational tool that helps researchers combine different types of biological data with prior biological knowledge to map how molecules like genes and proteins interact inside cells.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">By analysing different samples together at once, CORNETO shows which biological processes are common and which are unique across cell types and conditions.\u00a0<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Researchers have used CORNETO to reveal shared and cell-specific pathways in disease research, e.g. to identify signalling pathways associated with chemotherapy resistance in ovarian cancer patients.<\/span><\/li>\r\n<\/ul><\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<p>EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses machine learning to gain meaningful insights from complex biological data. CORNETO enables users to extract molecular networks \u2013 maps of how genes, proteins, and signalling pathways interact \u2013 by combining experimental data from different samples and conditions with prior biological knowledge, such as signalling or metabolic networks. This can help us to better understand the mechanisms that lead a cell to be healthy or diseased.<\/p>\n\n\n\n<p>Understanding how molecules interact inside our cells is key to uncovering the mechanisms that can go wrong, leading to disease. But as the types of omics data available to researchers grow in size and complexity, researchers often struggle to extract useful, meaningful patterns from them. CORNETO, which stands for Constrained Optimisation for the Recovery of NETworks from Omics, combines machine learning techniques with biological prior knowledge to simultaneously analyse multiple types of omics data, including transcriptomics, proteomics, and metabolomics.<\/p>\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      What do we mean by omics?    <\/h3>\n                <p class=\"vf-card__text\"><span style=\"font-weight: 400;\">Omics refers to the large-scale study of biological molecules and their functions within a living system, using high-throughput technologies to analyse complex datasets. This includes fields like genomics, transcriptomics, proteomics, and metabolomics.<\/span><\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<p>\u201cWe wanted to solve a common challenge in systems biology: how to make sense of omics data when you have so much complex data available all at once,\u201d said Julio Saez-Rodriguez, Head of Research at EMBL-EBI and Professor on leave at Heidelberg University. \u201cCORNETO helps by combining these complex data with prior information coming from biological databases to find patterns that are consistent, interpretable, and biologically meaningful.\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Unified omics analyses<\/h2>\n\n\n\n<p>Traditionally, scientists analyse data from one condition at a time \u2013 for example, comparing healthy cells to diseased ones \u2013 and build separate interaction networks for each. But this approach can miss the bigger picture. CORNETO uses machine learning to analyse multiple samples or conditions together, highlighting biological processes that are shared across datasets, and pinpointing the differences between samples. CORNETO is also designed to allow researchers to customise it for specific use cases or extend it to new data types as needed.<\/p>\n\n\n\n<p>\u201cUsing CORNETO is like finding the common threads in a tangled web,\u201d explained Pablo Rodr\u00edguez-Mier, postdoctoral researcher at Heidelberg University. \u201cIt helps researchers pull out the key biological processes that are happening across many samples and understand what\u2019s different or the same in each one.\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Real-world applications<\/h2>\n\n\n\n<p>Using CORNETO is especially valuable to researchers working in fields like cancer research, where there are similarities across patients, but no two patients are exactly alike. To demonstrate this, the researchers used CORNETO to analyse gene expression data from multiple cancer patients to discover which specific intracellular signalling pathways were behaving abnormally.&nbsp;<\/p>\n\n\n\n<p>Using only transcriptomics data, CORNETO identified key deregulated kinases, enzymes that regulate cell signalling, which were also detected independently using phosphoproteomics. The resulting networks revealed both shared pathways and patient-specific differences, a step toward the kinds of insights that could one day support personalised treatment strategies.<\/p>\n\n\n\n<p>CORNETO is also currently being used in the EU research project <a href=\"https:\/\/www.deciderproject.eu\">DECIDER<\/a> to identify deregulated signalling pathways associated with chemotherapy resistance in ovarian cancer patients.<\/p>\n\n\n\n<p>The researchers also used CORNETO to analyse metabolic pathways in yeast strains in which different genes were inactivated. Here, CORNETO was able to find the key processes the yeast cells were using to survive and grow. Understanding these essential processes could help scientists design better yeast strains for making biofuels and other products for industrial manufacturing.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Open-source and ready to use<\/h2>\n\n\n\n<p>CORNETO is available as <a href=\"http:\/\/github.com\/saezlab\/corneto\">open-source software on GitHub<\/a>. Here, you can also find tutorials, example datasets, and modular code to adapt CORNETO to your needs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Funding<\/strong><\/h3>\n\n\n\n<p>This work was funded by the European Union\u2019s Horizon 2020 Programme under the grant agreements No 951773 (<a href=\"https:\/\/permedcoe.eu\/\">PerMedCoE<\/a>) and No 965193 (<a href=\"https:\/\/www.deciderproject.eu\/\">DECIDER<\/a>).&nbsp;<\/p>\n\n\n\n<hr class=\"vf-divider\"\/>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"Spanish\"><strong>CORNETO: aprendizaje autom\u00e1tico para descifrar datos \u00f3micos complejos<\/strong><\/h1>\n\n\n\n<p><strong>CORNETO: aprendizaje autom\u00e1tico para descifrar datos \u00f3micos complejos<\/strong><\/p>\n\n\n\n<p><em>Una nueva herramienta combina conocimiento biol\u00f3gico con aprendizaje autom\u00e1tico para ayudar a los investigadores a extraer informaci\u00f3n significativa de datos \u00f3micos complejos<\/em><\/p>\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--bordered vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Resumen    <\/h3>\n                <p class=\"vf-card__text\"><!-- wp:list -->\r\n<ul class=\"wp-block-list\">\r\n \t<li style=\"list-style-type: none;\">\r\n<ul class=\"wp-block-list\"><!-- wp:list-item -->\r\n \t<li>CORNETO es una nueva herramienta computacional que permite a los investigadores combinar distintos tipos de datos biol\u00f3gicos con datos y conocimiento biol\u00f3gico previo para mapear c\u00f3mo interact\u00faan mol\u00e9culas como genes y prote\u00ednas dentro de las c\u00e9lulas.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<!-- \/wp:list-item --> <!-- wp:list-item -->\r\n<ul class=\"wp-block-list\">\r\n \t<li style=\"list-style-type: none;\">\r\n<ul class=\"wp-block-list\">\r\n \t<li>Al analizar m\u00faltiples muestras de forma conjunta, CORNETO revela qu\u00e9 procesos biol\u00f3gicos son comunes y cu\u00e1les son espec\u00edficos seg\u00fan el tipo celular o las condiciones experimentales.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<!-- \/wp:list-item --> <!-- wp:list-item -->\r\n<ul class=\"wp-block-list\">\r\n \t<li style=\"list-style-type: none;\">\r\n<ul class=\"wp-block-list\">\r\n \t<li>Los investigadores han utilizado CORNETO para identificar rutas compartidas y espec\u00edficas en estudios sobre enfermedades, por ejemplo, para detectar v\u00edas de se\u00f1alizaci\u00f3n asociadas con la resistencia a la quimioterapia en pacientes con c\u00e1ncer de ovario.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<!-- \/wp:list-item --><\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<p>Cient\u00edficos del EMBL-EBI, en colaboraci\u00f3n con la Universidad de Heidelberg, han desarrollado CORNETO, una nueva herramienta computacional que emplea t\u00e9cnicas de aprendizaje autom\u00e1tico para extraer informaci\u00f3n significativa de datos biol\u00f3gicos complejos. CORNETO permite a los usuarios reconstruir redes moleculares, que son mapas de interacciones entre genes, prote\u00ednas y v\u00edas de se\u00f1alizaci\u00f3n, combinando datos experimentales de distintas muestras y condiciones con datos biol\u00f3gicos ya conocidos, como redes de se\u00f1alizaci\u00f3n o metabolismo. Esto facilita la comprensi\u00f3n de los mecanismos que determinan si una c\u00e9lula est\u00e1 sana o enferma.<\/p>\n\n\n\n<p>Comprender c\u00f3mo interact\u00faan las mol\u00e9culas dentro de nuestras c\u00e9lulas es fundamental para identificar los mecanismos que pueden fallar y conducir a enfermedades. Sin embargo, a medida que aumentan el volumen y la complejidad de los datos \u00f3micos disponibles, los investigadores enfrentan dificultades para extraer patrones \u00fatiles y con sentido biol\u00f3gico. CORNETO&nbsp; &#8211; acr\u00f3nimo de <em>Constrained Optimisation for the Recovery of NETworks from Omics<\/em> &#8211;&nbsp; combina t\u00e9cnicas de aprendizaje autom\u00e1tico y conocimiento biol\u00f3gico previo para analizar simult\u00e1neamente m\u00faltiples tipos de datos \u00f3micos, como transcript\u00f3mica, prote\u00f3mica y metabol\u00f3mica.<\/p>\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      \u00bfQu\u00e9 entendemos por datos \u00f3micos?    <\/h3>\n                <p class=\"vf-card__text\">El t\u00e9rmino \u201c\u00f3micas\u201d hace referencia al estudio en profundidad de las mol\u00e9culas biol\u00f3gicas y sus funciones en un sistema vivo, utilizando tecnolog\u00edas de alto rendimiento para analizar conjuntos de datos complejos. Esto incluye disciplinas como la gen\u00f3mica, transcript\u00f3mica, prote\u00f3mica y metabol\u00f3mica.<\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<p>\u201cQuer\u00edamos resolver un desaf\u00edo com\u00fan en la biolog\u00eda de sistemas: c\u00f3mo interpretar los datos \u00f3micos cuando se dispone de tantos datos complejos al mismo tiempo\u201d, explica Julio Saez-Rodriguez, Director de Investigaci\u00f3n en EMBL-EBI y profesor en excedencia en la Universidad de Heidelberg. \u201cCORNETO ayuda combinando estos datos complejos con informaci\u00f3n previa proveniente de bases de datos biol\u00f3gicas, para identificar patrones que sean consistentes, interpretables y con relevancia biol\u00f3gica\u201d.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>An\u00e1lisis \u00f3mico unificado<\/strong><\/h2>\n\n\n\n<p>Tradicionalmente, los cient\u00edficos analizan los datos de un tipo a la vez &#8211; por ejemplo, comparando c\u00e9lulas sanas con c\u00e9lulas enfermas &#8211; y construyen redes de interacci\u00f3n separadas para cada caso. Sin embargo, este enfoque puede pasar por alto la visi\u00f3n global. CORNETO utiliza aprendizaje autom\u00e1tico para analizar m\u00faltiples muestras o condiciones de forma conjunta, destacando los procesos biol\u00f3gicos compartidos entre conjuntos de datos y se\u00f1alando las diferencias espec\u00edficas. Adem\u00e1s, est\u00e1 dise\u00f1ado para ser personalizable y adaptable a casos de uso espec\u00edficos o a nuevos tipos de datos.<\/p>\n\n\n\n<p>\u201cUtilizar CORNETO es como encontrar los hilos comunes en una red enmara\u00f1ada\u201d, afirma Pablo Rodr\u00edguez-Mier, investigador postdoctoral en la Universidad de Heidelberg. \u201cAyuda a los investigadores a identificar los procesos biol\u00f3gicos clave que ocurren en muchas muestras y a entender qu\u00e9 es igual o distinto en cada una\u201d.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Aplicaciones reales<\/strong><\/h2>\n\n\n\n<p>CORNETO resulta especialmente \u00fatil en campos como la investigaci\u00f3n oncol\u00f3gica, donde existen similitudes entre pacientes, pero ning\u00fan paciente es exactamente igual a otro. Para demostrar su utilidad, los investigadores utilizaron CORNETO para analizar datos de expresi\u00f3n g\u00e9nica de m\u00faltiples pacientes con c\u00e1ncer, con el fin de descubrir qu\u00e9 v\u00edas de se\u00f1alizaci\u00f3n intracelular estaban funcionando de manera an\u00f3mala.<\/p>\n\n\n\n<p>Utilizando \u00fanicamente datos transcript\u00f3micos, CORNETO identific\u00f3 quinasas clave desreguladas &#8211; enzimas que regulan la se\u00f1alizaci\u00f3n celular &#8211; que tambi\u00e9n fueron detectadas de manera independiente mediante fosfoprote\u00f3mica. Las redes resultantes revelaron tanto rutas compartidas como diferencias espec\u00edficas de cada paciente, un avance hacia el tipo de conocimientos que podr\u00edan respaldar tratamientos personalizados en el futuro.<\/p>\n\n\n\n<p>Actualmente, CORNETO tambi\u00e9n se est\u00e1 utilizando en el proyecto europeo<a href=\"https:\/\/www.deciderproject.eu\"> DECIDER<\/a> para identificar v\u00edas de se\u00f1alizaci\u00f3n desreguladas asociadas con la resistencia a la quimioterapia en pacientes con c\u00e1ncer de ovario.<\/p>\n\n\n\n<p>Los investigadores tambi\u00e9n aplicaron CORNETO al an\u00e1lisis de rutas metab\u00f3licas en cepas de levadura con diferentes genes inactivados. En este caso, CORNETO fue capaz de identificar los procesos clave que las c\u00e9lulas de levadura utilizaban para sobrevivir y crecer. Comprender estos procesos esenciales podr\u00eda ayudar a dise\u00f1ar cepas de levadura m\u00e1s eficientes para la producci\u00f3n de biocombustibles y otros productos en aplicaciones industriales.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>C\u00f3digo abierto y listo para usar<\/strong><\/h2>\n\n\n\n<p>CORNETO est\u00e1 disponible como<a href=\"http:\/\/github.com\/saezlab\/corneto\"> software de c\u00f3digo abierto en GitHub<\/a>. All\u00ed tambi\u00e9n se pueden encontrar tutoriales, conjuntos de datos de ejemplo y un c\u00f3digo modular que permite adaptar la herramienta a distintas necesidades.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">F<strong>inanciaci\u00f3n<\/strong><\/h3>\n\n\n\n<p>Este trabajo ha sido financiado por el programa Horizon 2020 de la Uni\u00f3n Europea bajo el acuerdo de beca No 951773 (<a href=\"https:\/\/permedcoe.eu\/\">PerMedCoE<\/a>) y No 965193 (<a href=\"https:\/\/www.deciderproject.eu\/\">DECIDER<\/a>).&nbsp;<\/p>\n\n\n\n\n","protected":false},"excerpt":{"rendered":"<p>New tool combines biological knowledge with machine learning to help researchers extract meaningful insights from complex omics data.<\/p>\n","protected":false},"author":77,"featured_media":75451,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[11060,2,17591],"tags":[4718,28,36,604,13934,1748,539],"embl_taxonomy":[2906,18995],"class_list":["post-75447","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-highlights","category-science","category-science-technology","tag-artificial-intelligence","tag-bioinformatics","tag-embl-ebi","tag-machine-learning","tag-omics","tag-press-release","tag-research-highlight","embl_taxonomy-embl-ebi","embl_taxonomy-julio-saez-rodriguez"],"acf":{"vfwp-news_embl_taxonomy":[2906,18995],"featured":true,"show_featured_image":false,"field_target_display":"both","field_article_language":{"value":"english","label":"English"},"article_intro":"<p>New tool combines biological knowledge with machine learning to help researchers extract meaningful insights from complex omics data<\/p>\n","related_links":false,"source_article":[{"publication_title":"Unifying multi-sample network inference from prior knowledge and omics data with CORNETO","publication_link":{"title":"","url":"https:\/\/www.nature.com\/articles\/s42256-025-01069-9","target":""},"publication_authors":"Rodr\u00edguez-Mier P., et al.","publication_source":"Nature Machine Intelligence","publication_date":"22 July 2025","publication_doi":"10.1038\/s42256-025-01069-9"}],"in_this_article":false,"press_contact":"EMBL-EBI Generic","article_translations":[{"translation_language":"Espa\u00f1ol","translation_anchor":"#Spanish"}],"languages":""},"embl_taxonomy_terms":[{"uuid":"a:3:{i:0;s:36:\"b14d3f13-5670-44fb-8970-e54dfd9c921a\";i:1;s:36:\"89e00fee-87f4-482e-a801-4c3548bb6a58\";i:2;s:36:\"a99d1a7c-ca83-4c00-ab61-d082d3e41ce3\";}","parents":[],"name":["EMBL-EBI"],"slug":"embl-ebi","description":"Where &gt; All EMBL sites &gt; EMBL-EBI"},{"uuid":"a:2:{i:0;s:36:\"4428d1fd-441a-4d6d-a1c5-5dcf5665f213\";i:1;s:36:\"440bf893-d142-4dc9-8114-0050fd8de689\";}","parents":[],"name":["Julio Saez Rodriguez"],"slug":"julio-saez-rodriguez","description":"Who &gt; Julio Saez Rodriguez"}],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>CORNETO: machine learning to decode complex omics data | EMBL<\/title>\n<meta name=\"description\" content=\"New tool combines biological knowledge with machine learning to help researchers extract meaningful insights from complex omics data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.embl.org\/news\/science\/corneto-machine-learning-to-decode-complex-omics-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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