{"id":23385,"date":"2023-03-08T13:29:57","date_gmt":"2023-03-08T18:29:57","guid":{"rendered":"https:\/\/genomequebec.com\/?page_id=23385"},"modified":"2025-07-31T12:08:21","modified_gmt":"2025-07-31T16:08:21","slug":"artificial-intelligence","status":"publish","type":"page","link":"https:\/\/genomequebec.com\/en\/genomics\/our-sectors\/artificial-intelligence\/","title":{"rendered":"Artificial Intelligence"},"content":{"rendered":"\n

Genomics creates immense quantities of data that are processed with the help of bioinformatics, a discipline that uses and designs computational tools to extract, organize and analyze data from living organisms. In recent years, with the development of artificial intelligence, researchers now have access to even more powerful algorithms capable of processing data like never before.<\/p>\n\n\n\n

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In healthcare, applications powered by artificial intelligence will considerably accelerate the move towards precision medicine. These tools not only analyze patients’ genetic profiles, but also make links with their medical histories and environmental factors likely to influence health, such as pollution levels and lifestyle habits.<\/p>\n\n\n\n

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Artificial intelligence can also be used to identify new therapeutic molecules in human and animal health, develop predictive models for bio-food production or track the emergence of pathogenic microorganisms.<\/p>\n\n\n\n

Montreal boasts a powerful scientific network in artificial intelligence, with the world’s largest university community in deep learning. Two collaborative hubs support a strong and innovative ecosystem: Mila, the Institut qu\u00e9b\u00e9cois d’intelligence artificielle, and IVADO, which brings together the research community, organizations and institutions. Collaborative research projects are conducted between artificial intelligence specialists and genomics research teams.<\/p>\n\n\n\n

Genomics and Artificial Intelligence: Innovative Projects Supported by G\u00e9nome Qu\u00e9bec, IVADO, and Oncopole<\/strong><\/h5>\n\n\n\n

Since 2020, the Omics Data Against Cancer (ODAC)<\/em> competition\u2014led by G\u00e9nome Qu\u00e9bec, IVADO, and Oncopole\u2014has been funding projects that push the boundaries of research in genomics and artificial intelligence (AI) to fight cancer. Here are four standout initiatives led by Quebec-based researchers.<\/p>\n\n\n\n

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1. The 3D Genome: A New Frontier in the Fight Against Cancer<\/a><\/strong>
By Nathalie Simon-Clerc, May 7, 2025<\/em>
Professor Mathieu Blanchette explores how the 3D structure of DNA inside cells influences gene expression and the development of cancers, particularly pediatric glioblastoma. Using artificial intelligence, his team models how mutations impact this complex folding.<\/p>\n\n\n\n

\u201cIf the genetic program isn’t folded correctly, some cellular functions may fail.\u201d<\/p>\n\n\n\n

In collaboration with pediatric oncology researcher Nada Jabado, this project aims to decode DNA\u2019s three-dimensional interactions to improve diagnostics and understanding of cancer\u2019s epigenetic mechanisms.<\/p>\n\n\n\n

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2. AI and Cancer: Developing Algorithms to Predict Drug Effectiveness<\/a><\/strong>
By Chlo\u00e9-Anne Touma, May 7, 2025<\/em>
Professor S\u00e9bastien Lemieux tackles the burden of traditional pharmaceutical testing by leveraging AI to predict the therapeutic activity of compounds based on a small sample set\u2014reducing the need for large, costly trials.<\/p>\n\n\n\n

\u201cCan we test 2,000 compounds, then predict the activity of 2 million?\u201d<\/p>\n\n\n\n

This robust and automatable system promises to streamline cancer drug development by identifying promising candidates more quickly.<\/p>\n\n\n\n

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3. Decoding the Epigenome with AI: Toward Precision Medicine for Cancer<\/a><\/strong>
By Sacha Isra\u00ebl, May 7, 2025<\/em>
Professors Jacques Drouin and Marc Bellemare combine molecular biology and AI to analyze epigenetic signatures\u2014chemical markers that regulate gene activation. Their project seeks to identify new, unknown markers and understand how these changes contribute to cancer development.<\/p>\n\n\n\n

\u201cBy developing epigenetic signatures for a cancer, we can better understand its dysfunctions and tailor treatments accordingly.\u201d<\/p>\n\n\n\n

This innovative approach paves the way for earlier diagnoses and personalized therapies.<\/p>\n\n\n\n

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4. When AI Dives into Cancer\u2019s Secrets<\/a><\/strong>
By Chlo\u00e9-Anne Touma, May 7, 2025<\/em>
Professor Amin Emad and Morag Park use AI models to anticipate how cancer cells will respond to treatments by identifying key genes behind drug resistance. Their work could guide the development of new personalized therapies.<\/p>\n\n\n\n

\u201cWe\u2019ve developed models that predict how cancer cells respond to drugs and identify potential therapeutic targets.\u201d<\/p>\n\n\n\n

Although data remains limited, this fundamental research lays the groundwork for more targeted and effective medicine.<\/p>\n\n\n\n

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A Promising Synergy for the Medicine of Tomorrow<\/strong>
These projects highlight the transformative impact of combining genomics and artificial intelligence. Through the ODAC competition, G\u00e9nome Qu\u00e9bec, IVADO, and Oncopole are driving scientific breakthroughs that could redefine how cancer is diagnosed and treated\u2014through precision, speed, and ethical integrity.<\/p>\n","protected":false},"excerpt":{"rendered":"

Genomics creates immense quantities of data that are processed with the help of bioinformatics, a discipline that uses and designs computational tools to extract, organize and analyze data from living…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":23285,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"16803,16784,16827,16834,16837,16823","_relevanssi_noindex_reason":"","footnotes":""},"class_list":["post-23385","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/pages\/23385","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/comments?post=23385"}],"version-history":[{"count":8,"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/pages\/23385\/revisions"}],"predecessor-version":[{"id":32134,"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/pages\/23385\/revisions\/32134"}],"up":[{"embeddable":true,"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/pages\/23285"}],"wp:attachment":[{"href":"https:\/\/genomequebec.com\/en\/wp-json\/wp\/v2\/media?parent=23385"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}