{"version":1,"type":"story","url":"https://digestai.news/story/mit-researchers-use-iris-model-to-decode-cell-signaling-pathways","json":"https://digestai.news/story/mit-researchers-use-iris-model-to-decode-cell-signaling-pathways.json","markdown":"https://digestai.news/story/mit-researchers-use-iris-model-to-decode-cell-signaling-pathways.md","slug":"mit-researchers-use-iris-model-to-decode-cell-signaling-pathways","headline":"MIT researchers use IRIS model to decode cell signaling pathways","summary":"MIT researchers have developed a machine learning model called IRIS that identifies unique gene activity patterns, or \"fingerprints,\" left by specific signaling pathways in cells. Led by assistant professor Pulin Li and graduate student Nicholas Hutchins, the team discovered that these fingerprints remain consistent across different cell types. This finding allows scientists to reconstruct the signaling history of cells without needing to map each cell type individually, a process that was previously considered too arduous.\n\nThe model, published in Nature Methods on Sept. 8, uses transfer learning to recognize signaling patterns in cell types it has not previously encountered. Trained on data from human embryonic stem cells, IRIS successfully predicted signaling events in mouse embryos during gastrulation. The system accurately identified when pathways activated in cells destined to become heart, gut, muscle, and spinal cord tissue.\n\nIn one test, IRIS predicted a specific signaling pathway required for lung development, a prediction confirmed by experiments in mouse embryos. This capability could accelerate the creation of organoids and improve stem cell engineering for regenerative medicine. By understanding the precise signals that drive cell fate, researchers may better study diseases like asthma and lung cancer and design therapies to repair damaged tissue.","keyPoints":["IRIS is a machine learning model that detects consistent signaling pathway fingerprints across different cell types.","The model was trained on human embryonic stem cell data and tested on mouse embryos during gastrulation.","IRIS correctly predicted signaling pathways for lung development, a result confirmed by experimental validation."],"whyItMatters":"This AI tool accelerates the mapping of cell development, potentially speeding up the creation of organoids for drug testing and improving regenerative medicine treatments for diseases like lung cancer.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["MIT","Whitehead Institute for Biomedical Research"],"models":["IRIS"],"people":["Pulin Li","Nicholas Hutchins"]},"firstPublishedAt":"2026-10-15T20:50:00Z","updatedAt":"2026-10-15T20:50:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"MIT News on AI","title":"AI model decodes the language cells use to communicate","url":"https://news.mit.edu/2026/ai-model-decodes-language-cells-use-communicate-1015","publishedAt":"2026-10-15T20:50:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"MIT researchers use IRIS model to decode cell signaling pathways\", 15 October 2026, https://digestai.news/story/mit-researchers-use-iris-model-to-decode-cell-signaling-pathways","publisher":"Digest AI","title":"MIT researchers use IRIS model to decode cell signaling pathways","datePublished":"2026-10-15T20:50:00Z","url":"https://digestai.news/story/mit-researchers-use-iris-model-to-decode-cell-signaling-pathways"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}