biostudies-literatureUnknown111Wallis FSAChildren’s Cancer FoundationChildren Cancer Free FoundationAdessium Foundation<h4>Background</h4>With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools.<h4>Methods</h4>Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types.<h4>Findings</h4>The RNA-seq based algorithm can classify 52 different tumour types (precision ∼99%, recall ∼80%), plus the underlying 96 tumour subtypes (precision ∼96%, recall ∼70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scorEBioMedicine105506https://www.ebi.ac.uk/biostudies/studies/S-EPMC11784659biostudies-literatureM&amp;M: an RNA-seq based pan-cancer classifier for paediatric tumours.PMC11784659Verwiel ETPvan Tuil MBaker-Hernandez JLVermeulen MAKester LAHiemcke-Jiwa LSKranendonk MEGWesseling PTops BBJFlucke UELuesink MHoving EWHehir-Kwa JYvan Hamersveld Cvan Noesel MMKemmeren Pde Haas VWallis FSAde Krijger RRJanse AKoudijs MJVormoor JHfalseM&amp;M: an RNA-seq based pan-cancer classifier for paediatric tumours.<h4>Background</h4>With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools.<h4>Methods</h4>Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types.<h4>Findings</h4>The RNA-seq based algorithm can classify 52 different tumour types (precision ∼99%, recall ∼80%), plus the underlying 96 tumour subtypes (precision ∼96%, recall ∼70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scor2025-01-01T00:00:00Z2025 Jan2026-05-02T14:28:42.742Z2025-04-04T22:22:32.728ZS-EPMC117846593970977010.1016/j.ebiom.2024.105506