A team led by Altuna Akalin at the Max Delbrück Center introduced Flexynesis, a deep learning toolkit designed for flexible integration of multi-omics datasets—including DNA, RNA, and protein information—along with medical records and imaging data. Published in Nature Communications, Flexynesis aims to assist clinicians in more precise cancer diagnosis, prognosis, and individualized therapy selection by overcoming limitations of existing modeling tools. Its flexibility across various tasks and accessibility via multiple platforms enable broad application in translational medicine and biomarker discovery.