Boston University to Apply Machine Learning to Alzheimer’s Biomarker and Cognitive Data
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A collaborative team spanning 11 institutes is applying advanced machine learning techniques to extensive datasets of Alzheimer’s disease biomarkers and cognitive data, seeking to address the high failure rate of clinical trials for the disease since 2003. The project, part of the Artificial Intelligence for Alzheimer’s Disease initiative, aims to identify key features within genomic, biomarker, and cognitive information to address fundamental barriers to prevention and drug discovery. Investigators believe Alzheimer’s is a multifactorial disease requiring personalized treatment approaches, a strategy that has seen success in cancer but remains largely unexplored in neurodegeneration. Researchers suggest precision medicine involves developing patient-specific treatment solutions based on genetic profiles and the availability of existing drug options, and this effort will build an AI/ML-ready database to facilitate scalable evaluation of potential therapies.
High Failure Rate Drives Need for AD Precision Medicine Nearly all Alzheimer’s disease clinical trials have ended in failure since 2003, with approximately 99 percent yielding no statistically significant benefit for patients; this statistic underscores the critical need for a different approach to treatment development. Researchers now believe the high failure rate stems from significant heterogeneity among trial participants, suggesting Alzheimer’s is not a single disease but rather a collection of conditions with diverse underlying causes and clinical presentations. This realization has propelled the focus toward precision medicine, a strategy successfully implemented in oncology but largely unexplored in the context of neurodegenerative diseases. A collaborative team spanning 11 institutes aims to address this gap by analyzing extensive genomic, biomarker, and cognitive data to pinpoint essential features for preventing the disease and discovering effective drugs. Jun, Director of Genome Guided Drug Discovery Core at the AI4AD initiative, is leading efforts to prioritize genetic variants and identify network-based signatures for repurposing existing drugs. Jun’s lab is developing an “AI/ML-ready database” by profiling predictor, signature, biomarker, and outcome (PreSiBO) features, which will facilitate scalable evaluation of potential treatments and enhance collaboration among researchers; this extended effort, according to the team, will increase the readiness of AI/ML applications in precision medicine for Alzheimer’s disease prevention and treatment. PreSiBO Database Enables AI/ML-Driven Drug Repurposing The increasing failure rate of Alzheimer’s disease clinical trials, approximately 99 percent since 2003, is driving a shift toward leveraging existing data through artificial intelligence and machine learning approaches to identify potential therapeutic interventions. Central to this initiative is the development of PreSiBO, an AI/ML-ready database designed to profile predictor, signature, biomarker, and outcome features for both targets and existing drugs relevant to specific genome-guided patient subgroups. Recent work from Dr. Jun’s lab has focused on applying AI/ML to target prioritization and drug repurposing, indicating a growing emphasis on precision medicine concepts within Alzheimer’s research. The creation of PreSiBO is intended to increase the readiness of AI/ML applications for both the prevention and treatment of Alzheimer’s disease, offering a crucial resource for accelerating the development of patient-driven treatment solutions. Precision medicine is to develop patient driven treatment solutions, largely depending on genetic profiles and the availability of existing drug options. Source: https://sites.bu.edu/junlab/research-overview/ai4ad/ Tags: Quantum News There is so much happening right now in the field of technology, whether AI or the march of robots. Adrian is an expert on how technology can be transformative, especially frontier technologies. But Quantum occupies a special space. Quite literally a special space. A Hilbert space infact, haha! Here I try to provide some of the news that is considered breaking news in the Quantum Computing and Quantum tech space. Latest Posts by Quantum News: Nature Validates Potential of MEMS Switches for Large-Scale Quantum Computing with Logic Gate Demonstration April 2, 2026 University of Eastern Finland Demonstrates 2D-Material Photodetectors on Silicon Nitride Chips April 2, 2026 Diamante Gains Funding to Expand Quantum-Safe Layer-1 Blockchain Protocol April 2, 2026