Artificial Intelligence and Machine Learning (AIML) approaches to empower genomics, drug, and biomarker discovery in ADRD
Paul M Thompson, Towfique Raj- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
In 2021, the NIA launched the Artificial Intelligence and Machine Learning (AIML) Consortium, to develop novel AI and machine learning techniques to tackle pressing questions in Alzheimer’s disease research.
Method
Drawing on vast repositories of data from the Alzheimer’s Disease Sequencing Project (ADSP) and other large public biobanks, the 8 awardees funded under this program meet monthly to present their innovations in data analytics in 4 main areas: (1) genome‐guided drug discovery and drug repositioning for AD; (2) discovery of novel genetic and molecular targets for AD therapeutics using whole genome and omics data; and (3) understanding the regulatory code of the genome using deep learning and bioinformatics, and (4) use of electronic health records (EHR) to study treatment outcomes in ADRD. A second branch of AIML activity draws on repositories of neuroimaging data (MRI/FLAIR, DTI, and amyloid‐ and tau‐sensitive PET) to (1) identify ADRD subtypes based on neuroimaging, clinical and neuropsychological data, that differ in terms of their genomic drivers and expected treatment response (for precision medicine and drug trial stratification), (2) perform genetic analysis of endophenotypes derived from brain images (such as longitudinal hippocampal volume changes, atrophy subtype scores, and PET or WMH signatures) for biomarker‐guided gene discovery, and (3) predict neuropathology (TDP‐43, CAA, LBD) from multimodal imaging using AI. The AIML initiative benefits from and helps the ADSP’s Phenotype Harmonization Consortium, as AI methods can also be used to adjust neuroimaging data for site and protocol effects (harmonization).
Result
In this overview of the AIML Consortium, we highlight some recent successes using (1) convolutional neural networks (CNNs), GANs and deep learning for ADRD subtyping and staging; (2) discovery of AD‐relevant motifs and networks in whole genome sequences and functional genomics data; (3) prognostic models of clinical outcomes based on WGS and biomarkers; and (4) novel methods from statistical physics, based on evolutionary action, which model the fitness effect of coding mutations analytically.
Conclusion
AIML investigators are hosting regular workshops at AAIC and upcoming genomics and clinical neurology conferences to disseminate and teach these approaches to the AD and broader scientific community.