Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity

Abstract

With increased research funding for Alzheimer’s disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

Chemoinformatics and Artificial Intelligence Colloquium: Progress and Challenges to Develop Bioactive Compounds

“…Access to AI technology and international networking can also accelerate the development of drugs for neglected diseases, Alzheimer's disease, and antibiotic resistance. The research group of Oprea developed ML models to identify a potential gene relevant to Alzheimer's disease immunity [28]. This analysis also identified potential risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2.…”

“…Learning from machine learning: some lessons from a genecentric Alzheimer's model [27,28] *By order of presentation. The content is organized into six sections: after this introduction, the challenges of chemoinformatics and AI methods are discussed, followed by opportunities, general insights, and overall discussion.…”

Chemoinformatics and Artificial Intelligence Colloquium: Progress and Challenges to Develop Bioactive Compounds

“…Learning from machine learning: some lessons from a genecentric Alzheimer's model [28,29] 1 In order of presentation. 2 Each lecture is associated with the references given in the far-right column and vice-versa.…”

“…Access to AI technology and international networking can also accelerate the development of drugs for neglected diseases, Alzheimer's disease, and antibiotic resistance. The research group of Oprea developed ML models to identify a potential gene relevant to susceptibility to Alzheimer's disease [29].…”

Multi-modal Protein Knowledge Graph Construction and Applications

“…KG is a structured semantic knowledge base for describing concepts and their interrelationships in the physical world in symbolic form, which can provide back-end support for a variety of tasks such as recommender systems [12]. In the biological domain, KG has been investigated for a long time [13][14][15]. CKG [16] collects annotations from 26 biomedical databases using ten ontologies.…”