AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

We developed an adaptive and unified stacking genomic selection framework and designed a model interpretation strategy to identify the candidate significant SNPs of target traits. Genomic selection (GS) is an important technique in modern molecular breeding. As a powerful machine learning (ML) GS approach, stacking ensemble learning (SEL) combines multiple basic models (base learners, BLs) and effectively blends the strengths of different models to precisely depict the complex relationships between phenotypes and genotypes.

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

Background: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.

New Methods: An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data.