Parkinson’s disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD’s diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD’s underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson’s Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.

Recent advances in the state-of-the-art force-torque sensors have improved the close-loop control of robotic manipulators. However, it is still challenging to perform a force-sensitive pick-and-place task, unless a considerable number of sensors monitor the process. The predictive capability of failures in conventional robotic object-sorting systems are limited. Using fifteen force-torque samples from the University of California-Irvine (UCI) database, we demonstrate the viability of failure prediction using an unsupervised Machine Learning (ML)-based method, whose learning parameters were optimised via Genetic Algorithms (GAs). This hybrid algorithm was deployed for discriminating between manipulation failure and successful object placement. GA was used to avoid overfitting or overtraining. The proposed model could detect robotic manipulation failures with 91.95% classification accuracy, thus improving on the performance of previous classification methods. This study validates the use of GAs and unsupervised ML to predict the extent of success for force-sensitive object placement using information on forces and torques alone.