Progressive and Interpretable Explanations for Machine Learning Models on Spectroscopy Data

Mass univariate aggregation methods for machine learning in neuroscience

Machine learning is a ubiquitous part of the modern neuroimaging toolkit, particularly for research aimed towards precision medicine goals of improving individual-level diagnosis and treatment. However, the high dimensionality of neuroimaging data poses significant challenges for constructing interpretable predictive m...

Beyond Spectral Attribution: A Validation Framework for Explainable AI in Biomedical Spectroscopy

Biomedical spectroscopy, including Raman, surface-enhanced Raman spectroscopy (SERS), infrared spectroscopy, and hyperspectral imaging, is increasingly combined with machine learning for disease classification, sample characterization, and biomarker-oriented analysis. However, high predictive performance does not estab...

Machine learning methods for predictive modeling in personalised medicine

This dissertation demonstrates how biologically informed machine learning and representation learning approaches can support scalable predictive modeling and transcriptomics-driven therapeutic inference across diverse biomedical applications.

Bridging the Explainability Gap A Novel Framework for Interpretable Deep Learning in Clinical Diagnostics

A Hybrid Ensemble Framework for Reliable and Interpretable Parkinson’s Disease Prediction Using Clinical Tabular Data

Parkinson’s disease (PD) prediction in clinical tabular data is challenging due to feature interactions and data imbalance. The current methods put emphasis on either predictive performance or interpretability in a unified way. This paper presents a comparative hybrid learning framework that evaluates conventional mach...

Message in a Bottleneck: Interpretable Deep Learning for Dynamic Effective Brain Connectivity.

A novel approach for deep extraction of causally informed features via restricted architecture (DECIFRA), designed for multivariate time series modeling that captures functionally grounded brain connectivity patterns, with certain dynamics associated with age or schizophrenia classification tasks and others consistentl...