Alzheimer's disease (AD) is one of the most common dementia, and its pathogenesis has not been clarified. The failure of amyloid targeted therapy has led us to rethink the pathogenesis of AD. There is growing evidence that complex diseases usually involve the impairment of multiple biological functions, rather than focus on several single genes. Protein-protein interaction network (PPIN) has been recognized as an important tool for identifying and predicting disease biomarkers. It is a great challenge to design network-based classification method for identifying effective, stable and interpretable biomarkers to distinguish the disease phenotype based on gene expression profile data. In this study, we used graph Laplacian regularization method to introduce topology information of PPIN, which can reveal the damaged networks involved in disease from heterogeneous gene expression profile data and identify disease-related biomarkers. The results in three AD datasets showed that the biomarkers identified by our method can not only distinguish the sample categories more accurately, but also help researchers understand the biological meaning behind complex diseases.
Keywords: Diseases prediction model; Pathway enrichment analysis; Protein-protein interaction network; Regression model; Systems biology.
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