The field is witnessing a significant shift towards developing more advanced and interpretable models, particularly in the areas of representation learning and survival analysis. Researchers are exploring innovative techniques such as graph contrastive learning, graph transformers, and Kolmogorov-Arnold Networks to improve model performance and interpretability. Notably, the integration of these techniques with traditional methods is leading to state-of-the-art results in various applications, including brain network classification, brain disorder diagnosis, and energy applications. Moreover, the development of novel frameworks like COHESION and TabKAN is enabling more effective and efficient analysis of multimodal and tabular data. The focus on interpretability is also evident in the development of symbolic regression methods for survival analysis and the proposal of models like RO-FIGS, which provide valuable insights into feature interactions. Some noteworthy papers in this regard include the proposal of PHGCL-DDGformer for brain network classification, AFBR-KAN for brain disorder diagnosis, and TabKAN for tabular data analysis. Overall, the field is moving towards developing more sophisticated, interpretable, and generalizable models that can effectively handle complex data and provide actionable insights. Noteworthy papers include: PHGCL-DDGformer, which achieves state-of-the-art results in brain network classification, and TabKAN, which advances tabular data modeling using Kolmogorov-Arnold Networks.