Longitudinal gene expression profiling provides crucial insights into dynamic biological processes, yet predictive modeling is severely hampered by irregular sampling, missing values, and high dimensionality. Mainstream approaches, including recurrent and transformer-based architectures, often struggle with the sparsity and temporal misalignment inherent in multi-visit transcriptomic datasets. We introduce G-Shapelets, a novel framework that learns to predict gene expression dynamics by matching discriminative local temporal patterns. Instead of modeling entire irregular trajectories, G-Shapelets identify multiscale temporal motifs-or shapelets-that serve as robust and interpretable building blocks for prediction. This approach is naturally resilient to gaps and irregular time intervals because it operates on local subsequences. By learning a dictionary of these shapelets and measuring their distance to segments of an input gene's trajectory, our model constructs a feature representation that effectively captures dynamic behavior. Extensive experiments on the longitudinal viral immunization dataset (HR-VILAGE3K3M) demonstrate that G-Shapelets achieves$\mathbf{4. 8 \%}$lower mean squared error and statistically significant improvements over recurrent, convolutional, and transformer baselines. Furthermore, we show that the learned shapelets correspond to meaningful biological patterns, offering interpretability that is absent in blackbox models. This framework opens new avenues for accurate and interpretable modeling of dynamic transcriptomic systems.

The operation of smart grids heavily relies on secure and accurate meter measurements provided by phasor measurement units (PMUs). Therefore, the optimal PMU placement (OPP) aiming to achieve the complete system observability of smart grids with as few PMUs as possible has been extensively investigated. Although many existing studies have focused on the OPP, few of them are concerned with the placement order of PMUs. To protect as many buses as possible in smart grids when installing PMUs in stages owing to high cost, this paper proposes the attack-resilient OPP strategy which places PMUs in order by using reinforcement learning guided tree search, where the sequential decision making of reinforcement learning is utilized to explore placement orders. The least-effort attack model is carried out to screen vulnerable buses such that the buses adjacent to these buses can be placed PMUs in advance to reduce the state space and action space of the large-scale smart grid environment. Based on that, the reinforcement learning guided tree search approach is used to explore the key buses which need placing PMUs, where the repeated exploration of the agent is avoided by tree search. Then, a reasonable placement order of PMUs is obtained according to the action sequence the proposed method provides. Finally, the effectiveness of the proposed method is verified on various IEEE standard test systems and the comparison results with existing methods are provided.