Gaussian processes (GPs) are a useful and powerful approach for modeling nonlinear phenomena in various scientific fields, including genomics and genetics. In this review, we demonstrate an application of GPs specifically in genetic association mapping. Our focus is on the identification of genetic variants that alter gene regulation along cellular states at the molecular level, as well as disease susceptibility over time at the population level. Additionally, we address the challenges and opportunities that lie ahead in this field.

In recent years, various novel techniques have emerged in the realm of deep learning for enhanced pattern recognition. Multimodal learning is a widely used approach that enables simultaneous data input across multiple modalities, including video, audio, and text.For customer relationship management in the field of marketing, a comprehensive analysis using combinations of multiple data, such as the behavior log and survey responses, is actively pursued to assess customer loyalty and predict future behavior. However, these modalities of datasets are typically aggregated into a single dataset comprising variables that are hand-crafted by the analysts. This may not fully exploit the high predictive performance achieved by feature extraction in deep learning techniques.In this study, we employ a source--target multihead attention transformer encoder in conjunction with serial feature fusion, which enables the creation of a multimodal and context-aware deep learning model. This model can simultaneously process two high-dimensional datasets―time-series panel data that aggregates daily smartphone app usage and cross-sectional data containing demographic variables and survey responses. Finally, the both data are effectively fused at the upper layers of the model for the output. Results of the exhaustive analysis demonstrate that the proposed model outperforms other major deep learning architectures.

Gravitational wave (GW) predicted by General Relativity has recently become observable. In gravitational wave observation, non-stationary and non-Gaussian noise, called ``transient noise'', frequently appear. It is known that the transient noise might cause the instabilities in the detector. Transient noise might also mimic and obscure the GW signal. Identifying and classifying transient noise have a possibility of improving the detector performance. This study employed the deep learning (variational auto-encoder) to extract latent variables of transient noise. A tool that visualizes the latent variables embedded in a 3D space by using UMAP was developed. Because the tool can also display the corresponding input images, the visualization tool enables to the analysis of noise distribution in the latent space alongside the input images. Unsupervised classification was also performed on the transient noise in the latent space. By evaluating clustering instability and misclassification rate, the suitable class numbers were estimated, and the classification results were discussed.