Protein sequence analysis presents a significant challenge in bioinformatics, impacting applications such as disease investigation and precision medicine. Despite advancements in sequencing technologies and the resultant proliferation of databases, classifying protein families remains a hurdle. This study introduces a novel deep few-shot network specifically designed for protein family classification, addressing the limitations of traditional methods by utilizing deep learning with sparse training datasets. Our experiments show that this framework outperforms state-of-the-art baseline models, achieving 97.3 % precision and 95.2 % recall on the training set, and 95.0 % precision and 93.5 % recall on the test set, when trained with only 25 shots per class. This work represents the first endeavor in crafting a deep network tailored for primary sequence family classification, achieving remarkable performance with minimal observations.

Noise reduction is an essential step of cDNA microarray image analysis for obtaining better-quality gene expression measurements. Wavelet-based denoising methods have shown significant success in traditional image processing. The complex wavelet transform (CWT) is preferred to the classical discrete wavelet transform for denoising of microarray images due to its improved directional selectivity for better representation of the circular edges of spots and near shift-invariance property. Existing CWT-based denoising methods are not efficient for microarray image processing because they fail to take into account the signal as well as noise correlations that exist between red and green channel images. In this paper, two bivariate estimators are developed for the CWT-based denoising of microarray images using the standard maximum a posteriori and linear minimum mean squared error estimation criteria. The proposed denoising methods are capable of taking into account both the interchannel signal and noise correlations. Significance of the proposed denoising methods is assessed by examining the effect of noise reduction on the estimation of the log-intensity ratio. Extensive experimentations are carried out to show that the proposed methods provide better noise reduction of microarray images leading to more accurate estimation of the log-intensity ratios as compared to the other CWT-based denoising methods.