One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

Fish identification and classification tasks allow scientists greater data on the sustainability and diversity of fisheries. Advances in computer vision, particularly convolutional neural networks (CNNs), have enabled automated fish detection at scale; however, increased model depth may not justify the additional time and computational cost. This study examines the ResNet family of models for binary fish identification to assess whether deeper networks provide meaningful performance advantages over simpler configurations. We compare ResNet-18, ResNet-50, ResNet-101, and ResNet-152 using both non-pretrained and pretrained initializations. We introduce an Energy-Weighted Score (EWS) to enable comparison of computational resource usage using cost-based weighting. A reliable fish versus no-fish classification can be achieved with as few as 18 layers, yielding 99.975% accuracy which improved to 100% accuracy following threshold optimization. For binary fish identification, increasing ResNet depth provides increased EWS scores with little impact on accuracy returns over shallower models. Overall, models with fewer layers outperformed deeper models with more parameters, and additional depth and tuning techniques were unable to outperform simpler configurations while delivering higher EWS scores.

Missing genotypes reduce statistical power and hinder genome-wide association studies. While reference-based methods are popular, they struggle in complex regions and under population mismatch. Existing reference-free deep learning models show promise in addressing this issue but often fail to impute rare variants in small datasets. We propose BiU-Net, a biologically informed U-Net model that segments genotype data and encodes positional information to preserve the genomic context. Evaluated on the 1000 Genomes Project, Louisiana Osteoporosis Study, and Simons Genome Diversity Project datasets, BiU-Net outperformed Beagle and sparse convolutional denoising autoencoder in overall metrics and in metrics stratified by minor allele frequency.