## 1 _Editorial_
Various renowned software packages, including Seurat [14] and Scanpy [15], offer comprehensive solutions for transcriptomic data analysis and are adept at tasks like data dimensionality reduction, cell clustering, and differential expression analysis. DL efficiently extracts rich, compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data, thus enhancing downstream analysis. Unsupervised learning, employed for data mining and pattern identification in unlabeled data, is widely applied in scRNA-seq for dimensionality reduction and cell clustering [16; 17; 18; 19]. In scRNA-seq, a low RNA capture rate frequently leads to dropout issues. Researchers utilize neural network algorithms for data imputation in scRNA-seq, effectively mitigating noise in gene expression profiles [20; 21; 22]. It is noteworthy that a significant advantage of DL in scRNA-seq data analysis is its capacity to handle nonlinear relationships between genes. In tasks like batch effect correction [23; 24], cell type identification [25], and gene regulatory network [26; 27] analysis, DL methods outperform traditional ones in terms of flexibility and efficiency. Deep learning also finds significant application in the field of spatial transcriptomics. Owing to sequencing technology limitations, emerging spatial transcriptomics has not yet achieved single-cell resolution in gene expression detection. Deep learning can be employed to synergistically analyze single-cell and spatial transcriptomic data, addressing this challenge [28; 29]. Additionally, deep learning is utilized for spatial domain identification [30], cell-cell communication [31], 3D reconstruction [32], and detecting spatially variable genes [33]. Furthermore, the development of pre-training models such as Genefomer [34] is paving the way for more sophisticated analyses of specific downstream tasks. Protemos is one of the leading application domains for AI. Research includes predicting proteins' three-dimensional structures and functions from primary sequences, studying protein interactions, and designing peptides [35]. Natural Language Processing (NLP) and Computer Vision (CV) methods, such as Transformers and Convolutional Neural Networks (CNNs), play a crucial role in the field of proteomics, particularly in protein residue modeling. A notable example is AlphaFold [36], which uses CNNs and RNNs to accurately predict protein spatial structures. DL also excels in identifying proteins' biological functions based on amino acid sequences, aiding in both general and specific protein recognition [37; 38; 39; 40; 41; 42; 43; 44]. In peptide research, it has revolutionized traditional methods, such as mass spectrometry, for peptide identification [45; 46; 47]. For protein sequence design, the ProtGPT2 model by [PERSON] et al. [48] demonstrates DL's capability in generating biologically consistent sequences. Analyzing post-translational modification (PTM) sites is another critical area where DL, particularly Transformer-based models, effectively classifies and predicts PTMs [49; 50]. In the field of pharmacoinformatics, Artificial Intelligence has shown potential in predicting drug targets and drug-protein affinity [51; 52]. Lastly, DL has significantly advanced single-cell proteomics analysis, improving proteome coverage and aiding in cell type/state identification from bulk tissue profiles [53]. Advancements in computing and algorithmic technology are broadening the scope of AI in life sciences. This evolution has drastically improved the processing and analysis of biological data, uncovering complex nonlinear correlations within biological systems and offering innovative approaches for disease research and drug development. AI's role in precision medicine is increasingly critical, especially in biomarker discovery, sample classification, and interpreting disease processes. Despite these advances, DL in life sciences faces challenges related to dataset type and size, which affect its effectiveness and present uncertainties.