## 1 _Editorial_  
Deep Learning and Machine Learning Applications  
in Biomedicine  
[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2,*  
The rise of omics research, spanning genomics, transcriptomics, proteomics, and epigenomics, has revolutionized our understanding of biological systems. While the development of these technologies offers immense opportunities for exploring biological complexity, the sheer volume and complexity of multi-omics data present significant analytical challenges. In this context, Artificial Intelligence (AI), with its advantages in data processing and learning capabilities, has become a key tool for multi-omics data analysis. The applications of AI have expanded to include various fields such as disease diagnosis, precision medicine, drug discovery, and elucidating pathogenic mechanisms. This paper delves into the latest advancements of AI in the life sciences sector, with a particular emphasis on its application in crucial areas such as genomics, transcriptomics, and proteomics. By analyzing these successful cases, we aim to demonstrate the potential of AI in handling and applying multi-omics data and provide valuable insights and guidance to researchers. Genomics, as a scientific field studying the genetic blueprint of organisms, is dedicated to decoding the genomes of living entities. The core objectives of this field include understanding genetic variations, gene functions within the genome, and their impact on an organism's morphology, physiology, and disease occurrence. Particularly, deep learning (DL), a branch of Artificial Intelligence, has demonstrated significant potential in deciphering gene regulation, exploring genome structure, and analyzing variation effects. Tools like DeepVariant [1] and Clairvoyante [2] utilize deep learning to analyze DNA sequence data for variation detection, including identifying single nucleotide polymorphisms (SNPs) and structural variations. Compared to traditional methods, deep learning excels in capturing complex dependencies between sequencing reads, thereby enhancing accuracy and efficiency and enabling more precise genetic analysis. In the realm of cancer genomics, deep learning tools are instrumental in assessing the pathogenicity of variations, elucidating the specific effects of particular variations and informing treatment strategies and prognosis [3, 4, 5]. This application is particularly pivotal considering the unique genetic landscapes presented by various cancers. The journey towards understanding gene function has often been hindered by the inefficiency of experimental annotation. Researchers are increasingly employing Artificial Intelligence to develop computational tools in functional genomics. This includes the prediction of gene functions [6] and regulatory elements like enhancers and promoters [7, 8]. Additionally, deep learning is utilized to mine and predict the functional impact of non-coding variations from large-scale genomic data, aiding in unraveling the complexities of gene regulatory networks and the potential causal mechanisms behind genetic variations [9, 10, 11]. Beyond these applications, deep learning also extends its influence into the realm of epigenomics. For instance, tools like DeepCpG [12] and DeepHitsone [13] analyze DNA methylation and histone modification patterns, contributing to a more profound understanding of how these epigenetic factors influence gene expression and disease development. With advancements in high-throughput sequencing technologies, the field of transcriptomics has broadened to include both single-cell and spatial resolution studies. The massive scale and complexity of raw transcriptomic data necessitate sophisticated computational algorithms and tools for preprocessing (quality control, dimensionality reduction, and clustering) and downstream analysis.