Editorial: Machine Learning Techniques on Gene Function Prediction Volume II

The document is an editorial summarizing the second volume of a special issue on the use of machine learning techniques in gene function prediction. The issue includes 24 papers that explore various aspects of this topic. Seven papers focus on protein function prediction or protein identification, using machine learning methods to identify specific proteins and predict their functions. Three papers examine the relationship between genes and diseases, including heart disease, Alzheimer's disease, and androgenic alopecia, using machine learning to predict disease occurrence and improve disease classification. Five papers discuss the relationship between genes and cancer, using machine learning to predict anticancer peptides and analyze subtypes of breast cancer. Other papers study the interactions between genes and proteins, gene and gene, and protein and protein, using machine learning to predict these interactions and improve understanding of them. The editorial concludes that the papers demonstrate the power of machine learning in gene function studies, particularly in inferring relationships between genes and diseases.