Novel Study Development of Classification Techniques for Gene Data Analysis using AI

Machine learning has the capability to derive general hypotheses from external data instances and predict the occurrence of new instances. While classification outputs provide insights into correctly classified test data samples, addressing unknown sample data in the same process is equally essential. This paper presents an overview of various supervised machine learning methods for gene categorization, highlighting important theoretical inquiries and suggesting novel avenues for research. Additionally, the paper recommends exploring uninvestigated combinations of biases to enhance the effectiveness of gene categorization techniques. By addressing these aspects, researchers can advance the field of gene categorization and unlock new possibilities for understanding biological phenomena.