Machine learning/AI is an influential tool in the analysis of RNA-Seq gene expression data. ML methods are broadly used to identify new biomarkers for disease diagnosis and treatment monitoring and to learn unseen patterns in gene expression that boost our understanding of the fundamental biological pathways. The success of an ML/AI model depends heavily on the input data. Identifying an appropriate dataset can be a challenge, and the data must be selected carefully, as a predictive model trained on the unreliable or inappropriate data will produce unreliable predictions. The rations of machine learning analyses in public functional genomics repositories are encountered by rare curated ML/AI-ready datasets. Therefore, it is important to study a data set sensibly to confirm its reputation for a machine learning job.
Bioconductor packages are used to show all differential gene expressions by generating the volcano map, Euclidean distances, and heatmap.

The aim of providing access to powerful statistical and graphical methods for the analysis of genomic data.

• To determine the differential gene expressions between groups are significant.
• To compare two or more groups of samples in order to identify the differentially expressed genes that are across experimental conditions.

• Undergraduate degree in Bioinformatics, Biology, Computational Biology, or related fields.
• Professionals in biotechnology, pharmaceuticals, or academic research.
• Individuals with a foundational understanding of molecular biology and an interest in computational data analysis.

• Proficiency in using GEO2R for differential gene expression analysis.
• Ability to interpret and validate gene expression data.
• Understanding of key bioinformatics concepts related to gene expression.
• Skills in data retrieval, normalization, and visualization.
• Preparedness for advanced research and career opportunities in genomics.