Table of Contents Version 1:Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data Version 2: Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data – Version 2 — Integrating Oracle AI Database 26ai and Oracle APEX Github: https://github.com/brunorsreis/–cancer-gene-expression-classification-version3/ 1. Introduction Version 2 already stores the gene expression dataset in Oracle, runs Python analytics such as PCA, t-SNE, and K-means, and writes the analytical outputs back into Oracle for Oracle APEX dashboards. Version…
Month: March 2026
Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data – Version 2 — Integrating Oracle AI Database 26ai and Oracle APEX
Introduction Artificial intelligence (AI) is increasingly transforming healthcare by enabling the analysis of complex biomedical datasets and supporting advances in precision medicine. One important application is the classification of cancer types using gene expression data. Gene expression datasets measure the activity of thousands of genes simultaneously, allowing researchers to identify molecular patterns associated with specific tumour types. These datasets are typically extremely high dimensional and require advanced machine learning techniques to extract meaningful insights. The dataset used in this project contains approximately 802 tumour samples and more than 20,000 gene…
Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data
Introduction Artificial intelligence (AI) is transforming healthcare by enabling advanced analysis of complex biomedical data and supporting clinical decision-making. Traditional statistical methods often struggle with high-dimensional datasets, while machine learning can identify patterns directly from large amounts of data. According to Abtahi and Astaraki (2026), AI is particularly valuable in healthcare when analysing large datasets where traditional methods are limited. One important application of AI is cancer classification using gene expression data. These datasets measure the activity of thousands of genes simultaneously, helping researchers identify molecular patterns linked to specific…