The study by Ramaswamy et al. (2001) aimed to determine whether molecular classification could achieve accurate diagnosis of multiple common adult malignancies. The researchers analyzed gene expression profiles from 218 tumor samples spanning 14 common tumor types and 90 normal tissue samples using oligonucleotide microarrays. They used a support vector machine (SVM) algorithm to evaluate the accuracy of a multiclass classifier. The overall classification accuracy was 78%, significantly higher than random classification (9%). Poorly differentiated cancers were more difficult to classify accurately, indicating that they have distinct molecular profiles compared to well-differentiated cancers. The study demonstrates the feasibility of accurate, multiclass molecular cancer classification and suggests a strategy for future clinical implementation of molecular cancer diagnostics. The findings also highlight the importance of molecular markers in distinguishing between different types of cancers and the potential for using gene expression data to improve cancer diagnosis and management.The study by Ramaswamy et al. (2001) aimed to determine whether molecular classification could achieve accurate diagnosis of multiple common adult malignancies. The researchers analyzed gene expression profiles from 218 tumor samples spanning 14 common tumor types and 90 normal tissue samples using oligonucleotide microarrays. They used a support vector machine (SVM) algorithm to evaluate the accuracy of a multiclass classifier. The overall classification accuracy was 78%, significantly higher than random classification (9%). Poorly differentiated cancers were more difficult to classify accurately, indicating that they have distinct molecular profiles compared to well-differentiated cancers. The study demonstrates the feasibility of accurate, multiclass molecular cancer classification and suggests a strategy for future clinical implementation of molecular cancer diagnostics. The findings also highlight the importance of molecular markers in distinguishing between different types of cancers and the potential for using gene expression data to improve cancer diagnosis and management.