Distinguishing lung tumours from normal lung based on a small set of genes.

Identifying specific molecular markers and developing sensitive detection methods are two of the fundamental requirements for detection and differential diagnosis of cancer. Toward this goal, we first performed cDNA array analysis using 65 non-small cell lung cancer and non-involved normal lung tissues. We then used several complementary statistical and analytical methods to examine gene expression profiles generated by us and others from four independent sets of normal and neoplastic lung tissues.

Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management.

Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available.