Helping Medical Researchers Classify Genes: New Micro-Array Software Identifies And Subgroups Genome Data

Part of the difficulty in gene research today is properly identifying and classifying genes into categories, says Professor Reza Emami, a lecturer at U of T's Institute for Aerospace Studies. The task is made more difficult by the fact that most organisms are made up of thousands of genes, while the human genome contains almost 100,000 genes, resulting in millions of data points. Rather than looking at each gene individually, the micro-array system and its analysis software look at the entire genome of each organism and group similar genes into "families."

"When you look at things like viruses or tumours in cell development, only a limited number of genes really impact on the cell," says Emami, who did this research while an associate in mechanical and industrial engineering. "What this work does is help weed out the so-called 'irrelevant' genes and identify the ones that have a legitimate impact. When you're able to put these genes into meaningful families, you can then find ways to manipulate their process in cells, leading to new areas of research and treatment."

As well, by applying what Emami calls "fuzzy clustering" to gene classification, the software is also able to help medical researchers determine a realistic view of which different genes might interact on cells and cause diseases.