and its growth shows no signs
of stopping. The human brain simply cannot deal with so much information. Therefore,
it is necessary to employ artificial intelligence techniques in the process of
scientific research. Artificial intelligence provides increased speed and "capacity"
compared to the human brain, which proves incredibly useful to researchers.
Specifically, neural networks are a very useful
tool. Neural networks mimic the behavior of the human brain, so they are often
used in applications where systematic thought processes are important. Processing
units are interconnected and communicate like biological neurons. Successful and
relevant connections are emphasized whereas irrelevant connections are downgraded.
This mimics the conditioned learning of biological organisms. This dynamic learning
is vital in the research process since science does not remain static.
Cancer is a difficult
disease to research. Cancer results from everyday cells that, in effect, "lose
their mind." In normal, healthy cells, the process of cell replication is
highly regulated. Many "checkpoints" are in place to make sure that
all steps are done properly. These "checkpoints" are usually molecules
that are created or degraded which signal the cell to either hold in the stage
where it is or to move on to the next stage of cell division. For example, one
such molecule believed to be involved in several different cancers is called p53.
This molecule is often absent or mutated in extracts from tumor cells, indicating
that perhaps p53 is a molecule that signals the
cell to stop dividing. If p53 is not produced,
then cells will divide too rapidly and cancer can result.
no two cancer physiologies are exactly alike, it is notoriously difficult to make
a prediction about whether the patient is likely to survive. Even with two cases
of the same type of cancer, one person may survive for many years while another
will die within months. A myriad of different physical factors contribute to the
outcome. This is one reason why cancer is such a frightening disease - no one
can tell you whether your particular complement of genes will put you at an advantage
Researchers at the National
Cancer Institute (NCI) are working on creating a model that will help predict
the prognosis of cancer patients. Particularly, they are working with a type of
cancer called Neuroblastoma. Neuroblastoma is a childhood cancer. It usually begins
with cells of the adrenal gland and spreads, creating tumors in the neck, chest,
abdomen or pelvis. Using DNA microarray analysis (see PC AI Volume 18, Issue 2
- "Microarrays and Artificial Intelligence") the researchers studied
the gene expression profiles of cancer patients. The microarrays consisted of
about 25,000 genes and the analysis was repeated for 49 patients. In order to
connect the microarray results with certain outcomes, the 49 patients were chosen
because their outcomes were known. Some of the patients survived for more than
three years without any cancer-related issues. Others died due to the disease.
Feeding this information into an artificial neural network, the researchers found
they were able to predict whether a patient would