| | Category | MATH | P21 | Bayesian probablity technique to estimate Schziophrenia |
| | Abstract | First I will take pre-existing samples from NCBI, which is the National |
| | Center for Biotechnology information. The data will be analyzed with |
| | Geo2R based on the samples containing Schizophrenia and the |
| | samples not containing it. The software will give me the top 250 genes |
| | with the highest levels of expression. This will happen for the samples |
| | from the superior temporal cortex and the samples from the anterior |
| | prefrontal cortex. From that database, I will also take more data sets for |
| | laboratory animals. I will take these genes and insert them into String |
| | Db to better see the connections in Homosapiens and other species. I |
| | then want to use probability distribution techniques like Bayes Theorem |
| | to find out if |
| | by combining probabilities that come from prior known proteins and |
| | current observations in other species, I can predict the proteins and |
| | genes that are downregulated or upregulated in humans. |
| | Bibliography | STRING: a database of predicted functional |
| | associations between proteins.Christian von Mering1,2, Martijn |
| | Huynen3, Daniel Jaeggi1,2, Steffen Schmidt1,2, Peer Bork1,2, and |
| | Berend Snel3. Nucleic Acids Research, 2003, Vol. 31, No. 1Potential |
| | genetic variants in schizophrenia:A Bayesian analysis. Hakan Hall et-al. |
| | The World Journal of Biological Psychiatry, 2007; 8(1): 1222 |