## 4 Results and Discussion
### Association Rule Mining
In the biological space, what can be defined as control/normal samples is a gray area, and therefore this may have a bearing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Selection Method** & \multirow{2}{*}{**Support**} & \multirow{2}{*}{**Confidence**} & **Lift** & **No. of Rules** & **Non-Redundant Rules** \\ \hline \multirow{4}{*}{GSE60052} & Graph-based & 0.5 & 0.9 & 2 & 19 & 15 \\  & PCA & 0.4 & 0.9 & 2 & 38 & 38 \\  & RFE & 0.3 & 0.9 & 1.98 & 357,986 & 112,357 \\ \hline \multirow{4}{*}{GSE81089} & Graph-based & 0.5 & 0.9 & 2 & 36 & 36 \\  & PCA & 0.4 & 0.9 & 1 & 121 & 121 \\ \cline{1-1}  & RFE & 0.4 & 0.9 & 1 & 899 & 884 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rules generated using Apriori from features selected using different approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**GSE60052**} \\ \hline \multicolumn{4}{c}{**Rules**} & **Support** & **Confidence** & **Lift** \\ \hline X & Y & & & \\ \hline \([\)SFTPA1, SDC4, LRRK2\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)ACVRL1, COL4A3, AQPI\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)EDNRB, SFITC, AGER\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)PTPRB, SFITPC, CLDN\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)EPAS1, EDNRB, LRRK2, AQPI\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)CLDN18, EPAS1, SFITPA1, AGER\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)EPAS1, NAPSA, LRRK2, AGER\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)TIMP3, CTSH, SFITA1, LRRK2\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)CTSH, NAPSA, TGFBR, SFITPC\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \([\)RRAS, PTPPR, YAP1, SMAD6\(]\Rightarrow\) [SLC34A2] & 0.5 & 0.9 & 2 \\ \hline \multicolumn{4}{c}{**GSE81089**} \\ \hline \hline \multicolumn{4}{c}{**Rules**} & **Support** & **Confidence** & **Lift** \\ \hline X & Y & & \\ \([\)ASPM, KIF4A, CL06, NUF2\(]\Rightarrow\) [TOP2A] & 0.5 & 1 & 2 \\ \([\)ASPM, CL06, CDC20, NUF2\(]\Rightarrow\) [TOP2A] & 0.5 & 1 & 2 \\ \([\)ASPM, CDC6, CDC8, NUF2\(]\Rightarrow\) [TOP2A] & 0.5 & 1 & 2 \\ \([\)TPX2, FOXM1, NUF2, IQGAP3\(]\Rightarrow\) [BIRC5] & 0.5 & 1 & 2 \\ \([\)CDC6, FOXM1, CDC20, UBE2C\(]\Rightarrow\) [TPX2] & 0.5 & 1 & 2 \\ \([\)ASPM, CDC6, DLGAP5, NUF2\(]\Rightarrow\) [TOP2A] & 0.5 & 1 & 2 \\ \([\)TPX2, CDCA8, UBE2C, IQGAP3\(]\Rightarrow\) [BIRC5] & 0.5 & 1 & 2 \\ \([\)ASPM, KIF4A, CDC6, UBE2C\(]\Rightarrow\) [CENPF] & 0.5 & 1 & 2 \\ \([\)TPX2, CDC6, FOXM1, IQGAP3\(]\Rightarrow\) [BIRC5] & 0.5 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of top ten rules generated from the two datasets after graph-based. on downstream analysis while at the same time bringing about class imbalance. Internal threats to this kind of study include experimental noise in the data as well as the assumption that gene expression level is uniform across cells. Co-regulation and co-expression between the features can also lead to redundancy in the datasets. Features without an assigned biological function may also not be informative unless in vitro experiments are designed to validate the function.