Enhanced gene ranking approaches using modified trace ratio algorithm for gene expression data

- From the existing TR algorithm, the rank of the genes was extracted and the dataset was reformed according to the new rank generated.
- Two different approaches were considered and hence two new methods, namely IG-TR Gene Ranking and CCA-TR Gene Ranking were proposed.
- Instead of considering Fisher's score as the criteria for scoring in the traditional TR algorithm, another statistical technique called Canonical Correlation score was selcted for generation of new rank list.
- KSI, BCR and BER were the three performance metrics chosen for assessing their performance.

Microarray technology enables the understanding and investigation of gene expression levels by analyzing high dimensional datasets that contain few samples. Over time, microarray expression data have been collected for studying the underlying biological mechanisms of disease. One such application for understanding the mechanism is by constructing a gene regulatory network (GRN). One of the foremost key criteria for GRN discovery is gene selection. Choosing a generous set of genes for the structure of the network is highly desirable. For this role, two suitable methods were proposed for selection of appropriate genes. The first approach comprises a gene selection method called Information gain, where the dataset is reformed and fused with another distinct algorithm called Trace Ratio (TR). Our second method is the implementation of our projected modified TR algorithm, where the scoring base for finding weight matrices has been re-designed. Both the methods' efficiency was shown with different classifiers that include variants of the Artificial Neural Network classifier, such as Resilient Propagation, Quick Propagation, Back Propagation, Manhattan Propagation and Radial Basis Function Neural Network and also the Support Vector Machine (SVM) classifier. In the study, it was confirmed that both of the proposed methods worked well and offered high accuracy with a lesser number of iterations as compared to the original Trace Ratio algorithm.