Functional Link Artificial Neural Network-based Disease Gene Prediction

Genes that contribute to complex traits pose special challenges that make candidate disease-associated gene discovery more difficult. In this work, we investigated topological features derived from PPI network to identify the causing genes of four complex diseases: Cancer, Type I Diabetes, Type 2 Diabetes, and Ageing genes. We used 10-fold cross-validation to evaluate the predictive capacity of all possible combinations of these features and found the features with the best predictive ability. We assessed the performance of Multi-layer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN), and Support Vector Machines (SVM). We found that SVM provides higher accuracy than MLP and FLANN. However, the FLANN has significantly low computation time while its accuracy is comparable to that of SVM and MLP.