Functional link artificial neural network-based disease gene prediction

Authors:
Jiabao Sun;Jagdish C. Patra;Yongjin Li
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 3
Cited 0

Adaptive pattern recognition and neural networks

Adaptive pattern recognition and neural networks
Discovering disease-genes by topological features in human protein--protein interaction network

Bioinformatics
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

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 1 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.