A Comparison of PCA, ICA and Neural Network-based Approaches for Determination of Regulatory Signals in Biological Systems

The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies such as DNA microarrays, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been employed for computing low-dimensional or hidden representations of these datasets, but in most cases the results are inconsistent with underlying real network. In this paper for the first time, we have employed and compared three linear (PCA and ICA) and non-linear (MLP neural network) dimensionality reduction techniques to uncover these regulatory signals from outputs of biological/biomedical networked systems. The three approaches were verified experimentally using the absorbance spectra of a network of seven hemoglobin solutions, and the results revealed superiority of the neural network to PCA and ICA. This study showed the capability of the neural network approach to efficiently determine the regulatory components in biological or biomedical networked systems.

A Comparison of PCA, ICA and Neural Network-based Approaches for Determination of Regulatory Signals in Biological Systems Keywords:

Regulatory signal , Biological/biomedical network , Principal Component Analysis (PCA) , Independent Component Analysis (ICA) , Multi-layer perceptron neural network