سال انتشار: ۱۳۸۴

محل انتشار: دومین کنفرانس بین المللی فناوری اطلاعات و دانش

تعداد صفحات: ۶

نویسنده(ها):

Alireza Zomorrodi – Master’s student of Biochemical Engineering، Department of Chemical Engineering, AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Bahram Nasernejad – Associate Prof. of Chemical Engineering، Department of Chemical Engineering, AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Majid Raissi Dehkordi3 – Department of Computer Engineering & Information Technology, AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Jahanshah Kabudian – Department of Computer Engineering & Information Technology, AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran.

چکیده:

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 theresults 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.