Int. J. of Computational Biology and Drug Design   »   2010 Vol.3, No.2

 

 

Title: Compact Extreme Learning Machines for biological systems

 

Author: Kang Li, Jing Deng, Hai-Bo He, Da-Jun Du

 

Addresses:
School of Electronics, Electrical Engineering and Computer Science, Queen
s University Belfast, Belfast, BT9 5AH, UK.
School of Electronics, Electrical Engineering and Computer Science, Queen
s University Belfast, Belfast, BT9 5AH, UK.
Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China

 

Abstract: In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness.

 

Keywords: ELM; extreme learning machines; fast recursive algorithms; linear-in-the-parameter; local regularisation; radial basis function; MAPK; SOS response; modelling; biochemical data; signal transduction pathway; transcriptional network; DNA damage; E. coli; simulation; computational biology.

 

DOI: 10.1504/IJCBDD.2010.035238

 

Int. J. of Computational Biology and Drug Design, 2010 Vol.3, No.2, pp.112 - 132

 

Date of acceptance: 29 Oct 2010
Available online: 16 Sep 2010

 

 

Editors Full Text AccessAccess for SubscribersPurchase this articleComment on this article