Determination of an Efficient Differentiation Pathway of Stem Cells Employing Predictory Neural Network Model
The stem cells have ability to differentiated
themselves through mitotic cell division and various range of
specialized cell types. Cellular differentiation is a way by which few
specialized cell develops into more specialized.This paper studies the
fundamental problem of computational schema for an artificial neural
network based on chemical, physical and biological variables of
state. By doing this type of study system could be model for a viable
propagation of various economically important stem cells
differentiation. This paper proposes various differentiation outcomes
of artificial neural network into variety of potential specialized cells
on implementing MATLAB version 2009. A feed-forward back
propagation kind of network was created to input vector (five input
elements) with single hidden layer and one output unit in output
layer. The efficiency of neural network was done by the assessment
of results achieved from this study with that of experimental data
input and chosen target data. The propose solution for the efficiency
of artificial neural network assessed by the comparatative analysis of
“Mean Square Error" at zero epochs. There are different variables of
data in order to test the targeted results.
[1] http://www.stemcellfacts.org/
[2] http://www.diffen.com/difference/Meiosis_vs_Mitosis
[3] http://stemcells.nih.gov/info/glossary.asp
[4] http://www.nonlinearbiomedphys.com/content/1/1/10
[5] Shakti Mehrotra, Om Prakash, B.N Mishra, B. Dwevedi "Efficiency of
neural networks for prediction of in vitro culture conditions and
inoculum properties for optimum productivity"
[6] Dina Goren-Bar, Tsvi Kuflik, Dror Lev " [6] Supervised Learning for
Automatic Classification fo Documents using Self-Organizing Maps".
[7] Kasthurirangan Gopalakrishnan, Siddhartha Khaitan and Anshu Manik
"Enhanced Clustering Analysis and Visulization Using Kohonen's Self-
Organizing Feature Map Network".
[8] http://www.ncbi.nlm.nih.gov/About/primer/genetics_cell.html
[9] http://stemcells.nih.gov/info/basics/basics4.asp
[10] Neural Network Design. By Martin T. Hagan, Howard B. Demuth, Mark
H. Beale.
[11] Nazmul Karim M, Yoshida T, Rivera SL, Saucedo VM, Eikens B, Oh
GS (1997) "Global and local neural network models".
[12] Hishimota Y (1997) Application of artificial neural network and genetic
algorithms to agriculture systems. Compute Electron Agric.
[1] http://www.stemcellfacts.org/
[2] http://www.diffen.com/difference/Meiosis_vs_Mitosis
[3] http://stemcells.nih.gov/info/glossary.asp
[4] http://www.nonlinearbiomedphys.com/content/1/1/10
[5] Shakti Mehrotra, Om Prakash, B.N Mishra, B. Dwevedi "Efficiency of
neural networks for prediction of in vitro culture conditions and
inoculum properties for optimum productivity"
[6] Dina Goren-Bar, Tsvi Kuflik, Dror Lev " [6] Supervised Learning for
Automatic Classification fo Documents using Self-Organizing Maps".
[7] Kasthurirangan Gopalakrishnan, Siddhartha Khaitan and Anshu Manik
"Enhanced Clustering Analysis and Visulization Using Kohonen's Self-
Organizing Feature Map Network".
[8] http://www.ncbi.nlm.nih.gov/About/primer/genetics_cell.html
[9] http://stemcells.nih.gov/info/basics/basics4.asp
[10] Neural Network Design. By Martin T. Hagan, Howard B. Demuth, Mark
H. Beale.
[11] Nazmul Karim M, Yoshida T, Rivera SL, Saucedo VM, Eikens B, Oh
GS (1997) "Global and local neural network models".
[12] Hishimota Y (1997) Application of artificial neural network and genetic
algorithms to agriculture systems. Compute Electron Agric.
@article{"International Journal of Information, Control and Computer Sciences:57073", author = "Mughal Yar M and Israr Ul Haq and Bushra Noman", title = "Determination of an Efficient Differentiation Pathway of Stem Cells Employing Predictory Neural Network Model", abstract = "The stem cells have ability to differentiated
themselves through mitotic cell division and various range of
specialized cell types. Cellular differentiation is a way by which few
specialized cell develops into more specialized.This paper studies the
fundamental problem of computational schema for an artificial neural
network based on chemical, physical and biological variables of
state. By doing this type of study system could be model for a viable
propagation of various economically important stem cells
differentiation. This paper proposes various differentiation outcomes
of artificial neural network into variety of potential specialized cells
on implementing MATLAB version 2009. A feed-forward back
propagation kind of network was created to input vector (five input
elements) with single hidden layer and one output unit in output
layer. The efficiency of neural network was done by the assessment
of results achieved from this study with that of experimental data
input and chosen target data. The propose solution for the efficiency
of artificial neural network assessed by the comparatative analysis of
“Mean Square Error" at zero epochs. There are different variables of
data in order to test the targeted results.", keywords = "Computational shcmin, meiosis, mitosis, neuralnetwork, Stem cell SOM;", volume = "4", number = "10", pages = "1545-4", }