TY - JOUR
T1 - Computational prediction of protein interaction networks through supervised classification techniques
AU - Fiona, Browne
AU - Wang, HY
AU - Zheng, H
AU - Francisco, Azuaje
PY - 2008/9
Y1 - 2008/9
N2 - This paper implements integrative methods to predict Pairwise (PW) and Module-Based (MB) protein interactions in Saccharomyces cerevisiae. The predictive ability of combining diverse sets of relatively strong and weak predictive datasets is investigated. Different classification techniques: Naive Bayesian (NB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. The assessment demonstrated that as the predictive power of single-source datasets became weaker, MLP and NB performed better than KNN. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, higher-quality datasets with increased interactome coverage and the integration of classification methods.
AB - This paper implements integrative methods to predict Pairwise (PW) and Module-Based (MB) protein interactions in Saccharomyces cerevisiae. The predictive ability of combining diverse sets of relatively strong and weak predictive datasets is investigated. Different classification techniques: Naive Bayesian (NB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. The assessment demonstrated that as the predictive power of single-source datasets became weaker, MLP and NB performed better than KNN. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, higher-quality datasets with increased interactome coverage and the integration of classification methods.
UR - http://www.inderscience.com/browse/index.php
U2 - 10.1504/IJFIPM.2008.020188
DO - 10.1504/IJFIPM.2008.020188
M3 - Article
SN - 1756-2112
VL - 1
SP - 205
EP - 221
JO - International Journal of Functional Informatics and Personalised Medicine
JF - International Journal of Functional Informatics and Personalised Medicine
IS - 2
ER -