TY  - JOUR
A1  - Hake, Tim
A1  - Bodenberger, Bernhard
A1  - Groth, Detlef
T1  - In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks
T2  - Human biology and public health
N2  - The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.
KW  - Python
KW  - correlation
KW  - network reconstruction
KW  - bootstrap
KW  - St. Nicolas House Algorithm
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/60082
SN  - 2748-9957
VL  - 1
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  -