2010/09/24 by Marco Grimaldi, Giuseppe Jurman, Roberto Visintainer
Biochemistry, Genetics and Molecular Biology · #Artificial intelligence #Artificial neural network #Bioinformatics and Genomic Networks #Biology #Computer network #Computer science #Data mining #Gene #Gene Regulatory Network Analysis #Gene expression #Gene expression and cancer classification #Gene regulatory network #Genetics #Inference #Machine learning #Multilayer perceptron #Network topology #Perceptron #Reverse engineering #Software #Stability (learning theory) #Task (project management) #Variety (cybernetics) #q-bio.MN
paper · pdf · doi:10.1371/journal.pone.0028646
published as PLoS ONE 6(12): e28646 (2011)
arxiv created 2010/09/24 · openalex publication_date 2011/12/28 · arxiv updated 2012/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.