Fondazione Bruno Kessler, Trento, Italy.
PLoS One. 2011;6(12):e28646. doi: 10.1371/journal.pone.0028646. Epub 2011 Dec 28.
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.
RegnANN 是一种基于多层感知器（MLP）集成的反向工程基因网络的新方法。该算法为网络中的每个基因构建一个回归器，独立估计其邻域。通过连接所有邻域，可以获得整个网络。RegnANN 对变量之间关系的性质没有任何假设，可能会捕捉到表达模式之间的高阶和非线性相关性。评估集中在模拟较大网络的合理子模块的合成数据和由大肠杆菌的子模块组成的生物数据上。我们同时考虑了 Barabasi 和 Erdös-Rényi 拓扑以及两种数据生成方法。我们验证了网络大小和数据量等因素对推断算法准确性的影响。使用 RegnANN 获得的准确率分数与 ARACNE、CLR 和 KELLER 三种参考算法的性能进行了系统比较。我们的评估表明，RegnANN 与测试的推断方法相比具有优势。RegnANN 的稳健性、发现二阶相关性的能力以及在合成数据和生物数据上使用此新方法获得的结果之间的一致性都很有前景，这激发了它在更广泛问题上的应用。
