IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan.
Bioinformatics. 2009 Nov 15;25(22):2962-8. doi: 10.1093/bioinformatics/btp494. Epub 2009 Aug 17.
The existing supervised methods for biological network inference work on each of the networks individually based only on intra-species information such as gene expression data. We believe that it will be more effective to use genomic data and cross-species evolutionary information from different species simultaneously, rather than to use the genomic data alone.
We created a new semi-supervised learning method called Link Propagation for inferring biological networks of multiple species based on genome-wide data and evolutionary information. The new method was applied to simultaneous reconstruction of three metabolic networks of Caenorhabditis elegans, Helicobacter pylori and Saccharomyces cerevisiae, based on gene expression similarities and amino acid sequence similarities. The experimental results proved that the new simultaneous network inference method consistently improves the predictive performance over the individual network inferences, and it also outperforms in accuracy and speed other established methods such as the pairwise support vector machine.
The software and data are available at http://cbio.ensmp.fr/~yyamanishi/LinkPropagation/.
现有的基于监督的生物网络推断方法仅基于种内信息（如基因表达数据）对每个网络进行处理。我们认为，同时使用来自不同物种的基因组数据和跨物种进化信息将更加有效，而不仅仅是使用基因组数据。
我们创建了一种新的半监督学习方法，称为链接传播，用于基于全基因组数据和进化信息推断多个物种的生物网络。该新方法应用于基于基因表达相似性和氨基酸序列相似性同时重建秀丽隐杆线虫、幽门螺杆菌和酿酒酵母的三个代谢网络。实验结果证明，新的同时网络推断方法始终优于个体网络推断的预测性能，并且在准确性和速度方面也优于其他已建立的方法，如成对支持向量机。
软件和数据可在 http://cbio.ensmp.fr/~yyamanishi/LinkPropagation/ 上获得。
