Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, 3000 Santa Fe, Argentina.
Center of Research and Development of Information System Engineering, CIDISI, System Engineering Department, UTN-FRSF, 3000 Santa Fe, Argentina.
Bioinformatics. 2018 Apr 1;34(7):1253-1260. doi: 10.1093/bioinformatics/btx730.
The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene expression data.
Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.
The web demo can be found at http://sinc.unl.edu.ar/web-demo/elm-grnnminer/. The source code is available at https://sourceforge.net/projects/sourcesinc/files/elm-grnnminer.
mrubiolo@santafe-conicet.gov.ar.
Supplementary data are available at Bioinformatics online.
从基因谱重建基因调控网络（GRN）在生物信息学中越来越受到关注，有助于理解细胞系统中的复杂调控机制。GRN 明确表示一组基因之间的调节因果关系，其重建是当今具有挑战性的计算问题。已经提出了几种方法，但它们大多数都需要不同的输入源来提供可接受的预测。因此，仅从时间基因表达数据重建 GRN 是一个巨大的挑战。
极限学习机（ELM）是一种新的监督神经模型，近年来由于其学习速度更快，在预测能力方面优于现有监督模型，因此引起了人们的兴趣。这项工作提出了一种用于 GRN 重建的新方法，其中 ELM 用于对基因表达时间序列之间的关系进行建模。使用了众所周知的 DREAM 竞赛基准工具生成的人工数据集。使用具有时间序列的知名 GRN 的真实数据集来验证此新提案。分析了比较方法中 GRN 大小增加的影响。在相同的实验条件下，获得的结果证实了 ELM 方法优于最新的最先进方法的优越性。
网络演示可在 http://sinc.unl.edu.ar/web-demo/elm-grnnminer/ 找到。源代码可在 https://sourceforge.net/projects/sourcesinc/files/elm-grnnminer/ 找到。
mrubiolo@santafe-conicet.gov.ar。
补充数据可在 Bioinformatics 在线获取。
