大规模生物网络对齐的随机块坐标Frank-Wolfe算法。

EURASIP journal on bioinformatics & systems biology Pub Date : 2016-04-08 eCollection Date: 2016-12-01 DOI:10.1186/s13637-016-0041-1
Yijie Wang, Xiaoning Qian
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引用次数: 6

摘要

随着生物医学研究中可用的“大”数据越来越多,从这些大数据中获得准确和可重复的生物学知识给计算带来了巨大的挑战。在本文中,受最近发展的随机块坐标算法的启发,我们提出了一种高度可扩展的随机块坐标Frank-Wolfe算法,用于具有一般紧性凸约束的凸优化,该算法在分析生物医学数据以更好地理解细胞和疾病机制方面具有多种应用。我们专注于实现衍生的随机块坐标算法,以对齐蛋白质-蛋白质相互作用网络,以识别基于IsoRank框架的保守功能途径。我们推导的随机块坐标Frank-Wolfe (SBCFW)算法具有收敛性保证,并且每次迭代的计算成本(时间和空间)自然会降低。我们在酵母网络中查询保守功能蛋白复合物的实验证实了这种技术在分析大规模生物网络中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Stochastic block coordinate Frank-Wolfe algorithm for large-scale biological network alignment.

With increasingly "big" data available in biomedical research, deriving accurate and reproducible biology knowledge from such big data imposes enormous computational challenges. In this paper, motivated by recently developed stochastic block coordinate algorithms, we propose a highly scalable randomized block coordinate Frank-Wolfe algorithm for convex optimization with general compact convex constraints, which has diverse applications in analyzing biomedical data for better understanding cellular and disease mechanisms. We focus on implementing the derived stochastic block coordinate algorithm to align protein-protein interaction networks for identifying conserved functional pathways based on the IsoRank framework. Our derived stochastic block coordinate Frank-Wolfe (SBCFW) algorithm has the convergence guarantee and naturally leads to the decreased computational cost (time and space) for each iteration. Our experiments for querying conserved functional protein complexes in yeast networks confirm the effectiveness of this technique for analyzing large-scale biological networks.

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From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. On biometric systems: electrocardiogram Gaussianity and data synthesis. BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition. Review of stochastic hybrid systems with applications in biological systems modeling and analysis. Bayesian inference for biomarker discovery in proteomics: an analytic solution.
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