Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA.
Bioinformatics. 2012 Jun 15;28(12):i106-14. doi: 10.1093/bioinformatics/bts232.
The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut-normalized cut prime (FABS-NC(')), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.
We compare the performance of FABS-NC(') to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC(') also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC(') consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC('): In some cases FABS-NC(') produces over half correctly predicted ranking experiment trials than FABS-SVM.
The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.
高通量药物分析（高内涵筛选或 HCS）的最新发展提供了大量定量多维数据。尽管它具有潜力，但它给学术界和行业分析师都带来了一些挑战。特别是直接从成千上万的图像中对几种药物的有效性进行排名更是如此。本文首次介绍了一种自动对药物性能进行排序的新框架，称为分数调整二分评分（FABS）。这种通用策略利用基于图的公式和解决方案，并避免了传统方法在实践中的许多缺陷。我们通过用特定算法实现 FABS 框架来进行实验，这是归一化割归一化割优先（FABS-NC(')）的一种变体，产生药物的排序。众所周知，这种算法在多项式时间内运行，因此可以很好地扩展到高通量应用中。
我们将 FABS-NC(')的性能与其他可用于药物排序的方法进行了比较。我们设计了 FABS 算法的两个变体：利用支持向量机（SVM）作为黑盒的 FABS-SVM，以及利用特征向量技术（谱）作为黑盒的 FABS-Spectral。我们还将 FABS-NC(')的性能与之前考虑过的三种其他方法进行了比较：中心排名（Center）、主成分分析排名（PCA）和图过渡能量方法（GTEM）。结论令人鼓舞：FABS-NC(')始终优于所有这五种替代方法。FABS-SVM 在这六种方法中性能排名第二，但远远落后于 FABS-NC(')：在某些情况下，FABS-NC(')比 FABS-SVM 产生的正确预测排序实验试验次数多一半。
本文接受发表后，将根据作者的要求提供评估中使用的系统和数据。
