Chen Jin, Chua Hon Nian, Hsu Wynne, Lee Mong-Li, Ng See-Kiong, Saito Rintaro, Sung Wing-Kin, Wong Limsoon
National University of Singapore, Graduate School of Computing, Singapore.
Genome Inform. 2006;17(2):284-97.
High-throughput experimental methods, such as yeast-two-hybrid and phage display, have fairly high levels of false positives (and false negatives). Thus the list of protein-protein interactions detected by such experiments would need additional wet laboratory validation. It would be useful if the list could be prioritized in some way. Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are reviewed in this paper, with a focus on techniques that rely only on topological information of the protein interaction network derived from such high-throughput experiments. In particular, we discuss indices that are abstract mathematical characterizations of networks of reliable protein-protein interactions--e.g., "interaction generality" (IG), "interaction reliability by alternative pathways" (IRAP), and "functional similarity weighting" (FSWeight). We also present indices that are based on explicit motifs associated with true-positive protein interactions--e.g., "new interaction generality" (IG2) and "meso-scale motifs" (NeMoFinder).
高通量实验方法，如酵母双杂交和噬菌体展示，具有相当高的假阳性（和假阴性）水平。因此，通过此类实验检测到的蛋白质-蛋白质相互作用列表需要额外的湿实验室验证。如果能以某种方式对该列表进行优先级排序将很有用。本文综述了评估通过此类高通量方法检测到的蛋白质-蛋白质相互作用可靠性的计算技术进展，重点关注仅依赖于从此类高通量实验得出的蛋白质相互作用网络拓扑信息的技术。特别是，我们讨论了作为可靠蛋白质-蛋白质相互作用网络抽象数学表征的指标——例如，“相互作用通用性”（IG）、“通过替代途径的相互作用可靠性”（IRAP）和“功能相似性加权”（FSWeight）。我们还提出了基于与真阳性蛋白质相互作用相关的明确基序的指标——例如，“新相互作用通用性”（IG2）和“中尺度基序”（NeMoFinder）。
