Gene Functional Prediction from Protein-Protein Interaction Data

佛罗里达国际大学获得了一笔拨款，用于开发帮助预测蛋白质-蛋白质相互作用的计算工具。在许多细胞过程中，蛋白质与其他蛋白质成对或作为较大复合物的组分相互作用，包括代谢，内分泌和外分泌，信号传导，合成和运输。蛋白质-蛋白质相互作用的系统鉴定、表征和理解将提供必要的知识基础，并将蛋白质组动力学和结构与细胞功能和表型联系起来。分析PPI数据的最重要应用之一是预测未表征蛋白质的功能注释。未表征蛋白质（也称为未注释蛋白质）是指我们不知道其功能的蛋白质。蛋白质的功能注释是后基因组时代的一个基本问题。然而，目前可用的计算工具的功能注释使用PPI需要改进。为了帮助解决这个问题，该项目将开发用于分析蛋白质-蛋白质相互作用（PPI）数据的高级聚类方法，并将开发用于整合基因表达谱和蛋白质-蛋白质相互作用数据的计算方法，这将提高预测蛋白质相互作用模块的鲁棒性和准确性。该项目将研究和开发用于评估PPI数据可靠性的方法;研究和开发用于结合多个，多样化和独立聚类结果的有效方法，以提高鉴定的质量和鲁棒性;研究和开发用于整合蛋白质相互作用数据和基因表达谱的方法。该项目将促进开发新的教育工具，并将涉及本科生和研究生。此外，佛罗里达国际大学（FIU）是美国西班牙裔学生的顶级学位授予者之一，该项目将通过促进代表性不足的群体参与其研究工作来建立这一强大的历史。有关该项目的其他信息可以在http://www.cs.fiu.edu/~taoli上找到。

The Florida International University has been awarded a grant to develop computational tools to help predict protein - protein interactions. Proteins interact with other proteins either in pairs or as components of larger complexes in many cellular processes, including metabolism, endocrine and exocrine, signaling, synthesis, and transport. A systematic identification, characterization and understanding of protein-protein interactions will provide an essential knowledge base, and link proteome dynamics and architecture to cellular function and phenotype. One of the most important applications of analyzed PPI data is to predict functional annotation for uncharacterized proteins. Uncharacterized proteins (also called unannotated proteins) refer to proteins for which we do not know their functions. Functional annotation of proteins is a fundamental problem in the post-genomic era. However, currently available computational tools for functional annotation using PPI need to be improved. To help solve this problem this project will developing advanced clustering methods for analyzing protein-protein interaction (PPI) data and will develop computational methods for integrating gene expression profiles and protein-protein interaction data which will improve the robustness and accuracy of predicting protein interaction modules. The project will Investigate and develop methods for evaluating the reliability of PPI data; Investigate and develop effective methods for combining multiple, diverse and independent clustering results to improve the quality and robustness of identification; and Investigate and develop methods for integration of protein interaction data and gene expression profiles. This project will facilitate the development of novel educational tools and will involve both undergraduate and graduate students. Moreover, Florida International University (FIU) is among the top awarders of degrees to Hispanic students in US and this project will build on this strong history by promoting the participation of underrepresented groups in its research efforts.Additional information about the project can be found at http://www.cs.fiu.edu/~taoli.