EMT/BSSE: Discovery of Gene and Protein Expression Patterns and Networks

来自测序项目、微阵列研究、基因功能研究、蛋白质-蛋白质相互作用、比较基因组学、结构生物学等领域的大规模数据库正在快速增长。这些数据有多种形式，包括表达式数据、网络/交互数据、本体、文本文档和原始图像/频谱数据。综合挖掘方法，结合这些庞大的公共存储库，是回答重要科学问题的关键组成部分。本研究的重点是开发新的数据挖掘技术，用于综合分析和挖掘来自多个来源的复杂基因/蛋白质表达和相互作用数据集。具体而言，本研究旨在：1)通过布尔表达和相干簇挖掘挖掘基因表达和调控模式。2)挖掘新的蛋白质相互作用和网络模块/基序。3)基于多个全基因组数据集的集成功能关系挖掘。鉴于这种复杂的、网络化的数据在各种领域的扩散，如社交网络、生物网络、语义网等，本研究中开发的方法和算法将广泛适用于其他重要领域。

Large-scale databases from sequencing projects, microarray studies, gene-function studies, protein-protein interactions, comparative genomics, structural biology, and so on are growing at rapid rates. These data come in diverse forms including expression data, network/interaction data, ontologies, text documents, and raw image/spectrum data. An integrated mining approach, combining these vast public repositories, is a crucial component in answering important scientific questions. This research is focusing on developing novel data mining techniques for integrated analysis and mining of complex gene/protein expression and interaction datasets taken from multiple sources. In particular, this research aims at: 1) Mining patterns of gene expression and regulation via Boolean expression and coherent cluster mining. 2) Mining novel protein interactions and network modules/motifs. 3) Integrated functional relationship mining over multiple genome-wide datasets. Given the proliferation of such complex, networked data in a variety of domains such as social networks, biological networks, semantic web, and so on, the methodology and algorithms being developed in this research will be widely applicable to other important areas.