Inductive logic programming for gene regulation prediction

作     者：Froehler, Sebastian Kramer, Stefan 

作者机构：Tech Univ Munich Inst Informat D-85748 Garching Germany 

出 版 物：《MACHINE LEARNING》 

年 卷 期：2008年第70卷第2-3期

页      面：225-240页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：inductive logic programming relational learning gene regulation gene expression systems biology 

摘      要：We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.