Generative and Discriminative Methods for Gene Finding and Functional Annotation

该项目完成的任务分为两个阶段，分别对应于基因预测和功能注释两个任务。在第一阶段，开发并应用了一个基因发现系统。这个系统被设计成在它所建模的基因特征、它所采用的机器学习算法以及它从中学习的实验数据范围方面是可伸缩和可扩展的。该项目首先通过将该系统应用于线虫的完整基因组来验证该系统，然后为识别人类DNA中的基因这一更困难的任务重新训练该系统。该项目的第二阶段由两部分组成。首先，将第一阶段用于基因发现系统的软件框架推广到相关蛋白质家族的建模。其次，为了从非连续数据中学习，该项目使用一种名为支持向量机(支持向量机)的区分学习方法开发了函数分类技术。由基于序列的建模系统计算的统计量用作支持向量机系统使用的一组特征。其他特征将来自DNA微阵列实验、每个基因的上游启动子区域、系统发育谱以及与已知蛋白质家族的相似性分数。

The tasks accomplished by this project are divided into two phases corresponding to the two tasks of gene prediction and functional annotation. In the first phase, a gene-finding system is developed and applied. This system is designed to be scalable and extensible with respect to the gene features it models, the machine learning algorithms it employs, and the range of experimental data from which it learns. This project first validates the system by applying it to the complete C.elegans genome, and then retrains the system for the more difficult task of recognizing genes in human DNA. The second phase of this project consists of two parts. First, the software framework used for the gene finding system from phase one is generalized to model families of related proteins. Second, in order to learn from non-sequential data, the project develops functional classification techniques using a discriminative learning method called support vector machines (SVM's). The statistics calculated by the sequence-based modeling system functions as one set of features used by the SVM system. Additional features will come from DNA microarray experiments, the upstream promoter region of each gene, phylogenetic profiles and similarity scores to known protein families.