Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

Abstract

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to "biology process" by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

Identifying noisy functional annotations of proteins using sparse semantic similarity

“…尽管 GO 组委会采用多种质量控制方法来确保各物种 GOA 文件的一 致性与质量 [4,5] , 但已有研究表明, 由于蛋白质功能信息收集来源广泛, GOA 文件中仍然包含一些错 误的蛋白质功能标注信息 (即蛋白质并不具有某些功能却被标注了这些功能), 本文将这些错误标注的 蛋白质功能信息定义为蛋白质的噪声功能标注. 现有蛋白质功能预测工作通常关注预测功能信息完全 未知的蛋白质功能 [11,12] , 增量标注蛋白质的缺失功能 [13,14] 和预测蛋白质的不相关功能 [15∼18] . 这些 工作均假设已知的蛋白质功能标注信息是准确无误的, 忽视了噪声标注信息对功能预测结果的影响.…”

Finding finer functions for partially characterized proteins by protein-protein interaction networks

“…In addition, the current PPI data only covers a small percentage of all the interactions [6,7] , and thus some proteins in a subclass may have no direct interaction links with others in the function-specific subnetwork. For the functional subclasses poorly covered by PPI and annotation data, it might be useful to exploit indirect PPI neighbors and topological information of the PPI subnetwork [30,42] as well as taxonomy similarity of the subclasses [43][44][45] in GO to find detailed protein functions.…”

Evaluation of Anti-Depressant Activity of spirulina, a blue-green algae on Chronic Unpredictable Stress Induced Depression in Rats

“…Continuous stress beginning in adolescence has been linked to anomalies in the endocrine system and brain function as well as long-term somatic consequences in adulthood, according to studies. [9][10][11] The main region of the brain involved in controlling emotion, regulating in learning, and enhancing memory is the hippocampus, which is the primary region responsible for these functions. Additionally, the focalization of depression is complicated and primarily involves CNS and neuroendocrine dysfunction, in addition to neurobiological and morphological alterations across the centres of the brain that are noticeably vulnerable to stress.…”