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| 1 | Identifying drug-target proteins based on network features显示文摘Proteins rarely function in isolation inside and outside cells, but operate as part of a highly intercon- nected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action in terms of informatics. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interac- tion network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins in the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been dem- onstrated to be drug-target proteins in the literature. | ZHU MingZhu1, GAO Lei1, LI Xia1,2 & LIU ZhiCheng1 1 School of Biomedical Engineering, Capital Medical University, Beijing 100069, China 2 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China | 2009 | Science China(Life Sciences)2009，52，4： | 3 |
| 2 | Identifying cancer genes from cancer mutation profiles by cancer functions显示文摘It is of great importance to identify new cancer genes from the data of large scale genome screenings of gene mutations in cancers. Considering the alternations of some essential functions are indispensable for oncogenesis, we define them as cancer functions and select, as their approximations, a group of detailed functions in GO (Gene Ontology) highly enriched with known cancer genes. To evaluate the efficiency of using cancer functions as features to identify cancer genes, we define, in the screened genes, the known protein kinase cancer genes as gold standard positives and the other kinase genes as gold standard negatives. The results show that cancer associated functions are more efficient in identifying cancer genes than the selection pressure feature. Furthermore, combining cancer functions with the number of non-silent mutations can generate more reliable positive predictions. Finally, with precision 0.42, we suggest a list of 46 kinase genes as candidate cancer genes which are annotated to cancer functions and carry at least 3 non-silent mutations. | LI YanHui1, GUO Zheng1,2, PENG ChunFang2, LIU Qing2, MA WenCai2, WANG Jing2, YAO Chen2, ZHANG Min2 & ZHU Jing1 1 Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China 2 School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China | 2008 | Science China(Life Sciences)2008，51，6： | 1 |
| 3 | Finding finer functions for partially characterized proteins by protein-protein interaction networks显示文摘Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins. | LI YanHui GUO Zheng MA WenCai YANG Da WANG Dong ZHANG Min ZHU ding ZHONG GuoCai LI YongJin YAO Chen WANG Jing | 2007 | Chinese Science Bulletin2007，52，24： | 0 |
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