An FPT Approach for Predicting Protein Localization from Yeast Genomic Data
文献类型：期刊论文
| 作者 | Wang J ; Li CH ; Wang EK ; Wang XD |
| 刊名 | plos one
 |
| 出版日期 | 2011 |
| 卷号 | 6期号:1页码:1-11 |
| 关键词 | SACCHAROMYCES-CEREVISIAE SUBCELLULAR-LOCALIZATION BAYESIAN NETWORKS EXPRESSION DATA MEMBRANE-PROTEINS DATA INTEGRATION GENE-EXPRESSION SIGNAL PEPTIDES IDENTIFICATION SITES |
| ISSN号 | 1932-6203 |
| 通讯作者 | wang j |
| 中文摘要 | accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long-standing questions such as that of protein localization. here, we develop a frequent pattern tree (fpt) approach to generate a minimum set of rules (mfpt) for predicting protein localization. we acquire a series of rules according to the features of yeast genomic data. the mfpt prediction accuracy is benchmarked against other commonly used methods such as bayesian networks and logistic regression under various statistical measures. our results show that mfpt gave better performance than other approaches in predicting protein localization. meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mfpt predicted differently than the simple naive bayesian method (snb). in our analysis of these 138 proteins, we present novel predictions for the location for 17 proteins, which currently do not have any defined localization. these predictions can serve as putative annotations and should provide preliminary clues for experimentalists. we also compared our predictions against the eukaryotic subcellular localization database and related predictions by others on protein localization. our method is quite generalized and can thus be applied to discover the underlying rules for protein-protein interactions, genomic interactions, and structure-function relationships, as well as those of other fields of research. |
| 收录类别 | SCI收录期刊论文 |
| 语种 | 英语 |
| WOS记录号 | WOS:000286520600001 |
| 公开日期 | 2012-06-11 |
| 源URL | [http://ir.ciac.jl.cn/handle/322003/44877]  |
| 专题 | 长春应用化学研究所_长春应用化学研究所知识产出_期刊论文 |
| 推荐引用方式 GB/T 7714 | Wang J,Li CH,Wang EK,et al. An FPT Approach for Predicting Protein Localization from Yeast Genomic Data[J]. plos one,2011,6(1):1-11. |
| APA | Wang J,Li CH,Wang EK,&Wang XD.(2011).An FPT Approach for Predicting Protein Localization from Yeast Genomic Data.plos one,6(1),1-11. |
| MLA | Wang J,et al."An FPT Approach for Predicting Protein Localization from Yeast Genomic Data".plos one 6.1(2011):1-11. |
入库方式： OAI收割
来源：长春应用化学研究所
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