Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome.
Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome.
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整合一组已知的人类蛋白质-蛋白质相互作用，为人类相互作用组的大规模绘图做好准备。
DOI：
10.1186/gb-2005-6-5-r40
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发表时间：
2005
期刊：
影响因子：
12.3
通讯作者：
Marcotte, EM
中科院分区：
文献类型：
--
作者：
Ramani, AK;Bunescu, RC;Mooney, RJ;Marcotte, EM
为了巩固已知的人类蛋白质相互作用，开发了两个测试来测量可用相互作用数据的相对准确度。此外，从Medline摘要中恢复了3，737种人类蛋白质之间的6，580种相互作用，并与现有的相互作用数据相结合，以获得7，748种人类蛋白质之间的31，609种相互作用的网络，其准确度与现有数据集相同。人们正在为酵母、蠕虫和苍蝇构建广泛的蛋白质相互作用图谱，以研究蛋白质如何组织成通路和系统，但对于人类蛋白质组，还没有这样的全基因组相互作用图谱。为了准备在人类中的研究，我们希望建立测试的准确性，未来的相互作用测定，并巩固已知的人类蛋白质之间的相互作用。我们建立了人类蛋白质相互作用数据集准确性的两个测试，并测量了可用数据的相对准确性。然后，我们开发并应用自然语言处理和文献挖掘算法，从Medline摘要中恢复了3，737种人类蛋白质之间的6，580种相互作用。使用三部分算法：首先，使用基于条件随机场的搜索引擎在Medline摘要中识别人类蛋白质名称，然后通过Medline摘要集上蛋白质名称的共现来识别相互作用，使用贝叶斯分类器过滤相互作用以丰富合法的物理相互作用。这些挖掘的相互作用与现有的相互作用数据相结合，获得了7，748种人类蛋白质之间的31，609种相互作用的网络，其准确度与现有数据集相同。这些相互作用和准确性基准将有助于解释当前的功能基因组学数据，并为确定未来大规模人类蛋白质相互作用测定的质量提供基础。从最佳采样相互作用集中每种蛋白质约15种相互作用预测到估计的25，000个人类基因，意味着在完整的人类蛋白质相互作用网络中有超过375，000种相互作用。因此，这一组不超过整个网络的10%。
In order to consolidate the known human proteins interactions two tests were developed to measure the relative accuracy of the available interaction data. In addition, 6,580 interactions among 3,737 human proteins were recovered from Medline abstracts and combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing data sets. Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins. We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets. These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network.
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影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
30.8
作者：
Jenssen, TK;Lægreid, A;Hovig, E
通讯作者：
Hovig, E
影响因子：
56.9
作者：
Li, SM;Armstrong, CM;Vidal, M
通讯作者：
Vidal, M
影响因子：
14.9
作者：
Bader, GD;Betel, D;Hogue, CWV
通讯作者：
Hogue, CWV
影响因子：
21.3
作者：
Bouwmeester, T;Bauch, A;Superti-Furga, G
通讯作者：
Superti-Furga, G