使用新的线性模型和词邻近网络揭示摘要和文本中的蛋白质相互作用
Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
摘要：
我们参加了第二届BioCreative挑战赛的三个蛋白质-蛋白质相互作用子任务：与蛋白质-蛋白质相互作用相关的摘要分类（IAS），蛋白质对的发现（IPS）和全文文档中表征蛋白质相互作用的文本段落（ISS）。我们采用了一种新的、轻量级的线性模型来处理抽象分类任务，该模型的灵感来自垃圾邮件检测技术，以及一种基于不确定性的集成方案。我们还使用了支持向量机和奇异值分解相同的功能进行比较。我们的方法，全文子任务（蛋白质对和通道识别）包括一个基于词邻近网络的特征扩展方法。就挑战评估中使用的性能指标（准确性、F分数和AUC）而言，我们对抽象分类任务（IAS）的方法是该任务的最佳提交方法之一。我们还报告了我们使用我们的方法制作的网络工具：蛋白质相互作用抽象相关性评估器（PIARE）。我们对全文任务的方法导致了最高的召回率以及正确段落的平均倒数排名。我们的抽象分类方法表明，一个简单的线性模型，使用相对较少的功能，是能够概括和揭示的概念性质的蛋白质-蛋白质相互作用的书目。由于新的方法是基于一个非常轻量级的线性模型，它可以很容易地移植和应用到类似的问题。在全文问题中，扩展字的功能与字邻近网络被证明是有用的，虽然需要一些改进进行了讨论。
英文摘要：
We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
