Incorporating Image-based Features into Biomedical Document Classification

这项拟议的研究旨在开发和推进除文本外还使用科学出版物中出现的图像数据的工具，以支持有益的、有针对性的生物医学文献的获取。生物医学出版物的数量以每年100多万种新出版物的速度增长。识别相关信息需要科学家和医生每天扫描无数的论文。对于科学数据库馆长(杰克逊实验室或UniProt等组织中的生物馆长)来说，这项任务尤其繁重，因为他们必须确定对数据库最重要的文章，在其中找到关于疾病、基因/蛋白质和突变的高质量证据，并对数据库条目中的发现和文章中相关证据的参考进行整理。值得注意的是，出版物中的大部分证据都是数字。因此，图像是相关性的丰富和基本指标。 虽然正在开发生物医学文本挖掘工具，以加快在出版物内搜索信息，但一些相互竞争的共同任务突出表明，需要更有效的工具来克服生物管理和科学发现的瓶颈。此外，生物策展人指出了图像作为关键信息来源的重要性。虽然图像分析是一个活跃的研究领域，但目前大多数生物医学图像处理的工作集中在图像识别、理解和索引上，而不是将图像作为文档分析的辅助工具。同样，大多数关于生物医学文献挖掘的工作都只关注文本。因此，除了文字以外，迄今在利用出版物内的图像提供有关文章所载信息的相关性的重要线索方面所做的工作很少。 我们的前提是，在生物馆长经验的支持下，来自图像的信息可以(也应该)直接纳入生物医学文档检索和分类，并将改进相关文章的准确识别(针对特定用户的需求)，同时指出其中的重要证据。我们将全面识别、开发和比较信息丰富的图像特征，开发基于这些特征的图像和文档表示方法和工具，并引入将基于图像的数据有效地整合到基于文本的文档分类过程中的手段。这项工作将包括以下基本任务：a)建立从PDF文章中获取图像的强大工具，并将复合图形分割成单独的图像面板；b)识别和研究生物医学图像表示的高信息量特征，并将生物医学图像分类为重要的类型和类别；c)使用文本和图像有效地表示文档，并整合基于文本和基于图像的分类器。我们的研究立足于真正的需求，确保对大量图像数据的访问，并通过在与我们合作的研究所内的几个广泛和不同的管理领域进行工作，努力使结果具有广泛的适用性：老鼠(杰克逊实验室)和蠕虫(蠕虫)的基因表达和表型证据，以及蛋白质-蛋白质相互作用的实验证据(蛋白质信息资源)。该项目的工作将产生新的方法和工具，利用图像和文本数据，促进更有效和更有重点的检索和挖掘，从而更好地支持生物管理和数据密集型生物医学发现。

The proposed research aims to develop and advance tools for using image-data appearing in scientific publications, in addition to text, in order to support beneficial, targeted access to the biomedical literature. The number of biomedical publications grows at a rate of over one million new publications per year. Identifying relevant information requires scientists and physicians to scan daily through a myriad of papers. For scientific database curators (bio-curators, in organizations such as Jackson Labs or UniProt), the task is particularly onerous, as they must identify articles most significant to the database, locate within them high-quality evidence concerning disease, genes/proteins and mutations, and curate the findings in database entries along with references to relevant evidence in the articles. Notably, much of the evidence within publications lies in figures. Thus, images are rich and essential indicators for relevance. While biomedical text mining tools are being developed to expedite search for information within publications, several competitive shared tasks underscored the need for more effective tools to overcome the bottleneck for bio-curation and for scientific discovery. Moreover, bio-curators point-out the importance of images as a key information source. While image analysis is an active research field, most current work on biomedical image processing focuses on image identification, understanding and indexing; Not on images as aids to document analysis. Similarly, most work on biomedical literature mining focuses on text alone. Thus, little has been done so far to utilize, in addition to text, images within publications that provide important cues about the relevance of the information embedded in articles. Our premise, supported by bio-curators experience, is that information derived from images can (and should) be directly incorporated into biomedical document retrieval and classification, and will improve accurate identification of relevant articles (for a given user’s needs) while pin-pointing significant evidence within them. We will comprehensively identify, develop and compare informative image-features, develop methods and tools for representing both images and documents based on such features, and introduce means to effectively integrate image-based data into the text-based document classification process. The work will comprise the following fundamental tasks: A) Building robust tools for harvesting images from PDF articles and segmenting compound figures into individual image-panels; B) Identification and investigation of highly-informative features for biomedical image-representation, and categorization of biomedical images into significant types and classes; C) Effective representation of documents using text and image, and integration of text-based and image-based classifiers. We anchor our research in genuine needs, secure access to much image data, and strive for broad-applicability of the results, by working within several broad and diverse curation-areas within institutes with which we collaborate: Evidence for gene-expression & phenotypes in Mouse (Jackson Labs) and in worm (WormBase), and experimental evidence for protein-protein interaction (Protein Information Resource). The work on this project will result in new methods and tools that take advantage of both image- and text-data, facilitating more effective and focused retrieval and mining, thus better supporting bio-curation and data-intensive biomedical discovery.