Extraction of Transcript Diversity from Scientific Literature

Transcript diversity generated by alternative splicing and associated mechanisms contributes heavily to the functional complexity of biological systems. The numerous examples of the mechanisms and functional implications of these events are scattered throughout the scientific literature. Thus, it is crucial to have a tool that can automatically extract the relevant facts and collect them in a knowledge base that can aid the interpretation of data from high-throughput methods. We have developed and applied a composite text-mining method for extracting information on transcript diversity from the entire MEDLINE database in order to create a database of genes with alternative transcripts. It contains information on tissue specificity, number of isoforms, causative mechanisms, functional implications, and experimental methods used for detection. We have mined this resource to identify 959 instances of tissue-specific splicing. Our results in combination with those from EST-based methods suggest that alternative splicing is the preferred mechanism for generating transcript diversity in the nervous system. We provide new annotations for 1,860 genes with the potential for generating transcript diversity. We assign the MeSH term “alternative splicing” to 1,536 additional abstracts in the MEDLINE database and suggest new MeSH terms for other events. We have successfully extracted information about transcript diversity and semiautomatically generated a database, LSAT, that can provide a quantitative understanding of the mechanisms behind tissue-specific gene expression. LSAT (Literature Support for Alternative Transcripts) is publicly available at http://www.bork.embl.de/LSAT/

Unsupervised Extraction of Representative Concepts from Scientific Literature

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we propose PhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201

Scientific Literature

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Systematic review of 15 years of scientific literature on public procurement

As public procurement research is fragmented in many sub-topics, a clear synthesis is lacking, which in turn inhibits the establishment of a clear body of knowledge. To start filling this gap, this systematic review provides an overview of the most influential literature in the field of public procurement. The findings are aimed at providing other researchers with relevant information to synthesize existing findings. We found that public procurement research is maturing, with increasing attention from diverse scientific disciplines. USA and UK are most productive publishers, while European countries have become increasingly active. Although a wide spectrum of research designs have been utilized, the reviewed articles focused on few. For example, although articles addressed twenty different topics, eleven were only studied once or twice, while 61.4% of papers researched the topic of procurement strategies. Considerable variations were observed across countries, indicating different research foci, as well as varying levels of maturity per research characteristic. Research seems to have underused existing scientific knowledge in that literature and meta-studies were only utilized in 13.2% and 5% of papers respectively. Practical applicability of research findings is inhibited by a detected imprecision of research, such as not specifying the procuring government level in 56.1% of reviewed papers. The overall conclusion with respect to the maturity level of public procurement research is that while various different paths have been laid, most researchers continued to walk the main roads. To develop the field further, we recommend to research the field from more diverse angles

Ten Simple Rules for Searching and Organizing the Scientific Literature

The exponentially increasing number of published papers (1.4 million per year by one estimate) makes it more and more difficult for us to manage the flood of scientific information. Each of us has acquired some protocol to find and organize journal articles and other references over the course of our careers. Most of those protocols are likely to have been formed by old routines or idleness rather than a structured approach to save time and frustration over the long run. Furthermore, with the Web 2.0 revolution, new ways of handling information are emerging (O’Reilly 2005). For example, traditional standalone tools for reference management like EndNote are being supplemented by centralized resources like RefWorks and social bookmarking sites as described subsequently. This fusion of personal and public information offers the promise of efficiency through better organization, which in turn leads to better science.

How can seasoned scientists do better using these tools and those newer to the field start off in the right way? To start to answer that question, I present ten simple rules to master the search and organization of new literature. This is not meant to be comprehensive. It represents the experiences of a few and I welcome your thoughts, through comments to this article, on what you do to keep your references organized.

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Embedding-based Scientific Literature Discovery in a Text Editor Application

Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author's manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (https://SciEditorDemo2020.herokuapp.com/) and a short video tutorial (https://youtu.be/pkdVU60IcRc) are available online