A standard computer with a modern processor, 16 GB RAM, 1 TB hard drive, and the Windows 10, Linux, or macOS operating system can be used. The KNIME Analytics Platform must be installed following the instructions at https://www.knime.com/installation. After the basic installation, specific extensions must be installed to run the TM workflows. As shown in Figure 1A, these extensions must be added from within KNIME by selecting “Install KNIME Extensions” from the File menu. A new installation window opens with the option to type in a keyword (Figure 1B). The keyword “text” is sufficient to display the KNIME Text-processing extension (English nodes are the default, but it is possible to select nodes to perform TM in other languages). This extension must be selected and installed. Similarly, by typing “vernalis” it is possible to install KNIME Community Extensions-Cheminformatics with the KNIME Vernalis nodes, and by typing “indexing,” one can install the KNIME Labs Extensions with the KNIME Indexing and Searching node.

The indexed information is retrieved from the ePMC site using the European PubMed Central Advanced Search KNIME node (Figure 1). It is recommended to perform an Advanced Search at https://europepmc.org/advancesearch using keywords and filters on the fields of interest. The resulting syntax in the Advanced Search windows may be copied and pasted into the General Query field in the node configuration window (Figure 1C). In the example, the syntax (“haemolytic uraemic syndrome” OR “hemolytic uremic syndrome”) was used to include two spellings of search terms for HUS publications. The years of publication were limited to 2020–2021. After the execution of this first node, the XPath node was used to select the information to work in, as exemplified by the paths shown in Figure 1D. Note that for the affiliation field, the string type CollectionCell must be specified. The Table Indexer node (Figure 1E) allows the user to select columns to index the information that will be consulted in the following Index Query node. This central node allows the user to obtain the necessary information for further processing. Changing the syntax of the query text makes it possible to evaluate publications per year, the most and least common journals chosen by the authors, the list of authors, the countries where the authors work, the number of publications per author, and so on (Figure 1F). It also allows the user to obtain the full text of the titles and the abstracts. Some examples of syntax in the query text in the KNIME Index Query node are shown in Table 1. Specifically, abstracts are used to obtain the linguistic corpus of interest. Statistics and corresponding graphs can be made in KNIME (using Value Counter, Sorter, Scatter Plot and other specific nodes) or by exporting the data to any statistical software.

To obtain the full list of authors, we recommend the workflow shown in Figure 2. After a specific index query, the authorString field constitutes the corpus to extract information by successive application of nodes.

The automatic clustering of topics and the retrieval of a bag of words are two powerful tools for analyzing a corpus consisting of all the abstracts of publications; they are counted, depending on each query, in hundreds, thousands or even millions of items. The Topic Extractor node is used for the automatic and unsupervised detection of topics and keywords after an index query and a preprocessing of the text of the corpus (as detailed in Figure 3, fork 1). Topic extraction is based on a simple parallel threaded implementation of latent Dirichlet allocation (LDA), following Newman et al. [9], with a sparse LDA sampling scheme and data structure from Yao et al. [10]. This technique uses the Machine Learning for Language Toolkit (MALLET) topic modeling library.

The above-mentioned text preprocessing technique can be used to create a bag of words from a set of documents through the Bag of Words Creator node (Figure 3, fork 2). The use of different nodes results in table presentations or graphically ordered representations (as clouds of words), as shown in Figure 3.

Thematic clustering in our example showed a clear differentiation between HUS (topics 2 and 3) and aHUS (topics 0, 1, and 4), as shown in both the table and word cloud outputs.

As noted, the corpus of abstracts contains valuable information, which can be cross-referenced with external data sources to separate and rank data of interest. The workflow in Figure 4 shows a cross-check between the abstracts of articles on HUS published in 2020 and 2021 and the list of the Food and Drug Administration (FDA) approved drugs (https://www.fda.gov/drugs/development-approval-processdrugs/drug-approvals-and-databases). This workflow makes it possible to detect mentions of some of these drugs in abstracts. In fact, the corpus could be cross-checked with any other list of interest in the same way.