On this page the relations are shown between your concept and the vocabularies that are selected in the left panel. The total number of hits with your concept and the total of terms in the vocabulary are displayed in parenthesis.
Relations The table that is generated indicates per column:
The Search field can be used to search in the entire table for a keyword. Selecting a row in the table shows the abstracts in the right results panel. Abstracts in the table can be selected by clicking one or more rows in this table and added to your Abstract Set by pressing the Add selected abstracts to Abstract Set. The collected abstracts can be visited anytime via the My Abstracts tab. The selection in My Set does not change in case search conditions are altered.
Clicking the button ‘Add Concept to KMAP Multi Set’ when a concept is selected in the table add this concept to your collected concept set which you can further explore using the KMAP-Multi tab.
Wordclouds The wordcloud is a visual representation of the data in the Relations table. The font size for each concept is proportional to the Score. The sliders can be used to select the maximum number of words per category and the scaling of the fonts.
Network This network shows the data from the Relations in a network with the selected concept in the center and up to a number of 50 other concepts per category. These concepts (dots with their name inside) have a different colour dependent on the type (same colors used as for the worldclouds ). In the righthand above corner a bar called ‘number of connections per category’ can be used to create and alter the network picture using between 0 and 50 concepts per category. The thickness of the arrows between the nodes indicate the relationship between the concepts. The thicker the node the higher the score on abstracts in which the two co-occur. By clicking on the arrow you are redirected to the TenWise server that depicts the abstracts with the co-occurrences. By clicking on the nodes you are redirected to the TenWise server that depicts the abstracts on this single concept. Finally the network shape can be changed manually by dragging the nodes across the screen. Screenshots can be made and used for reporting ed.
This pages shows abstracts for your selected concept, ranked between 0 and 1 for various types of evidence. The score is determined by running a previously trained Random Forest model on the abstracts. Evidence is defined as the biological context of the abstract in which the concept appears. For example, an abstract with a score of 1 for clinical, is most likely about a clinical trial, or an observational study with patients. Likewise, an abstract with a high score for mouse is most likely about a mouse experimental model. The type of model can be selected in the left hand selection panel and described below. Not all models are available in the model if you are not logged in.
In all cases the 100 abstracts with the highest score for the selected model are shown. By changing the selection of the model, the output in the tab Evidence is automatically updated. The generated table (Article classes) has the following columns:
Citation: The articles in which your concept occurs selected for high scores with the selected model.
Score: The score (between 0 and 1) from the randomForest model
The search field button allow to select words used in the citations. This can be any word including author names and journal abbreviations.
Year: The year of the publication.
Clicking a row in the table shows the corresponding abstract on the righthand side. Clicking on the hyperlink (number) brings you to the abstract on the Pubmed site. Abstracts in the table can be selected and added to you set by pressing the button: Add selected abstracts to My Abstracts. If you go to My Abstracts then you will find the selected abstacts listed so you can revisit this at a later stage. The selection in My Abstracts does not change in case search conditions are altered.
This tab shows analysis results for the set of concepts that have been saved in the concept set. This tab has a number of sub-tabs.
Concept set: The concepts that are currently in your set.
X-table: A table in which all concepts in your set are linked to each other. Clicking on a rwo in the table show the abstracts in which both concepts co-occur. This can also be done in sentence mode, in which case only the sentences in which both concepts co-occur are shown.
Heatmap This is a heatmap in which the dot size depicts the strength of the relation (Score) between two concepts and the color indicates the overlap (number of abstracts).
Network: Network view, in which all concepts in your set are linked (if there is a literature connection). In the case that there is no conenction between the concepts in your set, a warning message is shown.
On this page the abstracts that are currently in the set, are shown. Using the radiobuttons on the left panel, you can format the output that you need. The default output is to show all the abstracts with citation information. If you need the identifiers, for example to import them into a reference manager program like EndNote, choose the PMIDS option. If you want to copy the abstracts directly into your document, select the Citations option.
You can use the download button to download the abstracts to an Excel file. This file contains all the information on authors, journal etc. An example is shown below.
For a thorough description on how to score cooccurrences, we suggest to read the introductory chapter of the PhD thesis of Stefan Evert The Statistics of Word Cooccurrences.
More importantly, we believe that: