Version 0.2 (Preview Release)
This User Manual provides operating instructions for new users to our search and collaboration portal. The portal is designed for life science researchers, healthcare professionals and biologists where they can quickly identify candidate items – be it proteins, genes, cell lines or reagents for their experiments. They can also provide feedback on quality of search results - both for their relevance and their accuracy.
Terminology and Data Model
All search results are identified broadly as “Concepts” that can be as raw as a url, a document in its native opaque format, or a fully qualified and well-defined typed entity such as a Protein with all its attributes corresponding from say Uniprot knowledgebase along with its references across various articles. Concepts are broadly categorized as People (users, researchers), Places (including Institutions, Vendors, and Universities), Events (Dates, Times, Specific Events), Products (Bioentities, Reagents, Equipment and Materials), Documents (Articles, URLs) or simply Concepts (Dynamically discovered or user-defined concepts based on their (frequent) occurrences in search space such as ‘disease’, ‘transformation’, ‘test subject’ etc…).
An instance of an entity may have one or more Concept types – as an example a keyword may be a proper noun, a place, an organization or all of these things at the same time. It may also have various degree of confidence measure associated with each type. Additionally, it may belong to one or more subject domains – such as Biology, Chemistry, News, Current Events, Music, Sports, Politics or more...
Projects at a high-level are various verticals where we may wish to apply our search optimizations and learning algorithms or trained models. To begin with, we have focused in Biology / Life Sciences domain and specifically NIH open access articles. We will in near future allow users to add their own projects, subscribe to one or more projects and will make sure that search results are scoped with-in their specified projects.
We have incorporated notion of Dictionaries. For example there are known set of Bioentities and Reagents that we can incorporate as dictionary terms. We will allow users to add their own custom dictionaries as well. These dictionaries not only help our search engine to improve identification of known entities and concepts but they also help improve look up for new concepts in the neighborhood of these known items. As we add more such dictionaries and build more trained sets, our search engine surfaces more results across various verticals. The goal of the underlying platform and data model is to continue to process raw data through the entire pipeline without disruption as more models in one or more domains are added. We will be continually adding new dictionaries and trained models through our interim releases.
The user-interface is based upon a classic clean Search model. User begins their operations with a simple search interface where they can type any keyword to initiate search processing.
The results are retrieved in a two-pane results view where left pane provides various concept types (categorized in a domain neutral (people/orgs, places, events, products) and domain specific (products – Proteins, Genes, Cells, Reagents… etc. specific to Life Sciences) entities) and right pane provides various result views – documents, time-series, concept graph, or geographical etc.
Search and Filter Pane
This pane allows users to perform fresh or subsequent searches as a funnel (increasingly narrow search criteria using original keywords coupled with one or more search results).
This pane is view specific. Based upon currently selected view in the Results Pane (see next), view specific tasks are shown in this pane aligned to the right edge.
Results pane is the main client area and is used to display search results in variety of views – Graph of concepts and their inter-relationships, List of Documents, tag cloud, people, geography or time-series views.
Graph view is the default view that shows color-coded concepts of various types and their relationships to other concepts. The view is designed to let users easily interact with the original search results – expand or narrow down to specific set of concepts and their relationships. They can then perform subsequent funnel searches to get to very precise results.
Once you select one or more concept types from the top filter in the filter pane, you can see the results in a graphical display with concepts and their relationships displayed in corresponding concept type color and varying edge thickness indicating strength of relationship between connected nodes respectively.
Interacting with Results
You can interact with the graph by clicking using both left and right mouse buttons with following behavior for both nodes (concepts) and edges (relations).
Right-click provides a pop-up menu with following actionable items.
Properties dialog provides attributes of the selected Concept or its relationship and a list of all the references found across various articles.
You can click the toggle button > to the left of each reference and reveal the title of the article from where this reference was obtained.
You can click on the title (left-click) and see the text of the article color-coded with all the concepts high lighter along with any significant text fragments (highlighted in yellow) that were used as part of search construction.