Previously, adding all hidden relationships between entities in a graph required non-intuitive configuration of the explore filter.

With Release 2.1, simply select your node(s) you wish to surface hidden relationships for, and use the Find all relationships button within the Find menu. This will add all hidden relationships between your selected node(s) and other nodes already in your graph.

This functionality is particularly useful for exploring interrelationships in graph, for example how might a target of interest relate to one or multiple diseases of interest.

Before Release 2.1, finding mutually related nodes between two nodes of interest was tedious and performance intensive. This action involved numerous steps including fully expanding both nodes of interest, mapping hidden relationships between the two datasets, then using a combination of select neighbors and select inverse to only identify the final dataset.

In Release 2.1 you can find mutually related nodes between two entities of interest at the click of a button. Simply select your two nodes and use the Find mutually related nodes button in the Find menu to instantly add overlapping relationships.

We released new “Views” in Release 2.02 which included various metrics to enhance the visual representation of relationships within graph as well as provided a way to distinguish between volumes and measures of different data domains, for example volume of publications versus patents or clinical trials.

In Release 2.1, we added a new View called Max Assertion Strength. This view gives you the ability to see the highest assertion within a relationship directly on the graph itself. Toggling Max Assertion Strength updates the relationship title within graph in realtime.

In Release 2.02 we launched embedded highlighting within the Supporting Research widget to assist in identifying free text search results within the returned dataset.

We have expanded this functionality in Release 2.1 by highlighting tagged entities within the widget. This feature will help clarify the context of a sentence by giving insight into nuances such as tagged synonyms and acronym handling.

Searching for nodes had previously been difficult in graph due to common string usage across entities as well as relying upon ontologies to choose an accurate and intuitive entity name.

For example, searching for the gene “IL6” would return results for IL6R, IL6ST, and more.

With Exact Match in the search for nodes menu, you may now apply an “exact match” operator when searching for entities to explore in graph.

Prior to Release 2.1, filtering graph was limited to the unique graph data properties such as Number of Hidden Relationships and relationship properties such as Average Assertion Strength and Number of Knowledge Extractions. We heard from you that while these properties are helpful for initial filtering, they fall short in providing the biological context needed in order to really surface and identify the most relevant relationships of interest.

With Release 2.1 we are incredibly excited to launch numerous biological properties across entities that will enable you to apply additional filter criteria to your graph in order to surface and identify the most relevant relationships of interest to you and your research.

These properties are sourced from a diverse set of databases and ontologies and will dramatically accelerate the speed at which you enhance your understanding of the relationships in graph.

These new biological properties include:

Gene

• Gene Type

Disease

• Therapeutic Area (note that diseases may belong to one or multiple TAs)

Drug

• Molecule Type

Search for nodes was previously limited to the entity’s ontology ID and ontology name. This proved difficult for user’s to easily search and find their nodes of interest that they wish to explore in graph.

In Release 2.1 we have included a catalog of our entity synonyms which will allow you to search for nodes using more common, user-friendly terminology. This new data paired with other search enhancements such as Exact Match, will make it easier than ever to find and add your entities of interest to graph.

For example, to identify Lung Cancer in your search, you would have needed to enter either “Lung Neoplasms” or “MESHD008175” in order to find your entity of interest in graph.

Today with Release 2.1, you can enter “Lung Cancer”, or any other entity synonym, and still return the correct node in graph

Nodes in graph previously contained references to the entity’s ontology ID, which was useful to support follow-up research on entities of interest outside of graph.

We have extended this data feature by including numerous new cross-references in graph, particularly for Genes. This will enable you to quickly take the cross reference ID from graph and continue your research without the uncertainty that you are using the correct entity identification in external databases.

The new cross-reference IDs and metadata fields may be accessed by selecting a node and selecting the Detail tab.

These new cross-references and metadata fields include:

Gene

• OpenTargets ID

• Ensembl ID

• Uniprot ID

• Entrez ID

• Mouse Genome Informatics ID

• Gene approved name

Phenotype

• Phenotype definition

Variant

• Chromosome

• Variant position

• dbSNP ID