Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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We are now living in the era of biological big data. The cost of sequencing is rapidly decreasing. Simultaneously, we are witnessing the rise of bio-computing tools and platforms that enable scientists to process sequencing data at remarkable speed and scale. This abundance of data, when harnessed effectively, holds the key to making informed decisions around target identification and validation stages in drug discovery. Selecting a good target to drug is one of the earliest and most consequential decisions made when developing new therapeutics.
Today, the challenge often lies not in the acquisition of data, but in its interpretation and utilization. Simply having data is no longer enough. To fully exploit the volume and variety of data available, we need systems to capture the relationships and patterns from observations to form a more holistic view. This is where Knowledge Graphs (KG) truly shine, and when properly used, will completely transform that way data is used in drug discovery. In this article, we describe at a high level, what a Knowledge Graph is and how to get started crafting one.
What is a Knowledge Graph?
At its core, a Knowledge Graph (KG) is a formal structure designed to represent information as a set of entities and the intricate relationships between them. In simple terms, we can think of individuals like BRCA1 or Breast Cancer as nodes, and relationships like mutated_in as descriptive edges between nodes.
When developed properly, it can enable automated reasoning and inference to unearth hidden implicit connections that often belie breakthroughs and discoveries. Many leading biotech and pharmaceutical companies have been ingesting data and constructing internal knowledge graphs to use in a variety of applications from drug repurposing to target identification.
DeepLink, Janssen Pharmaceuticals: Using DeepLink, an internal knowledge graph platform, researchers successfully identified two hallmark targets (one of them is now in their portfolio) for pulmonary hypertension.
ARCH, AbbVie: ARCH is the name of AbbVie’s internal knowledge graph. In a case study, AbbVie scientists used the embedded logic to discover a putative therapeutic in their portfolio that could be used to treat Carney Complex, a rare and deadly disease with no approved treatments.
The essential and most important component of a KG is the underlying ontology. Ontologies are semantic data models which determine the types of concepts that exist in the KG. Importantly, they are distinct from individual (data points) in the KG because they represent entire categories of concepts, not a specific named concept. For example, instead of describing the gene SOX2 and specific properties about SOX2, the ontology focuses on defining the concept of Gene and capturing the characteristics that a Gene should have. Some of these characteristics can be relationships to other concepts in the ontology. For example, we can describe the relationship between a Gene and Pathway with participates_in.
Bioinformatic data mining using natural language, without hallucinations.