I’m thrilled to announce the launch of our new community platform, hosting more content and scaling to infinite conversations. It is just the beginning! Our goal remains the success of our community members, helping grow their relationships in the world of graphs! Learn more about this recent move.
In this week’s newsletter, learn about estimating the probability of a drug receiving approval in clinical trials – a huge optimization of the pharmaceutical research workflow. The authors of this study used Neo4j to ingest data from diverse databases independently, then integrated them based on their use case. Their machine learning approach features biological activities, physico-chemical properties of the compounds, target-related features, and NLP-based compound representation. Don’t miss Predicting Clinical Trial Using Graph Database Integration and Machine Learning.
- Featured Community Member: Sean Robinson
- Graph Embeddings Explained: How Nodes Get Mapped To Vectors
- Health: Predicting Clinical Trial Using Graph Database Integration & Machine Learning
- Discover Aura Free: Graphconnect Conference Agenda
- Under The Hood: Real-time Analytical Processing
- Ai: Inductive Graph Neural Networks For Transfer Learning
- Interview: On Graph Database Use Cases And Solutions
- Tweet Of The Week: @jpjarnoux
- Of Interest
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