The post highlights how the way we visualize scientific data can shape what insights we see. It shows that visualization isn't just about showing data, but about guiding interpretation through thoughtful design choices. From axis scales to data grouping, even small decisions can shift the viewer’s understanding. Clear, purposeful visuals are key to communicating science effectively.
Perspectives in Visualization
iKraph, a large-scale biomedical knowledge graph constructed from all PubMed abstracts using an award-winning information extraction pipeline. By integrating data from 40 public databases and high-throughput genomics, iKraph surpasses traditional curated datasets in both scale and accuracy. The study demonstrates iKraph's utility in real-time drug repurposing during the COVID-19 pandemic, identifying over 1,200 candidate drugs within four months—many later validated by clinical trials or publications. A cloud-based platform provides academic users access to this rich, structured data for AI-driven biomedical research
iKraph: AI-Powered Knowledge Graph
Caution Ahead: Navigating AI in Bioinformatics
Fios Genomics highlights the growing use of AI in bioinformatics, while warning of its potential pitfalls. It emphasizes that while machine learning can accelerate discovery, it can also introduce biases if data quality and model validation are overlooked. The post cautions against treating AI as a black box and urges researchers to maintain interpretability and scientific rigor. Ultimately, it advocates for a balanced approach—leveraging AI’s power while grounding analyses in strong biological context and validation.