AI in Bioinformatics: Turning Complex Data into Actionable Insights with BullFrog Data Networks®
The life sciences industry faces a paradox: researchers have access to more biological and clinical data than ever, yet over 90% of drug candidates still fail before reaching the market. The problem isn’t data scarcity, it’s the inability to extract reliable, causal insights from complex, multimodal datasets.
In this white paper, based on BullFrog AI’s webinar“AI in Bioinformatics: Overcoming Pitfalls in Statistical, ML, and Generative AI Approaches,” we explore why traditional statistical methods, and many AI tools fall short in biomedical research. You’ll learn how compositional data traps, unstable feature importance, and over reliance on generative AI can distort findings, and how BullFrog Data Networks®, powered by the bfLEAP® causal AI platform, overcomes these limitations with explainable, reproducible intelligence.
Discover how BullFrog AI combines causal modeling, probabilistic feature importance, and graph analytics to transform biological complexity into clarity, enabling faster target identification, more reliable biomarkers, and smarter clinical trials.