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The research aims to address challenges in applying causal discovery methods to biological systems using deep learning.

April 1, 2026Open Access

Are we ready for causal discovery in biological systems using deep learning?

Puntos clave

The research aims to address challenges in applying causal discovery methods to biological systems using deep learning.
Review of advancements in causal discovery methods over the past three decades
Analysis of neural methods for inferring causal relationships from biological data
Identification of key technological hurdles in the application of these methods
Emerging approaches go beyond traditional assumptions about acyclicity
Neural methods provide efficient, scalable solutions for causal inference
Five key technological challenges hinder the full realization of these methods in biological contexts

Resumen

Abstract The field of causal discovery has advanced considerably over the past three decades, in terms of perspectives, computational methods, and foundational concepts. Nevertheless, their application to biological systems that are commonly found in nature (i.e. large-scale, self-regulating), continues to face significant challenges. In this regard, we highlight emerging approaches that go beyond the traditional assumption of global acyclicity, instead leveraging efficient and scalable neural methods to infer pairwise causal relationships, directly from the data. Nonetheless, there remains five key technological hurdles, which must be overcome, to realize the deeper understanding and stronger inference biological causal networks promise.

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