CSBJ Special Issue
Explainable and Causal AI in Computational Life Sciences
Call for Papers
Special Issue on Explainable and Causal AI in Computational Life Sciences
Related to the WS-RIDL 2026 focus on reliable, interpretable, and causally grounded AI in computational life sciences, Computational and Structural Biotechnology Journal (CSBJ) invites submissions that advance explainable and causal AI for computational life sciences. The issue will showcase methods and applications that improve the transparency, interpretability, and reliability of AI-driven discovery.
Scope
Artificial intelligence is increasingly used in therapeutic target identification, drug discovery, patient stratification, disease diagnosis, and clinical decision support. The issue welcomes methodological and application-oriented contributions that move beyond black-box prediction toward interpretable, causally grounded, and trustworthy AI models. The special issue welcomes contributions from the broader community, including work developed from discussions and collaborations around WS-RIDL 2026.
Topics of Interest
- Interpretable and explainable machine learning
- Causal inference and causal discovery
- Multi-omics, single-cell, imaging, EHR, and wearable data
- Molecular property prediction and drug discovery
- Clinical decision support and precision medicine
- Evaluation of interpretability, robustness, fairness, and utility
- Open-source tools, benchmarks, datasets, and workflows
- Reviews, perspectives, and emerging transparent AI methods
Guest Editors
About the Journal
CSBJ is a Science Partner Journal published in collaboration with the American Association for the Advancement of Science (AAAS). The journal has an Impact Factor of 4.8 and a CiteScore of 8.2.