Manuscript Topics

Recent years have seen rapidly growing interest in, and application of, Artificial Intelligence / Machine Learning/Deep Learning methodology in the biomedical data analysis space. Simultaneously, network-centered methods have been increasing in salience. These two trends intersect in computational systems biology, perhaps best exemplified by the Bayesian Networks (BNs) modeling. BNs possess two critical advantages over comparable methodology: first, high explain ability and interpretability, and second, ability to infer sparse causal and dependence / independence relationship patterns from the “flat” data.

Ongoing work in biological network modeling methodology includes improving the scalability of network construction, evaluating different methods for handling mixed data types, incorporating latent variables, assessing the network robustness and validity, implementing more realistic simulation frameworks, and developing more robust software interfaces and visualization options, specifically aimed at the biomedical applications and multimodal data. On a more fundamental level, interrogating the notions of causality vs. dependence / independence vs. correlation vs. mutual information remains an active research area at the interface of statistics, computer science and information theory.

The motivation behind this special issue is to invite investigators in computational biology, statistics and computer science to publish their research related to the above methodological and theoretical issues, as well as applications of network-centered causal learning and dependency modeling to wide-ranging biological, medical and healthcare data analysis problems.

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