Practical Causal Evaluation Metrics for Biological Networks
This work addresses a practical issue for researchers in systems biology by providing a more biologically meaningful evaluation metric for causal network inference, though it is incremental as it builds on existing methods.
The authors tackled the problem of evaluating causal networks in systems biology, where existing metrics often ignore the qualitative nature of biological data, and developed a new metric called sSID that identified a different optimal algorithm and improved classification performance in transcriptomic data.
Estimating causal networks from biological data is a critical step in systems biology. When evaluating the inferred network, assessing the networks based on their intervention effects is particularly important for downstream probabilistic reasoning and the identification of potential drug targets. In the context of gene regulatory network inference, biological databases are often used as reference sources. These databases typically describe relationships in a qualitative rather than quantitative manner. However, few evaluation metrics have been developed that take this qualitative nature into account. To address this, we developed a metric, the sign-augmented Structural Intervention Distance (sSID), and a weighted sSID that incorporates the net effects of the intervention. Through simulations and analyses of real transcriptomic datasets, we found that our proposed metrics could identify a different algorithm as optimal compared to conventional metrics, and the network selected by sSID had a superior performance in the classification task of clinical covariates using transcriptomic data. This suggests that sSID can distinguish networks that are structurally correct but functionally incorrect, highlighting its potential as a more biologically meaningful and practical evaluation metric.