Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

TRPM3 in the eye and in the nervous system - from new findings to novel mechanisms

The calcium-permeable cation channel TRPM3 can be activated by heat and the endogenous steroid pregnenolone sulfate. TRPM3's best understood function is its role as a peripheral noxious heat sensor in mice. However, the channel is expressed in various tissues and cell types including neurons as well as glial and epithelial cells. TRPM3 expression patterns differ between species and change during development. Furthermore, a plethora of TRPM3 variants that result from alternative splicing have been identified and the majority of these isoforms are yet to be characterized. Moreover, the mechanisms underlying regulation of TRPM3 are largely unexplored. In addition, a micro-RNA gene (miR-204) is located within the TRPM3 gene. This complexity makes it difficult to obtain a clear picture of TRPM3 characteristics. However, a clear picture is needed to unravel TRPM3's full potential as experimental tool, diagnostic marker and therapeutic target. Therefore, the newest data related to TRPM3 have to be discussed and to be put in context as soon as possible to be up-to-date and to accelerate the translation from bench to bedside. The aim of this review is to highlight recent results and developments with particular focus on findings from studies involving ocular tissues and cells or peripheral neurons of rodents and humans.

Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI)

BACKGROUND

In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster.

METHODS

Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub-symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100-fold cross-validation.

RESULTS

The importance of the variables of the data set (6 pain-related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub-symbolic classifiers. A generalized post-hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy.

CONCLUSIONS

Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human-understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy.