## 4 Discussion  
Machine learning is widely used in neurophysiology and integrative neuroscience to decode single/multi-unit recordings [29; 30; 31; 32] or voxel pattern activations from fMRI [33], in neural prosthetics [34], and in many other applications. However, ML is still not commonly used for histological data analysis. Here we applied machine learning to classify in vitro cultivated astrocytes according to their nuclear expression of LMNB1. We used eight morphological features to predict five nuclear morphologies of LMNB1 expression. We trained several classifiers to this task and showed that tree ensembles can classify astrocyte morphology at rates better than chance. This is a step towards the automation of histologic data analysis, which is important, considering the development of techniques for faster anatomical data production, such as CLARITY [35] and PACT [36] that do not require time-consuming slicing of the brain.  
Our results show that a single classification tree predicts less accurately than tree ensembles. This is consistent with [37], who showed that an ensemble of weak learners can predict highly unbalanced data more accurately. Surprisingly, we found that the tree ensemble performs better than the tree bigger. Tree bigger is an algorithm that works very similarly to the random forest, which is considered the benchmark for this kind of analyses [38]. This is probably because the tree ensemble relies on a boosting algorithm, where individual trees work in series and each tree is presented with the whole training sample, whereas the tree bigger is a bagging algorithm, where several trees work in parallel and each of them is trained on a subset of the samples, so that every tree is trained on an even smaller sample than the initial one.  
We also showed that the best results are obtained using RUSBoost instead of AD-ABoost, and by combining LMNB1 categories by morpho-functional similarities. Finally, we tested several types of cross validation. We obtained the best results with LOOCV, however bootstrapping yielded more balanced results, in that it also gave good results for Class 3, the least represented one. LOOCV and bootstrapping are the most computationally intensive and time-consuming CVs, however, they offer broader exposure to all categories during the training phase [39].  
Finally, we estimated the important predictors: the normalized intensity of LMNB1, nuclear area, cellular area, and the number of ramifications. Although it is not surprising that the intensity of expression of the LMNB1 protein may be related to the way in which it is expressed in the nucleus, we are not aware of any previous research or evidence that has specifically examined this relationship. This suggests that this may be a new or novel finding, and further research may be needed to confirm and explore this potential correlation. On the other hand, the other three important predictors are a novel finding, and open new perspectives on the mechanism of action of LMNB1 and its expression. None of the predictors considered were able to predict the membership class on their own as accurately as when they were combined with the other features. This suggests that the relationship between these predictors and the LMNB1 class is likely to be complex, and may not follow a simple, linear pattern. Together, our analyses show new perspectives regarding the understanding of the relationships between cellular features that are difficult to study with simpler statistical tools. In future work, it would be important to validate the results obtained from our small data sample on a larger and more diverse dataset. Moreover, exploring other machine learning algorithms or techniques, could potentially yield even better results. Finally, it would be interesting to investigate the biological significance of the identified predictors and their potential role in astrocyte function and physiology.