## 4 Discussion, Conclusions, and Future Directions  
Our approach for classifying tumor vs non tumor samples through their DNA methylation data is proposed as a starting point for the application of HD computing to the life science domain. Building a HD model is much easier than any other procedure involving state-of-the-art classification algorithms, but we demonstrated that it can reach and overcome them in terms of accuracy, running time, and memory usage. We want to highlight that our experiments have not been performed in parallel. However, the proposed approach can be easily run in parallel on both CPU and GPU architectures due to the nature of the HD paradigm [39; 40]. This would drastically speed up the procedure for building the classification model and the prediction of a class for new observations. Moreover, because the HD-classifier is very sensitive and depends on the number of HD-levels used to encode the data, a fine tuning procedure of the method is sometimes necessary in order to produce the best results in term of accuracy. Indeed, we are currently working on a new method for the automatic estimation of the best parameters for the HD algorithm in order to achieve the best performances in terms of computational time, memory consumption, and accuracy. Additionally, our proposal can work on pure classification tasks only, without extracting relevant features, as some supervised machine learning algorithms can perform, e.g., rule- or tree-based classification algorithms. However, this functionality can be possibly defined by analysing the content distribution of the HD representations of the classes and identifying the features that are involved in the definition of patterns that can discriminate one class from the others. These features correspond to specific CpG islands that are overlapped to the genomic region of known genes. If the identified genes are known in literature to be responsible for the advancement of a specific disease, our proposal could be adopted for diagnostic purposes. To summarize, we presented a brain-inspired hyperdimensional classifier. The proposed approach has been applied on three biological datasets concerning experiments that were conducted on both healthy and diseased individuals affected by three different types tumors, e.g., Breast Invasive Carcinoma (BRCA), Kidney Renal Papillary Cell Carcinoma (KIRP), and Thyroid Carcinoma (THCA). We validated our method by comparing the achieved results with three different classification algorithms, one specifically designed for omics data ( BIGBIOCL) and two state-of-the-art ones (Decision Trees and Support Vector Machines). Our approach overcomes all of them in terms of computational time, the amount of memory, and achieved accuracy, highlighting that it efficiently manages massive datasets on consumer laptops with a very limited amount of computational resources. Therefore, HD computing is a valid highly efficient alternative to state-of-the-art classification algorithms. The very limited set of arithmetic operations required to encode a dataset, i.e., element wise sum and subtraction of vectors, makes the HD-classifier the first candidate for the novel emergent class of processors, which has the main goal of consuming just a small fraction of the energy actually consumed from the current class of CPUs. Additionally, the promising results achieved in terms of speed and accuracy make our approach a good candidate for quasi-real-time analytics applications in healthcare and life sciences. In the future, we plan to extend the experimentation on other omics datasets, in order to confirm the validity of our approach. Additionally, we want to improve the computational performance of