## 1 Introduction  
In the last decade, the field of genomics has undergone great evolutions thanks to Next Generation Sequencing (NGS), a high-throughput technology for efficiently detecting the location, the structure, and the functionalities of the composing element (DNA) with low cost and high speed. NGS is adopted in cancer research with the aim to find novel biomarkers, factors of prediction, and targets for achieving advancements in prognosis and treatments [1; 2; 3; 4]. In particular, the NGS technique of DNA methylation [5; 6] has been proven to play an important role in knowledge discovery of cancer [7; 8; 9; 10]. Indeed, DNA methylation data are actually used in early diagnosis of tumors [11; 12], because they enable finding potential biomarkers, i.e., specific patterns and characteristics that are common in tumor samples. DNA methylation is a biochemical modification that involves the addition of a methyl group in correspondence of carbon-5 in cytosine. This happens almost only in the dinucleotide CpG, which is the cytosine followed by a guanine [6]. This phenomenon is so common in DNA that it can be assessed that, among all of the CpG islands, 80% of them are methylated in mammals [13]. We can speak of hypo- or hyper-methylation, which regulate the turning on or off of genes that, for example, act as tumor-suppressors. Therefore, it becomes increasingly important to understand epigenetics modification, like DNA methylation, in human cells. In this work, we consider the data of DNA methylation experiments performed on cancer patients by focusing on three different types of cancer, i.e., Breast Invasive Carcinoma (BRCA), Kidney Renal Papillary Cell Carcinoma (KIRP), and Thyroid Carcinoma (THCA). These data are provided by the Genomic Data Commons ([[https://gdc.cancer.gov/](https://gdc.cancer.gov/)]([https://gdc.cancer.gov/](https://gdc.cancer.gov/))) [14], a repository of cancer data, shared by several projects, such as The Cancer Genome Atlas (TCGA) [15], which we have considered. Indeed, it contains genomic and clinical data of more than 30 tumor types of over 20,000 patients derived from different NGS experiments, e.g., DNA methylation. We design, develop, and apply a new machine learning algorithm, called HD-classifier, which relies on an in-memory cognitive-based hyperdimensional approach to distinguish tumor from non-tumor samples through the processing of their DNA methylation data in order to analyze those data. Machine learning techniques are very promising and wide adopted for the classification of cancer patients. Indeed machine learning (ML) has been used for cancer diagnosis and detection and also applied towards cancer prediction and prognosis [16]. ML is widely applied for case control studies, i.e., specific studies that aim to identify subjects by outcome status at the outset of the investigation, e.g., whether the subject is diagnosed with a disease. Subjects with such an outcome are categorized as cases. Once an outcome status is identified, controls (i.e., subjects without the outcome, but from the same source population) are selected. In supervised learning, a classification model is defined starting from labeled training data, with which the classifier tries to make predictions about unavailable or future data. Therefore, supervision means that, in the set of samples or datasets, the desired output signals (e.g., presence of a disease or not) are already known as previously labeled. In this type of learning, which relies on labels of discrete classes and on a training set, we will therefore have a task based on supervised techniques [10; 12; 17; 18]. Classification problems are intended to identify the characteristics that indicate the group to which each sample belongs [19]. Previous studies that consider methylation data [10; 20] have achieved good performance using state-of-the-art machine learning methods, e.g., rule based classifier [21] and decision trees [22; 23], applied in order to produce classification models that are able to predict which class a new sample belongs.