first you must convert all epigenomic features (eg: methylation of a given position) to numerical features (eg: one-hot encoding), and apply this transformation to all samples. Then you will have an sparse and wide table, as each sample will be a row and each feature a column. There is no "right" number of samples, but remember that for genomic the number of features is huge, so a tiny number of samples will make difficult the training any model.

Thank you so much for your invaluable help and insights!I appreciate your time and expertise.

DNA methylation patterns, either independently or in combination with other epigenetic features such as histone modifications and chromatin accessibility, may enhance predictive performance in various studies. Furthermore, multi-omics models that integrate DNA methylation with RNA sequencing or genomic data have demonstrated greater potential for further improving performance. However, it is important to note that there is no single approach guaranteed to produce optimal results, as performance is dependent on several factors, including data quality, data balancing, and model design. Notably, an appropriate sample size presenting high-quality data is crucial for effective training of the models. For additional information, review articles such as "Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts" and "A Review of Deep Learning Models for the Prediction of Chromatin Interactions with DNA and Epigenomic Profiles" may provide valuable insights. In the context of oncology, the data might exhibit high heterogeneity; therefore, the samples used in the models should be carefully accounted for accordingly.