“…As an example we mention the molecular-level data collected via 30], their use opens up new challenges in understanding the huge amount of generated data: from new approaches to deal with high data volumes generated at higher and higher speeds and that could be presented in a variety of forms (structured, semi-structured, and/or unstructured data) [24], to new approaches to deal with data heterogeneity [22,24], or deal with incomplete data [21,24] or even irreproducible data-which is a major issue at least in immunology and cell biology [31,32], and even challenges in understanding the biological mechanisms behind the data [33]. While artificial intelligence techniques (e.g., machine learning, natural language processing, computational intelligence) can provide faster and more accurate results in data analytics compared to classical statistical methods [24] (especially if the training data is not biased in any way) they don't provide us with a mechanistic understanding of the data.…”