Omics data allow us to decipher the different languages of biology: that of DNA, RNA, proteins, and metabolites. Each layer—genomics, epigenomics, transcriptomics, proteomics, and metabolomics—provides an essential piece for understanding biological systems in all their complexity. At Datharsis, we analyze and integrate this information to transform biomedical data into explainable and applicable knowledge.
The hidden language of omics data Read Post »
Jesús García, co-founder of Datharsis, attended an international program in Cologne, Germany, and confirmed that true innovation lies in answering the “why” of data, not just measuring the “what.” This highlights Datharsis’s value in transforming data into real, practical knowledge, helping projects go a step further.
Datharsis in Cologne (2025) Read Post »
In an increasingly digitalized industrial world, model interpretability is key. At Datharsis, we transform complex data into clear, reliable decisions, empowering industrial experts. Discover how our 100% interpretable models help you predict quality, monitor processes, and comply with regulations, such as the EU AI Act.
Interpretability driving industry Read Post »
In today’s cybersecurity, analyst shortages and sophisticated threats are a challenge. Data science emerges as key to automating and transparenting analysis, allowing analysts to focus on critical tasks and transform cybersecurity.
Revolutionizing cybersecurity Read Post »
Which variables should you actually interpret in a statistical model?
In this article, we explain how Sparse PCA (SPCA) — and a new corrected version — can help you identify key variables, such as biomarkers, without losing interpretability. A practical approach from biostatistics for projects with lots of data and little certainty.
Sparse PCA and biomarkers: understanding more, assuming less Read Post »
