We then used the five biomarker candidates to evaluate their performance using well-established machine learning and statistical algorithms (SVM, PLS-DA, and random forests) in screening for leukemia on additional plasmas obtained from mice developing genetically different NOTCH1-dependent tumors (ΔE-NOTCH1 tumors obtained in a Bcat1 knockout background; Bcat1−/− NOTCH1-T tumors) and plasmas obtained from mice having an abnormal but non-tumorigenic polyclonal CD4+CD8+ DP subset (pre-leukemia) in a Bcat1+/+ or Bcat1−/− background. The gene discussed is NOTCH1; the disease is leukemia.