  Leukemia, the cancer of blood cells, originates in the blood-forming cells of
the bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cells
partially become mature that look like normal white blood cells but do not
resist infection effectively. Early detection of CML is important for effective
treatment, but there is a lack of routine screening tests. Regular check-ups
and monitoring of symptoms are the best way to detect CML in the early stages.
In the study, we developed a multi-layer-perception-based meta-ensemble system
using protein amino acid sequences for early risk prediction of CML. The
deleterious mutation analysis of protein sequences provides 7discriminant
information in amino acid sequences causing CML. The protein sequences are
expressed into molecular descriptors using the values of hydrophobicity and
hydrophilicity of the amino acids. 9 These descriptors are transformed in
various statistical and correlation-based feature spaces. These 10 features
information is given to several diverse types of base learners. The preliminary
predictions of 11 base-learners are employed to develop Multi-Layered
Perceptron (MLP) based meta-ensemble. The 12 proposed learning approach
effectively utilizes the discriminant information to classify CML/non- 13 CML
protein sequences. The proposed prediction system has given improved results
and it can be 14 employed as a potential biomarker for early diagnosis of CML.
