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Related Experiment Video

Updated: Sep 19, 2025

From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia

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Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.

Waqar Ahmad1, Abdul Raheem Shahzad2, Muhammad Awais Amin1,3

  • 1Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.

Plos One
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances early Chronic Myeloid Leukaemia (CML) detection using machine learning on protein data. The developed online tool achieves up to 94% accuracy, aiding in timely patient intervention.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Machine Learning in Healthcare

Background:

  • Leukaemia prevalence is rising in Southeast Asia, with a high fatality rate.
  • Early prediction of Chronic Myeloid Leukaemia (CML) is crucial for improving patient outcomes.
  • Existing prediction systems require enhancement for greater accuracy and early detection.

Purpose of the Study:

  • To significantly improve early-stage Chronic Myeloid Leukaemia (CML) prediction systems.
  • To leverage protein sequential data from key genes for CML outcome prediction.
  • To develop a user-friendly tool for early CML detection.

Main Methods:

  • Utilized protein sequential data from altered genes (BCL2, HSP90, PARP, RB).
  • Employed feature extraction methods: Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC).
  • Applied Machine Learning models (SVM, XGBoost, RF, KNN, DT, LR) after outlier handling and feature selection validation (PCA).

Main Results:

  • Achieved prediction accuracy rates between 66% and 94% across various Machine Learning models.
  • Comprehensive evaluation using accuracy, sensitivity, specificity, and F1-score.
  • Demonstrated the effectiveness of protein sequential data in CML prediction.

Conclusions:

  • The developed Machine Learning approach offers a substantial enhancement for early CML detection.
  • A user-friendly online dashboard application is proposed for practical clinical use.
  • This tool has significant potential to aid healthcare professionals in early CML diagnosis and management.