Analysis of heterogeneous genomic samples using image normalization and machine learning.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Analyzing heterogeneous populations like viral quasispecies presents significant bioinformatics challenges.
• Machine learning applications in sequence data analysis are hindered by data complexity, biases, and structural variations.

Purpose of the Study:

• To develop a novel preprocessing approach for analyzing heterogeneous genomic data.
• To transform irregular genomic data into a normalized image format for machine learning applications.
• To apply this method to viral infection staging and transmission cluster detection.

Main Methods:

• A novel sequence image normalization technique was developed.
• Genomic data was transformed into normalized image data.
• Support Vector Machine (SVM) and clustering techniques were applied to image data for classification and analysis.

Main Results:

• The image-based approach successfully restates genomic analysis problems as image classification tasks.
• Infection staging for Hepatitis C Virus (HCV) achieved over 95% accuracy.
• Detection of viral transmission clusters yielded over 97% accuracy.

Conclusions:

• The sequence image normalization method provides robust conversion of genomic data for machine learning.
• This approach overcomes limitations of traditional machine learning methods for viral population analysis.
• The method facilitates visualization and improves accuracy in molecular epidemiology and viral disease surveillance.