NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering

Area of Science:

• Computational Biology
• Genetics
• Bioinformatics

Background:

• The single individual haplotype problem is crucial for genetic analysis and disease research.
• Existing methods struggle with noisy DNA fragment data, limiting accuracy and scalability.
• Solving this NP-hard problem requires robust algorithms for real-world applications.

Purpose of the Study:

• To introduce NCMHap, a novel method for accurate individual haplotype reconstruction.
• To address the challenge of low performance in existing methods when dealing with noisy sequence data.
• To develop a scalable and accurate computational approach for haplotype phasing.

Main Methods:

• Utilized the Neutrosophic c-means (NCM) clustering algorithm.
• NCM effectively identifies and mitigates the impact of noise and outliers in DNA fragment data.
• The NCMHap method was evaluated on benchmark datasets.

Main Results:

• NCMHap demonstrates encouraging performance when NCM is appropriately tuned.
• The method significantly outperforms existing approaches, particularly as noise levels in the data increase.
• Validation on simulated and real datasets confirms the method's efficacy.

Conclusions:

• NCMHap is a validated method for haplotype reconstruction.
• The approach is recommended for datasets with substantial gaps and noise.
• This offers a promising tool for genetic studies involving challenging data.