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Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

Lahiru Iddamalgoda¹, Partha S Das², Achala Aponso¹

¹Department of Computing, Informatics Institute of Technology, University of Westminster Colombo, Sri Lanka.

Frontiers in Genetics

|August 26, 2016

Summary

This summary is machine-generated.

Data mining and pattern recognition improve genetic studies for inherited diseases. Combining gene prioritization, protein interaction (PPI), and K-nearest neighbors methods can accurately identify causal genetic variants.

Keywords:

data mining inherited diseases machine learning protein-protein interaction single nucleotide polymorphism

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Biomedical Data Mining

Background:

Genetic studies are crucial for understanding inherited diseases.
Accurate prioritization of single nucleotide polymorphisms (SNPs) linked to diseases remains a significant challenge.
Existing data mining models for biomedical applications require refinement for disease-causal variant identification.

Purpose of the Study:

To review current data mining and pattern recognition models for identifying inherited diseases.
To discuss the necessity of binary classification and scoring-based prioritization methods for causal variant determination.
To propose an integrated approach for enhanced genetic factor categorization in disease causation.

Main Methods:

Review of state-of-the-art data mining and pattern recognition techniques.
Analysis of binary classification and scoring-based prioritization methods for SNPs.
Exploration of gene prioritization and protein-interaction (PPI) network analysis.
Application of the K-nearest neighbors algorithm.

Main Results:

Current data mining models show promise but face challenges in precise SNP prioritization.
Binary classification and scoring methods offer distinct advantages and disadvantages for variant identification.
Gene prioritization and PPI methods, when combined with K-nearest neighbors, demonstrate potential for accurate genetic categorization.

Conclusions:

An integrated approach combining gene prioritization, PPI networks, and K-nearest neighbors offers a robust strategy for identifying genetic factors in inherited diseases.
This integrated method can improve the accuracy of categorizing genetic variants contributing to disease causation.
Further research into these combined methodologies is warranted for advancing precision medicine.