ReviewFrontiers in genetics2016
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.
Review in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.Journal of ovarian research · 2025Article
- Article
- Overview of frequent pattern mining.Genomics & informatics · 2022Article
- Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer.Frontiers in oncology · 2022Article
- Systems Challenges of Hepatic Carcinomas: A Review.Journal of clinical and experimental hepatologyReview
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Authors and funding
6 authors.
Funding
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Abstract
Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.
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