Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

Autor: Lahiru Iddamalgoda, Partha Sarathi Das, Achala Aponso, Vijayaraghava Seshadri Sundararajan, Prashanth Suravajhala, Jayaraman K Valadi
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: Frontiers in Genetics, Vol 7 (2016)
Druh dokumentu: article
ISSN: 1664-8021
DOI: 10.3389/fgene.2016.00136
Popis: Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for 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 for 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
Databáze: Directory of Open Access Journals