A framework for the application of decision trees to the analysis of SNPs data

Fiaschi, Linda; Garibaldi, Jonathan M.; Krasnogora, Natalio

doi:10.1109/cibcb.2009.4925715

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…With SNPs and other clinical conditions contributing to a wide range of inherited diseases (Fiaschi et al, 2009 ), a general framework has been proposed to find variants for pre-eclampsia, a progressive disorder that occurs during pregnancy and soon after the birth, affecting both the mother and the baby (Roberts et al, 1989 ). Mutated risk genes, genetic, and environmental factors are thought to be of key importance in such diseases (Risch and Merikangas, 1996 ; Liangcai et al, 2008 ).…”

Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

“…To overcome this limitation, PSI-BLAST (Altschul et al, 1997 ) or COBALT (Papadopoulos and Agarwala, 2007 ) enables feature selection methods. Family history related data can additionally be used to infer performance of the proposed solution (Fiaschi et al, 2009 ), in addition to feature selection entities such as transcriptional and promoter regions (Liangcai et al, 2008 ; Wu et al, 2014 ). Whereas the combination methods explain more originality of how SNPs specific to diseases can be analyzed, KEGG pathways are useful to scrutinize other environment factors with SNP data.…”

Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

“…Whereas the combination methods explain more originality of how SNPs specific to diseases can be analyzed, KEGG pathways are useful to scrutinize other environment factors with SNP data. The phenotype data associated with diseases and its threshold values are used to transform categorical data into Boolean ones (Fiaschi et al, 2009 ) which helps in machine learning. However, it must be done judiciously as selection may significantly affect the accuracy of the solution.…”

Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

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

et al. 2016

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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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Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

Section: Combination Of Snp Methods Attributing To Inherited Diseasesmentioning

confidence: 99%

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

et al. 2016

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“…Alternating Decision Trees (ADTrees) form a subclass of decision trees that have been utilized for case-control genetic studies by [Liu et al, 2005] and [Fiaschi et al, 2009]. Two key features of ADTrees that differentiate them from typical, binary decision trees such as CART or C4.5 are: 1) ADTrees may produce nonbinary structures; and 2) traversing an ADTree to classify an individual requires following all possible paths in parallel rather than choosing only one path at each node.…”

Section: Introductionmentioning

confidence: 99%

“…Alternating Decision Trees (ADTrees) form a subclass of decision trees that have been utilized for case-control genetic studies by Liu et al [2005] and Fiaschi et al [2009]. Two key features of ADTrees that differentiate them from typical, binary decision trees such as CART or C4.5 are:…”

Section: Introductionmentioning

confidence: 99%

Bootstrap Aggregating of Alternating Decision Trees to Detect Sets of SNPs That Associate With Disease

Guy

Santago

Langefeld

2012

Genetic Epidemiology

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Complex genetic disorders are a result of a combination of genetic and non-genetic factors, all potentially interacting. Machine learning methods hold the potential to identify multi-locus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of SNPs of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges. We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of ADTrees. The algorithm is order nk2, where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7822 SNPs in 3548 individuals). Our results suggest that BADTrees holds promise as a low computational order algorithm for detecting complex combinations of SNP and environmental factors associated with disease.

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Karmaşık Hastalıkların Teşhisinde Veri Madenciliği Yöntemlerinin Başarım Karşılaştırması

Yücebaş

2018

Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Bütünsel genom ilişkilendirme çalışmalarında (BGİÇ) ortaya çıkan verilerin yüksek miktarda ve çok boyutlu olması, profillerin hastalıklarla ilişkilendirilmesi ve buradan teşhise gidilmesi sırasında farklı veri madenciliği yöntemlerinin kullanılması ile mümkün olmaktadır. Yapılan çalışmada 1025 vaka ve 531 kontrolden oluşan melonom veri kümesi ile farklı etnik kökenli 2325 vaka ve 2350 kontrolden oluşan ve prostat kanseri veri kümesi kullanılmıştır. Bu hastalıklarla ilgili profiller Karar Ağacı, Naive Bayes, Destek Vektör Makinası gibi farklı veri madenciliği yöntemleri ile incelenmiştir. Her iki hastalık için de destek vektör makinası kullanılan yöntemler arasında en iyi başarımı sağlamıştır. İlgili yöntem prostat kanseri veri kümesinde %75.68’lık bir kesinlik değeri sunarken, melonom veri kümesi için %78,6’lik bir kesinlik değeri yakalamıştır.

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A framework for the application of decision trees to the analysis of SNPs data

Cited by 3 publications

References 29 publications

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

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

Bootstrap Aggregating of Alternating Decision Trees to Detect Sets of SNPs That Associate With Disease

Karmaşık Hastalıkların Teşhisinde Veri Madenciliği Yöntemlerinin Başarım Karşılaştırması

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