Towards Applying Associative Classifier for Genetic Variants

With the availability of biological data and the power of sharing, it produces many opportunities for computer scientists to perform researches in bioinformatics. Generally the researches propose methods for different tasks, mainly to develop algorithms in diagnosing and identification of diseases. One of the primary studies that relevant to health and diseases is genome wide association studies (GWAS). Normally the studies are conducted in different populations to replicate the risk loci of specific disease and the number of groups are keep on progressing, including those from Asian country. Computer scientists should be involved in GWAS due to certain problems and the complexity of the processes involved. The problems and past studies related to GWAS are presented in this paper.

[1]  Nicholas L. Smith,et al.  SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association , 2009, Biostatistics.

[2]  Jesús S. Aguilar-Ruiz,et al.  Gene association analysis: a survey of frequent pattern mining from gene expression data , 2010, Briefings Bioinform..

[3]  Cristian R. Munteanu,et al.  Data Mining in Complex Diseases Using Evolutionary Computation , 2009, IWANN.

[4]  Alexander A. Morgan,et al.  Clinical assessment incorporating a personal genome , 2010, The Lancet.

[5]  Athanasios V. Vasilakos,et al.  Computational Intelligence in Bioinformatics: SNP/Haplotype Data in Genetic Association Study for Common Diseases , 2009, IEEE Transactions on Information Technology in Biomedicine.

[6]  Samir Elloumi,et al.  Integrated Generic Association Rule Based Classifier , 2007 .

[7]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[8]  Tzu-Hao Wang,et al.  A genome-wide association study primer for clinicians. , 2009, Taiwanese journal of obstetrics & gynecology.

[9]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[10]  Roger W. Jelliffe,et al.  Human Genetic Variation, Population Pharmacokinetic - Dynamic Models, Bayesian Feedback Control, and Maximally Precise Individualized Drug Dosage Regimens , 2009 .

[11]  Mingzhu Zhang,et al.  Survey on Association Rules Mining Algorithms , 2010 .

[12]  M. Anandhavalli,et al.  Association Rule Mining in Genomics , 2010 .

[13]  Ivan Merelli,et al.  SNPRanker: a tool for identification and scoring of SNPs associated to target genes , 2010, J. Integr. Bioinform..

[14]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[15]  Wenjun Zhong,et al.  Classification tree for detection of single-nucleotide polymorphism (SNP)-by-SNP interactions related to heart disease: Framingham Heart Study , 2009, BMC proceedings.

[16]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[17]  Qianchuan He,et al.  BIOINFORMATICS ORIGINAL PAPER , 2022 .

[18]  Ed Keedwell,et al.  Ant colony optimisation to identify genetic variant association with type 2 diabetes , 2011, Inf. Sci..

[19]  Qi Luo.  Advancing Computing, Communication, Control and Management , 2010 .

[20]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[21]  Ramkishore Bhattacharyya,et al.  Cohesion: A concept and framework for confident association discovery with potential application in microarray mining , 2011, Appl. Soft Comput..

[22]  Pavel Krömer,et al.  Upgrading Web Search Queries , 2007 .

[23]  Sofianita Mutalib,et al.  A brief survey on GWAS and ML algorithms , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[24]  Alberto Prieto,et al.  Bio-inspired systems: Computational and ambient intelligence , 2011, Neurocomputing.

[25]  Scott M. Williams,et al.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.

[26]  Jing Yuan,et al.  Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies , 2009, BioData Mining.

[27]  David Page,et al.  Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma , 2005, BIOKDD.

[28]  Ling Guo,et al.  GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases , 2010 .

[29]  Park,et al.  Open Access Research Article Identification of Type 2 Diabetes-associated Combination of Snps Using Support Vector Machine , 2022 .

[30]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[31]  Bruce R. Korf,et al.  Human Genetics and Genomics , 2006 .