Statistical and Computational Methods for Genetic Diseases: An Overview
暂无分享,去创建一个
Antonino Staiano | Francesco Camastra | Maria Donata Di Taranto | A. Staiano | F. Camastra | M. D. D. Taranto
[1] C. Kendziorski,et al. Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping , 2006, Biometrics.
[2] M. McCarthy,et al. Underlying genetic models of inheritance in established type 2 diabetes associations. , 2009, American journal of epidemiology.
[3] Steven Henikoff,et al. SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..
[4] Andreas Ziegler,et al. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data , 2010, Bioinform..
[5] John D. Storey,et al. Multiple Locus Linkage Analysis of Genomewide Expression in Yeast , 2005, PLoS biology.
[6] Michael Zuker,et al. Mfold web server for nucleic acid folding and hybridization prediction , 2003, Nucleic Acids Res..
[7] M. Boehnke,et al. So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. , 2007, American journal of human genetics.
[8] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[9] D. Nyholt. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. , 2004, American journal of human genetics.
[10] Robert L. Fathke,et al. Characterization of Coding Synonymous and Non-Synonymous Variants in ADAMTS13 Using Ex Vivo and In Silico Approaches , 2012, PloS one.
[11] Antonino Staiano,et al. A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients , 2010, Inf. Sci..
[12] L. del Vecchio,et al. The novel variant p.Ser465Leu in the PCSK9 gene does not account for the decreased LDLR activity in members of a FH family , 2014, Clinical chemistry and laboratory medicine.
[13] P. Bork,et al. A method and server for predicting damaging missense mutations , 2010, Nature Methods.
[14] John P A Ioannidis,et al. Meta-analysis in genome-wide association studies. , 2009, Pharmacogenomics.
[15] Brad T. Sherman,et al. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.
[16] Deanne M. Taylor,et al. Powerful SNP-set analysis for case-control genome-wide association studies. , 2010, American journal of human genetics.
[17] Hailiang Huang,et al. Gene-Based Tests of Association , 2011, PLoS genetics.
[18] Robert W. Williams,et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function , 2005, Nature Genetics.
[19] John P A Ioannidis,et al. Discovery properties of genome-wide association signals from cumulatively combined data sets. , 2009, American journal of epidemiology.
[20] Kai Wang,et al. Multiple testing in genome-wide association studies via hidden Markov models , 2009, Bioinform..
[21] A Y Kashiwabara,et al. Splice site prediction using stochastic regular grammars. , 2007, Genetics and molecular research : GMR.
[22] Antonino Staiano,et al. Investigation of Single Nucleotide Polymorphisms Associated to Familial Combined Hyperlipidemia with Random Forests , 2012, WIRN.
[23] Gregory R. Grant,et al. Bioinformatics - The Machine Learning Approach , 2000, Comput. Chem..
[24] Jason H. Moore,et al. Missing heritability and strategies for finding the underlying causes of complex disease , 2010, Nature Reviews Genetics.
[25] Jana Marie Schwarz,et al. MutationTaster evaluates disease-causing potential of sequence alterations , 2010, Nature Methods.
[26] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[27] Yan Cui,et al. Inferring gene transcriptional modulatory relations: a genetical genomics approach. , 2005, Human molecular genetics.
[28] Julian Little,et al. Systematic Reviews of Genetic Association Studies , 2009, PLoS medicine.
[29] L. Kruglyak,et al. Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.
[30] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[31] X. Chen,et al. Random forests for genomic data analysis. , 2012, Genomics.
[32] David W Fardo,et al. Statistical Approaches to Combine Genetic Association Data. , 2013, Journal of biometrics & biostatistics.
[33] David G. Stork,et al. Pattern Classification , 1973 .
[34] David Baux,et al. A Classification Model Relative to Splicing for Variants of Unknown Clinical Significance: Application to the CFTR Gene , 2013, Human mutation.
[35] E. Petretto,et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease , 2005, Nature Genetics.
[36] S. Salzberg,et al. GeneSplicer: a new computational method for splice site prediction. , 2001, Nucleic acids research.
[37] Andrew I Su,et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics' , 2005, Nature Genetics.
[38] J. Hirschhorn,et al. A comprehensive review of genetic association studies , 2002, Genetics in Medicine.
[39] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[40] Marylyn D. Ritchie,et al. GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease , 2006, BMC Bioinformatics.
[41] C. Kimchi-Sarfaty,et al. Understanding the contribution of synonymous mutations to human disease , 2011, Nature Reviews Genetics.
[42] Hiroyuki Honda,et al. Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma , 2004, BMC Bioinformatics.
[43] John P A Ioannidis,et al. The power of meta-analysis in genome-wide association studies. , 2013, Annual review of genomics and human genetics.
[44] Ivan Rusyn,et al. Computational tools for discovery and interpretation of expression quantitative trait loci. , 2012, Pharmacogenomics.
[45] M. Stephens,et al. Bayesian statistical methods for genetic association studies , 2009, Nature Reviews Genetics.
[46] Antonino Staiano,et al. Association of USF1 and APOA5 polymorphisms with familial combined hyperlipidemia in an Italian population. , 2015, Molecular and cellular probes.
[47] E. Zeggini,et al. Defining the power limits of genome‐wide association scan meta‐analyses , 2011, Genetic epidemiology.
[48] Park,et al. Open Access Research Article Identification of Type 2 Diabetes-associated Combination of Snps Using Support Vector Machine , 2022 .
[49] Simon C. Potter,et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.
[50] Kerrie L. Mengersen,et al. Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[51] P. Bradley,et al. Toward High-Resolution de Novo Structure Prediction for Small Proteins , 2005, Science.
[52] Rachel B. Brem,et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors , 2003, Nature Genetics.
[53] Xin-She Yang,et al. Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.
[54] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[55] C. Béroud,et al. Human Splicing Finder: an online bioinformatics tool to predict splicing signals , 2009, Nucleic acids research.
[56] J. Nap,et al. Genetical genomics: the added value from segregation. , 2001, Trends in genetics : TIG.
[57] M. Daly,et al. Genome-wide association studies for common diseases and complex traits , 2005, Nature Reviews Genetics.
[58] Scott M. Williams,et al. Guidelines for Genome-Wide Association Studies , 2012, PLoS genetics.
[59] Life Technologies,et al. A map of human genome variation from population-scale sequencing , 2011 .
[60] D. Balding. A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.
[61] Jason H. Moore,et al. Genetic programming neural networks: A powerful bioinformatics tool for human genetics , 2007, Appl. Soft Comput..
[62] B. Fridley,et al. Gene set analysis of SNP data: benefits, challenges, and future directions , 2011, European Journal of Human Genetics.
[63] C. Gieger,et al. Genomewide association analysis of coronary artery disease. , 2007, The New England journal of medicine.
[64] Eleazar Eskin,et al. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. , 2011, American journal of human genetics.
[65] Alain Xayaphoummine,et al. Kinefold web server for RNA/DNA folding path and structure prediction including pseudoknots and knots , 2005, Nucleic Acids Res..
[66] Andrew B. Nobel,et al. FastMap: Fast eQTL mapping in homozygous populations , 2008, Bioinform..
[67] N. Bing,et al. Genetical Genomics Analysis of a Yeast Segregant Population for Transcription Network Inference , 2005, Genetics.
[68] Judy H. Cho,et al. Finding the missing heritability of complex diseases , 2009, Nature.
[69] Jinhua Wang,et al. ESEfinder: a web resource to identify exonic splicing enhancers , 2003, Nucleic Acids Res..
[70] P. Holmans. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. , 2010, Advances in genetics.
[71] John P. A. Ioannidis,et al. Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls , 2008, Human Genetics.
[72] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[73] Andreas Ziegler,et al. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data , 2010, Bioinform..
[74] J. Ott,et al. Detecting gene-gene interactions using support vector machines with L1 penalty , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).
[75] Ching Lee Koo,et al. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology , 2013, BioMed research international.
[76] J. Zhu,et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations , 2004, Cytogenetic and Genome Research.
[77] Thomas A. Hopf,et al. Protein structure prediction from sequence variation , 2012, Nature Biotechnology.
[78] Mitchell H. Gail,et al. On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs , 2009, 1010.5046.
[79] Hua Xu,et al. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks , 2012, BMC Systems Biology.
[80] Ping Wang,et al. A review of statistical methods for expression quantitative trait loci mapping , 2006, Mammalian Genome.
[81] Nathan Mantel,et al. Chi-square tests with one degree of freedom , 1963 .
[82] Miguel Pérez-Enciso,et al. Qxpak.5: Old mixed model solutions for new genomics problems , 2011, BMC Bioinformatics.
[83] J. Ioannidis,et al. Meta-analysis methods for genome-wide association studies and beyond , 2013, Nature Reviews Genetics.
[84] Jonathan J Shuster,et al. Empirical vs natural weighting in random effects meta‐analysis , 2009, Statistics in medicine.
[85] D. Altshuler,et al. A map of human genome variation from population-scale sequencing , 2010, Nature.
[86] Peter Kraft,et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis , 2012, Nature Genetics.
[87] K. Lange,et al. Prioritizing GWAS results: A review of statistical methods and recommendations for their application. , 2010, American journal of human genetics.
[88] Xi Chen,et al. Pathway hunting by random survival forests , 2013, Bioinform..
[89] Christina Kendziorski,et al. Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping , 2006, PLoS genetics.