Fizzy: Feature subset selection for metagenomics

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Ditzler, Gregory ^a   Morrison, J Calvin ^b   Lan, Yemin ^c   Rosen, Gail L ^b  

^a University of Arizona   (United States)

^b Drexel University   (United States)

^c DREXEL UNIVERSITY   (United States)

Author keywords

Comparative metagenomics; Feature subset selection; Open source software

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SOFTWARE; ECOLOGY; INFORMATION THEORY; LEARNING SYSTEMS; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SET THEORY; SOFTWARE ENGINEERING;

BACTERIAL COMMUNITY; FEATURE SUBSET SELECTION; FEATURE SUBSET SELECTION METHODS; FUNCTIONAL FEATURES; METAGENOMICS; OPERATIONAL TAXONOMIC UNITS; SOFTWARE IMPLEMENTATION; SUBSET SELECTION;

FEATURE EXTRACTION;

ALGORITHM; BIOLOGY; GENETIC DATABASE; GENETICS; HUMAN; METAGENOMICS; MICROFLORA; PROCEDURES; SOFTWARE; VEGETARIAN;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; HUMANS; METAGENOMICS; MICROBIOTA; SOFTWARE; VEGETARIANS;

EID: 84946416256     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0793-8     Document Type: Article

References (31)

1. Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010; 464:59-65.
2. Turnbaugh P, et al. A core gut microbiome in obese and lean twins. Nature. 2009; 475:480-5.
3. Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinf.2008;9(386). http://www.biomedcentral.com/1471-2105/9/386.
4. Department of Energy. DOE Systems Biology Knowledge Base. 2013. http://www.kbase.science.energy.gov.
5. The NIH HMP Working Group, et al. The nih human microbiome project. Genome Res. 2009; 19(12):2317-23.
6. Gilbert J, et al. Meeting Report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project. Stand Genomic Sci. 2010; 3(3):243-8.
7. Guyon I, Elisseeff A. An introduction to variable and feature selection. JMLR. 2003; 3:1157-82.
8. Breiman L. Random forest. Mach Lrn. 2001; 45(1):5-32.
9. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011; 12:R60.
10. Yang H, Moody J. Data Visualization and Feature Selection: New Algorithms for Non-Gaussian Data. In: Advances in Neural Information Processing Systems: 1999.
11. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005; 27(8):1226-38.
12. Ditzler G, Polikar R, Rosen G. A bootstrap based neyman-pearson test for identifying variable importance. IEEE Trans Neural Netw and Learn Syst. 2015; 26(4):880-6.
13. Ditzler G, Austen M, Polikar R, Rosen G. Scaling a Subset Selection Approach Via Heuristics for Mining Massive Datasets. In: IEEE Symposium on Computational Intelligence and Data Mining: 2014. p. 439-45.
14. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc. 1996; 58(1):267-88.
15. McDonald D, et al. The biological observation matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. GigaScience. 2012;1(7). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626512/pdf/2047-217X-1-7.pdf.
16. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7:335-6.
17. Brown G, et al. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. JMLR. 2012; 13:27-66.
18. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011; 12:2825-30.
19. Knight R, Leach J, et al. The American Gut Project. Web. 2014.
20. Kursa M, Rudnicki W. Feature selection with the boruta package. J Stat Softw.2010;36(11).
21. Glick-Bauer M, Yeh MC. The health advantage of a vegan diet: Exploring the gut microbiota connection. Nutrients. 2014; 6:4822-38.
22. Wu G, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh S, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011; 334(6052):105-8.
23. Matijašić B, Obermajer T, Lipoglavšek L, Grabnar I, Avguštin G, Rogelj I. Association of dietary type with fecal microbiota in vegetarians and omnivores in slovenia. Eur J Nutr. 2014; 53(4):1051-64.
24. Ruengsomwong S, Korenori Y, Sakamoto N, Wannissorn B, Nakayama J, Nitisinprasert S. Senior thai fecal microbiota comparison between vegetarians and non-vegetarians using pcr-dgge and real-time pcr. J Microbiol Biotechnol. 2014; 24(8):1026-33.
25. Kim MS, Hwang SS, Park EJ, Bae JW. Strict vegetarian diet improves the risk factors associated with metabolic diseases by modulating gut microbiota and reducing intestinal inflammation. Environ Microbiol Rep. 2013; 5(5):765-75.
26. Deuring JJ, Peppelenbosch MP, Kuipers EJ, van der Woude CJ, de Haar C. Impeded protein folding and function in active inflammatory bowel disease. Biochem Soc Trans. 2011; 39:1107-11.
27. Theodoratou E, et al. The role of glycosylation in IBD. Nat Rev Gastroenterol Hepatol. 2014; 11(10):588-600.
28. Campbell B, Yu L, Rhodes J. Altered glycosylation in inflammatory bowel disease: a possible role in cancer development. Glycoconj J. 2001; 18(11-12):851-8.
29. Baranska M, Trzcinski R, Dziki A, Rychlik-Sych M, Dudarewicz M, Skretkowicz J. The role of n-acetyltransferase 2 polymorphism in the etiopathogenesis of inflammatory bowel disease. Dig Dis Sci. 2011; 56(7):2073-80.
30. Ashrafi K. Obesity and the Regulation of Fat Metabolism: Worm Book; 2007, pp. 1-20. http://www.ncbi.nlm.nih.gov/pubmed/18050496.
31. Caporaso JG, et al. Moving pictures of the human microbiome. Genome Biol. 2011; 12:5.