Predicting genome-wide redundancy using machine learning

BMC Evolutionary Biology

Volumn 10, Issue 1, 2010, Pages

  Chen, Huang Wen ^a   Bandyopadhyay, Sunayan ^a,b   Shasha, Dennis E ^a   Birnbaum, Kenneth D ^c  

^a POLYTECHNIC UNIVERSITY   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY BIOLOGY; GENE EXPRESSION; GENETIC ANALYSIS; GENETIC VARIATION; GENOME; GENOMICS; HERB; MACHINERY; MUTATION; NUMERICAL MODEL; RESEARCH WORK; SENSITIVITY ANALYSIS;

ARABIDOPSIS; ARABIDOPSIS THALIANA;

ALGORITHM; ARABIDOPSIS; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; GENE DUPLICATION; GENE EXPRESSION REGULATION; GENETICS; MULTIGENE FAMILY; PLANT GENE; PLANT GENOME; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL;

ALGORITHMS; ARABIDOPSIS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; GENE DUPLICATION; GENE EXPRESSION REGULATION, PLANT; GENES, PLANT; GENOME, PLANT; LOGISTIC MODELS; MULTIGENE FAMILY; ROC CURVE;

EID: 78349246347     PISSN: None     EISSN: 14712148     Source Type: Journal    
DOI: 10.1186/1471-2148-10-357     Document Type: Article

References (62)

1. Widespread paleopolyploidy in model plant species inferred from age distributions of duplicate genes. G Blanc KH Wolfe, Plant Cell 2004 16 1667 1678 10.1105/tpc.021345 15208399
2. Unequal genetic redundancies in Arabidopsis - a neglected phenomenon? GC Briggs KS Osmont C Shindo R Sibout CS Hardtke, Trends Plant Sci 2006 11 492 498 10.1016/j.tplants.2006.08.005 16949326
3. Plants with double genomes might have had a better chance to survive the Cretaceous-Tertiary extinction event. JA Fawcett S Maere Y Van de Peer, P Natl Acad Sci USA 2009 106 5737 5742 10.1073/pnas.0900906106
4. The flowering world: a tale of duplications. Y Van de Peer JA Fawcett S Proost L Sterck K Vandepoele, Trends in Plant Science 2009 14 680 688 10.1016/j.tplants.2009.09.001 19818673
5. Dude, where's my phenotype? Dealing with redundancy in signaling networks. S Cutler P McCourt, Plant Physiol 2005 138 558 559 10.1104/pp.104.900152 15955914
6. Arabidopsis gene knockout: phenotypes wanted. N Bouche D Bouchez, Curr Opin Plant Biol 2001 4 111 117 10.1016/S1369-5266(00)00145-X 11228432
7. NCBI GEO: archive for high-throughput functional genomic data. T Barrett DB Troup SE Wilhite P Ledoux D Rudnev C Evangelista IF Kim A Soboleva M Tomashevsky KA Marshall,, et al. Nucleic acids research 2009 37 885 D890 10.1093/nar/gkn764 18940857
8. NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. DJ Craigon N James J Okyere J Higgins J Jotham S May, Nucleic Acids Res 2004 32 575 577 10.1093/nar/gkh133 14681484
9. The origin of subfunctions and modular gene regulation. A Force WA Cresko FB Pickett SR Proulx C Amemiya M Lynch, Genetics 2005 170 433 446 10.1534/genetics.104.027607 15781713
10. Preservation of duplicate genes by complementary, degenerative mutations. A Force M Lynch FB Pickett A Amores YL Yan J Postlethwait, Genetics 1999 151 1531 1545 10101175
11. The probability of duplicate gene preservation by subfunctionalization. M Lynch A Force, Genetics 2000 154 459 473 10629003
12. Pervasive and persistent redundancy among duplicated genes in yeast. EJ Dean JC Davis RW Davis DA Petrov, PLoS Genet 2008 4 1000113 10.1371/journal. pgen.1000113 18604285
13. Redundant gene functions and natural selection. A Wagner, Journal of Evolutionary Biology 1999 12 1 16 10.1046/j.1420-9101.1999.00008.x (Pubitemid 29033103)
14. Evolution of genetic redundancy. MA Nowak MC Boerlijst J Cooke JM Smith, Nature 1997 388 167 171 10.1038/40618 9217155
15. The limits of subfunctionalization. T MacCarthy A Bergman, BMC Evol Biol 2007 7 213 10.1186/1471-2148-7-213 17988397
16. Evolutionary capacitance as a general feature of complex gene networks. A Bergman ML Siegal, Nature 2003 424 549 552 10.1038/nature01765 12891357
17. Detection of deleterious genotypes in multigenerational studies. III. Estimation of selection components in highly selfing populations. R Liu AM Ferrenberg LU Gilliland RB Meagher MA Asmussen, Genet Res 2003 82 41 53 10.1017/S0016672303006311 14621270
18. A genetic uncertainty problem. D Tautz, Trends Genet 2000 16 475 477 10.1016/S0168-9525(00)02118-1 11074284
19. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. ME Hillenmeyer E Fung J Wildenhain SE Pierce S Hoon W Lee M Proctor RP St Onge M Tyers D Koller,, et al. Science 2008 320 362 365 10.1126/science.1150021 18420932
20. An empirical comparison of supervised learning algorithms. R Caruana A Niculescu-Mizil, ACM New York, NY, USA 2006 161 168
21. Machine learning and its applications to biology. AL Tarca VJ Carey XW Chen R Romero S Draghici, PLoS Comput Biol 2007 3 116 10.1371/journal.pcbi. 0030116 17604446
22. Predicting gene function in Saccharomyces cerevisiae. A Clare RD King, Bioinformatics 2003 19 Suppl 2 i42 49 14534170
23. Machine learning of functional class from phenotype data. A Clare RD King, Bioinformatics 2002 18 160 166 10.1093/bioinformatics/18.1.160 11836224
24. Predicting phenotype from patterns of annotation. OD King JC Lee AM Dudley DM Janse GM Church FP Roth, Bioinformatics 2003 19 Suppl 1 183 189 10.1093/bioinformatics/btg1024 12855456
25. An en masse phenotype and function prediction system for Mus musculus. M Tasan W Tian DP Hill FD Gibbons JA Blake FP Roth, Genome Biol 2008 9 Suppl 1 8 10.1186/gb-2008-9-s1-s8 18613952
26. A map of the interactome network of the metazoan C. elegans. S Li CM Armstrong N Bertin H Ge S Milstein M Boxem PO Vidalain JD Han A Chesneau T Hao,, et al. Science 2004 303 540 543 10.1126/science.1091403 14704431
27. Functional bioinformatics for Arabidopsis thaliana. A Clare A Karwath H Ougham RD King, Bioinformatics 2006 22 1130 1136 10.1093/bioinformatics/btl051 16481336
28. A gene expression map of the Arabidopsis root. K Birnbaum DE Shasha JY Wang JW Jung GM Lambert DW Galbraith PN Benfey, Science 2003 302 1956 1960 10.1126/science.1090022 14671301
29. A high-resolution root spatiotemporal map reveals dominant expression patterns. SM Brady DA Orlando JY Lee JY Wang J Koch JR Dinneny D Mace U Ohler PN Benfey, Science 2007 318 801 806 10.1126/science.1146265 17975066
30. A gene expression map of Arabidopsis thaliana development. M Schmid TS Davison SR Henz UJ Pape M Demar M Vingron B Scholkopf D Weigel JU Lohmann, Nat Genet 2005 37 501 506 10.1038/ng1543 15806101
31. PLETHORA proteins as dose-dependent master regulators of Arabidopsis root development. C Galinha H Hofhuis M Luijten V Willemsen I Blilou R Heidstra B Scheres, Nature 2007 449 1053 1057 10.1038/nature06206 17960244
32. Functional divergence of duplicated genes formed by polyploidy during Arabidopsis evolution. G Blanc KH Wolfe, Plant Cell 2004 16 1679 1691 10.1105/tpc.021410 15208398
33. A recent polyploidy superimposed on older large-scale duplications in the Arabidopsis genome. G Blanc K Hokamp KH Wolfe, Genome Res 2003 13 137 144 10.1101/gr.751803 12566392
34. The gene regulatory logic of transcription factor evolution. GP Wagner VJ Lynch, Trends Ecol Evol 2008 23 377 385 10.1016/j.tree.2008.03.006 18501470
35. Linear motifs: evolutionary interaction switches. V Neduva RB Russell, FEBS Lett 2005 579 3342 3345 10.1016/j.febslet.2005.04.005 15943979
36. Trinucleotide repeats are clustered in regulatory genes in Saccharomyces cerevisiae. ET Young JS Sloan K Van Riper, Genetics 2000 154 1053 1068 10757753
37. The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. S Rhee W Beavis T Berardini G Chen D Dixon A Doyle M Garcia-Hernandez E Huala G Lander M Montoya, Nucleic acids research 2003 31 224 10.1093/nar/gkg076 12519987
38. Transcriptional profile of the Arabidopsis root quiescent center. T Nawy JY Lee J Colinas JY Wang SC Thongrod JE Malamy K Birnbaum PN Benfey, Plant Cell 2005 17 1908 1925 10.1105/tpc.105.031724 15937229
39. A gene expression map of the Arabidopsis root. KS Birnbaum DE Wang JY Jung JW Lambert GM Galbraith DW Benfey N P, Science 2003 302 1956 1960 10.1126/science.1090022 14671301
40. Transcriptional and posttranscriptional regulation of transcription factor expression in Arabidopsis roots. JY Lee J Colinas JY Wang D Mace U Ohler PN Benfey, Proc Natl Acad Sci USA 2006 103 6055 6060 10.1073/pnas.0510607103 16581911
41. Cell-specific nitrogen responses mediate developmental plasticity. ML Gifford A Dean RA Gutierrez GM Coruzzi KD Birnbaum, Proc Natl Acad Sci USA 2008 105 803 808 10.1073/pnas.0709559105 18180456
42. Cell identity mediates the response of Arabidopsis roots to abiotic stress. JR Dinneny TA Long JY Wang JW Jung D Mace S Pointer C Barron SM Brady J Schiefelbein PN Benfey, Science 2008 320 942 945 10.1126/science.1153795 18436742
43. Robust estimators for expression analysis. E Hubbell WM Liu R Mei, Bioinformatics 2002 18 1585 1592 10.1093/bioinformatics/18.12.1585 12490442
44. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. B Usadel T Obayashi M Mutwil FM Giorgi GW Bassel M Tanimoto A Chow D Steinhauser S Persson NJ Provart, Plant Cell Environ 2009 32 1633 1651 10.1111/j.1365-3040.2009.02040.x 19712066
45. Clustal W and Clustal X version 2.0. MA Larkin G Blackshields NP Brown R Chenna PA McGettigan H McWilliam F Valentin IM Wallace A Wilm R Lopez,, et al. Bioinformatics 2007 23 2947 2948 10.1093/bioinformatics/btm404 17846036
46. PAML: a program package for phylogenetic analysis by maximum likelihood. Z Yang, Comput Appl Biosci 1997 13 555 556 9367129
47. The role of occam's razor in knowledge discovery. P Domingos, Data Min Knowl Disc 1999 3 409 425 10.1023/A:1009868929893
48. IH Witten E Frank, Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, San Francisco 2 2005
49. LIBSVM: a library for support vector machines. C-C Chang C-J Lin, Book LIBSVM: a library for support vector machines. (Editor ed. eds.) City; 2001 http://www.csie.ntu.edu.tw/∼cjlin/libsvm
50. JR Quinlan, C4. 5: Programs for Machine Learning Morgan Kaufmann 1993
51. Simplifying Decision Trees. JR Quinlan, Book Simplifying Decision Trees (Editor ed. eds.) City: Massachusetts Institute of Technology 1986
52. Generating accurate rule sets without global optimization. E Frank IH Witten, Proc 15th International Conf on Machine Learning 1998 144 151
53. A Bayesian method for the induction of probabilistic networks from data. GF Cooper E Herskovits, Machine Learning 1992 9 309 347
54. Estimating continuous distributions in Bayesian classifiers. GH John P Langley, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence 1995 338 345
55. Ridge Estimators in Logistic Regression. SL Cessie JCV Houwelingen, Applied Statistics 1992 41 191 201 10.2307/2347628
56. How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. AK Seewald, Proceedings of the Nineteenth International Conference on Machine Learning table of contents 2002 554 561
57. Support-vector networks. C Cortes V Vapnik, Machine Learning 1995 20 273 297
58. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. J Platt, Advances in large margin classifiers 1999
59. Support vector machine classification and validation of cancer tissue samples using microarray expression data. T Furey N Cristianini N Duffy D Bednarski M Schummer D Haussler, Bioinformatics 2000 16 906 10.1093/ bioinformatics/16.10.906 11120680
60. Feature subset selection: a correlation based filter approach. MA Hall LA Smith, 1997 855 858
61. Bioconductor: open software development for computational biology and bioinformatics. R Gentleman V Carey D Bates B Bolstad M Dettling S Dudoit B Ellis L Gautier Y Ge J Gentry, Genome biology 2004 5 80 10.1186/gb-2004-5-10-r80 15461798
62. Using GOstats to test gene lists for GO term association. S Falcon R Gentleman, Bioinformatics 2007 23 257 10.1093/bioinformatics/btl567 17098774