Abstract
With the development and application of high-throughput technologies, an enormous amount of biological data has been produced in the past few years. These large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transcription factors and histone modifications. We demonstrate that machine learning provides powerful tools to quantitatively relate gene expression with transcription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analyzing and processing biological data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andre D, Koza J (1996) A parallel implementation of genetic programming that achieves super-linear performance. Proceedings of the international conference on parallel and distributed processing techniques and applications, CSREA Press, Sunnyvale:A.H.R.
Berger S (2007) The complex language of chromatin regulation during transcription. Nature 447(7143):407–412
Chadwick L (2012) The NIH roadmap epigenomics program data resource. Epigenomics 4(3):317–324
Chen X, Xu H, Yuan P, Fang F, Huss M, Vega V, Wong E, Orlov Y, Zhang W, Jiang J (2008) Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133(6):1106–1117
Cheng C, Gerstein M (2012) Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells. Nucleic Acids Res 40(2):553–568
Cheng C, Li L (2008) Systematic identification of cell cycle regulated transcription factors from microarray time series data. BMC Genomics 9:116
Cheng C, Shou C, Yip K, Gerstein M (2011a) Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors. Genome Biol 12(11):R111
Cheng C, Yan K, Yip K, Rozowsky J, Alexander R, Shou C, Gerstein M (2011b) A statistical framework for modeling gene expression using chromatin features and application to modencode datasets. Genome Biol 12(2):R15
Cheng C, Alexander R, Min R, Leng J, Yip K, Rozowsky J, Yan K, Dong X, Djebali S, Ruan Y (2012) Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res 22(9):1658–1667
Cheng C, Ung M, Grant G, Whitfield M (2013) Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding rnas. PLoS Computational Biol 9(7):e1003132
Cloonan N, Forrest A, Kolle G, Gardiner B, Faulkner G, Brown M, Taylor D, Steptoe A, Wani S, Bethel G (2008) Stem cell transcriptome profiling via massive-scale mrna sequencing. Nat Methods 5(7):613–619
Creyghton M, Cheng A, Welstead G, Kooistra T, Carey B, Steine E, Hanna J, Lodato M, Frampton G, Sharp P (2010) Histone h3k27ac separates active from poised enhancers and predicts developmental state. Proceedings of the National Academy of Sciences of the United States of America 107(50):21,931–21,936
Eggermont J, Kok J, Kosters W (2004) Genetic programming for data classification:partitioning the search space. Proceedings of the 2004 ACM symposium on Applied computing ACM Press, Nicosia, pp 1001–1005
ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74
Farnham P (2009)Insights from genomic profiling of transcription factors. Nat Rev Genet 10(9):605–616
Gerstein M, Lu Z, Nostrand EV, Cheng C, Arshinoff B, Liu T, Yip K, Robilotto R, Rechtsteiner A, Ikegami K (2010) Integrative analysis of the caenorhabditis elegans genome by the modencode project. Science 330(6012):1775–1787
Ghosh P, Bagchi M (2009) Qsar modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection. Curr Med Chem 16(30):4032–4048
Johnson D, Mortazavi A, Myers R, Wold B (2007) Genome-wide mapping of in vivo protein-dna interactions. Science 316(5830):1497–1502
Kandoth C, McLellan M, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael J, Wyczalkowski M (2013) Mutational landscape and significance across 12 major cancer types. Nature 502(7471):333–339
Khan M, Alam M (2012) A survey of application: genomics and genetic programming, a new frontier. Genomics 100(2):65–71
Kotanchek M, Smits G, Vladislavleva E (2006) Pursuing the pareto paradigm tournaments, algorithm variations & ordinal optimization. In: Riolo RL, Soule T, Worzel B (eds) Genetic programming theory and practice IV, genetic and evolutionary computation, vol 5. Springer, Ann Arbor, pp 167–185. doi:10.1007/978-0-387-49650-4–11
Kotanchek ME,Vladislavleva E, Smits G(2012) Symbolic regression is not enough: It takes a village to raise a model. In: Riolo R, Vladislavleva E, Ritchie MD, Moore JH (eds) Genetic programming theory and practice X, genetic and evolutionary computation. Springer, Ann Arbor, pp 187–203. doi:10.1007/978-1-4614-6846-2-13, http://dx.doi.org/10.1007/978-1-4614-6846-2-13
Koza JR, Mydlowec W, Lanza G, Yu J, Keane MA (2001) Automatic synthesis of both the topology and sizing of metabolic pathways using genetic programming. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann, San Francisco, pp 57–65. http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/koza-gecco2001.pdf
Kurdistani S, Tavazoie S, Grunstein M (2004) Mapping global histone acetylation patterns to gene expression. Cell 117(6):721–733
Lander E, Linton L, Birren B, Nusbaum C, Zody M, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921
Li B, Carey M, Workman J (2007) The role of chromatin during transcription. Cell 128(4):707–719
Maston G, Evans S, Green M (2006) Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet 7:29–59
Mikkelsen T, Ku M, Jaffe D, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim T, Koche R (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448(7153):553–560
Mitra A, Almal A, George B, Fry D, Lenehan P, Pagliarulo V, Cote R, Datar R, Worzel W (2006) The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer. BMC Cancer 6:159
Moore J, White B (2006) Genome-wide genetic analysis using genetic programming: the critical need for expert knowledge. In: Riolo RL, Soule T, Worzel B (eds) Genetic programming theory and practice IV, Springer, genetic and evolutionary computation, vol 5, pp 11–28
Orlando D, Lin C, Bernard A, Wang J, Socolar J, Iversen E, Hartemink A, Haase S (2008) Global control of cell-cycle transcription by coupled cdk and network oscillators. Nature 453(7197):944–947
Pennacchio L, Ahituv N, Moses A, Prabhakar S, Nobrega M, Shoukry M, Minovisky S, Dubchak I, Holt A, Lewis K (2006) In vivo enhancer analysis of human conserved non-coding sequences. Nature 444(7118):499–502
Pennacchio L, Bickmore W, Dean A, Nobrega M, Bejerano G (2013) Enhancers: five essential questions. Nat Rev Genet 14(4):288–295
Ren B, Robert F, Wyrick J, Aparicio O, Jennings E, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E (2000) Genome-wide location and function of dna binding proteins. Science 290(5500):2306–2309
Simon I, Barnett J, Hannett N, Harbison C, Rinaldi N, Volkert T, Wyrick J, Zeitlinger J, Gifford D, Jaakkola T (2001) Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106(6):697–708
Stamatoyannopoulos J, Snyder M, Hardison R, Ren B, Gingeras T, Gilbert D, Groudine M, Bender M, Kaul R, Canfield T (2012) An encyclopedia of mouse dna elements (mouse encode). Gen Biol 13(8):418
Stormo G (2000) Dna binding sites: representation and discovery. Bioinformatics 16(1):16–23
Strahl B, Allis C (2000) The language of covalent histone modifications. Nature 403(6765):41–45
Venter J, Adams M, Myers E, Li P, Mural R, Sutton G, Smith H, Yandell M, Evans C, Holt R (2001) The sequence of the human genome. Science 291(5507):1304–1351
Whitfield M, Sherlock G, Saldanha A, Murray J, Ball C, Alexander K, Matese J, Perou C, Hurt M, Brown P (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13(6):1977–2000
Worzel W, Yu J, Almal A, Chinnaiyan A (2009) Applications of genetic programming in cancer research. Int J Biochem Cell Biol 41(2):405–413
Yip K, Cheng C, Bhardwaj N, Brown J, Leng J, Kundaje A, Rozowsky J, Birney E, Bickel P, Snyder M (2012) Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome biol 13(9):R48
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cheng, C., Worzel, W. (2015). Application of Machine-Learning Methods to Understand Gene Expression Regulation. In: Riolo, R., Worzel, W., Kotanchek, M. (eds) Genetic Programming Theory and Practice XII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-16030-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-16030-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16029-0
Online ISBN: 978-3-319-16030-6
eBook Packages: Computer ScienceComputer Science (R0)