Publications

1. Mitra, S., Malik, R., Wong, W., Rahman, A., Hartemink, A., Pritykin, Y., Dey, K., & Leslie, C. (2023) “Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis.” Nature Genetics, 56, April 2024. pp. 627–636. Online Access.
2. Chen, B., MacAlpine, H., Hartemink, A., & MacAlpine, D. (2023) “Spatiotemporal kinetics of CAF-1–dependent chromatin maturation ensures transcription fidelity during S-phase.” Genome Research, 33, December 2023. pp. 2108–2118. [Supp. Info.]
3. Luo, K., Zhong, J., Safi, A., Hong, L., Tewari, A., Song, L., Reddy, T., Ma, L., Crawford, G., & Hartemink, A. (2022) “Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.” Genome Research, 32, June 2022. pp. 1183–1198. [Supp. Info.]
4. Li, Y., Hartemink, A., & MacAlpine, D. (2021) “Cell-cycle–dependent chromatin dynamics at replication origins.” Genes, 12, December 2021. pp. 1998:1–13.
5. Mitra, S., Zhong, J., Tran, T., MacAlpine, D., & Hartemink, A. (2021) “RoboCOP: Jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data.” Nucleic Acids Research, 49, August 2021. pp. 7925–7938. [Supp. Info.]
6. Tran, T., MacAlpine, H., Tripuraneni, V., Mitra, S., MacAlpine, D., & Hartemink, A. (2021) “Linking the dynamics of chromatin occupancy and transcription with predictive models.” Genome Research, 31, June 2021. pp. 1035–1046. [Supp. Info.]
7. Tripuraneni, V., Memisoglu, G., MacAlpine, H., Tran, T., Zhu, W., Hartemink, A., Haber, J., & MacAlpine, D. (2021) “Local nucleosome dynamics and eviction following a double-strand break are reversible by NHEJ-mediated repair in the absence of DNA replication.” Genome Research, 31, May 2021. pp. 775–788. [Supp. Info.]
8. Mitra, S., Zhong, J., MacAlpine, D. & Hartemink, A. (2020) “RoboCOP: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy.” Research in Computational Molecular Biology 2020 (RECOMB20), Lecture Notes in Bioinformatics, Schwartz, R., ed. 12074, May 2020. pp. 136–151. [Supp. Info.]
9. McDowell, I., Barrera, A., D'Ippolito, A., Vockley, C., Hong, L., Leichter, S., Bartelt, L., Majoros, W., Song, L., Safi, A., Koçak, D., Gersbach, C., Hartemink, A., Crawford, G., Engelhardt, B., & Reddy, T. (2018) “Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding.” Genome Research, 28, September 2018. pp. 1272–1284.
10. Welch, J., Hartemink, A., & Prins, J. (2017) “MATCHER: Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.” Genome Biology, 18, 24 July 2017. 138 (pp. 1–19). [Supp. Info.]
11. Welch, J., Hartemink, A., & Prins, J. (2017) “E pluribus unum: United states of single cells.” Research in Computational Molecular Biology 2017 (RECOMB17), Lecture Notes in Bioinformatics, Sahinalp, S.C., ed. 10229, May 2017. pp. 400–401.
12. Mayhew, M., Iversen, E., & Hartemink, A. (2017) “Characterization of dependencies between growth and division in budding yeast.” Journal of the Royal Society Interface, 14, February 2017. 20160993 (pp. 1–12).
13. Sparks, E., Drapek, C., Gaudinier, A., Li, S., Ansariola, M., Shen, N., Hennacy, J., Zhang, J., Turco, G., Petricka, J., Foret, J., Hartemink, A., Gordân, R., Megraw, M., Brady, S., & Benfey, P. (2016) “Establishment of expression in the SHORTROOT-SCARECROW transcriptional cascade through opposing activities of both activators and repressors.” Developmental Cell, 39, 5 December 2016. pp. 585–596.
14. Welch, J., Hartemink, A., & Prins, J. (2016) “SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Genome Biology, 17, 23 May 2016. 106 (pp. 1–15).
15. Welch, J., Liu, Z., Wang, L., Lu, J., Lerou, P., Purvis, J., Qian, L., Hartemink, A., & Prins, J. (2016) “SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Research in Computational Molecular Biology 2016 (RECOMB16), Lecture Notes in Bioinformatics, Singh, M., ed. 9649, April 2016. pp. 239–240.
16. Zhong, J., Luo, K., Winter, P., Crawford, G., Iversen, E., & Hartemink, A. (2016) “Mapping nucleosome positions using DNase-seq.” Genome Research, 26, March 2016. pp. 351–364.
17. Zhang, Y., Henao, R., Carin, L., Zhong, J., & Hartemink, A. (2016) “Learning a hybrid architecture for sequence regression and annotation.” AAAI Conference on Artificial Intelligence 2016 (AAAI16), February 2016. pp. 1415–1421. Also available at arXiv, arXiv:1512.05219.
18. Scholl, Z., Zhong, J., & Hartemink, A. (2015) “Chromatin interactions correlate with local transcriptional activity in Saccharomyces cerevisiae.” bioRxiv, bioRxiv:021725.
19. Belsky, J., MacAlpine, H., Lubelsky, Y., Hartemink, A., & MacAlpine, D. (2015) “Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assembly.” Genes and Development, 29, 15 January 2015. pp. 212–224.
20. Pfenning, A., Hara, E., Whitney, O., Rivas, M., Wang, R., Roulhac, P., Howard, J., Wirthlin, M., Lovell, P., Ganapathy, G., Mouncastle, J., Moseley, M., Thompson, J., Soderblom, E., Iriki, A., Kato, M., Gilbert, M., Zhang, G., Bakken, T., Bongaarts, A., Bernard, A., Lein, E., Mello, C., Hartemink, A., & Jarvis, E. (2014) “Convergent transcriptional specializations in the brains of humans and song-learning birds.” Science, 346, 12 December 2014. pp. 1256846-1–13. [Author Summary] [Kavli Biggest Science Stories 2014] [Nature] [Science] [New Scientist] [Washington Post] [MIT News]
21. Whitney, O., Pfenning, A., Howard, J., Blatti, C., Liu, F., Ward, J., Wang, R., Audet, J.-N., Kellis, M., Mukherjee, S., Sinha, S., Hartemink, A., West, A., & Jarvis, E. (2014) “Core and region-enriched networks of behaviorally regulated genes and the singing genome.” Science, 346, 12 December 2014. pp. 1256780-1–11. [Author Summary]
22. Zhong, J., Wasson, T., & Hartemink, A. (2014) “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” Bioinformatics, 30, 15 October 2014. pp. 2868–2874.
23. Zhong, J., Wasson, T., & Hartemink, A. (2014) “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” Research in Computational Molecular Biology 2014 (RECOMB14), Lecture Notes in Bioinformatics, Sharan, R., ed. 8394, April 2014. pp. 433–447.
24. Meyer, P., Siwo, G., Zeevi, D., Sharon, E., Norel, R., DREAM6 Promoter Prediction Consortium, Segal, E., & Stolovitsky, G. (2013) “Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach.” Genome Research, 23, November 2013. pp. 1928–1937.
25. Mordelet, F., Horton, J., Hartemink, A., Engelhardt, B., & Gordân, R. (2013) “Stability selection for regression-based models of transcription factor-DNA binding specificity.” Intelligent Systems in Molecular Biology 2013 (ISMB13), Bioinformatics, 29, July 2013. pp. i117–i125.
26. Mayhew, M. & Hartemink, A. (2013) “Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae.” IEEE International Symposium on Biomedical Imaging 2013: From Nano to Macro (ISBI 2013), April 2013. pp. 1062–1065.
27. Guo, X., Bernard, A., Orlando, D., Haase, S., & Hartemink, A. (2013) “Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.” PNAS, 110, 5 March 2013. pp. E968–E977. [Deconvolution Website] [Author Summary] [Supp. Info.]
28. Perez-Pinera, P., Ousterout, D., Brunger, J., Farin, A., Glass, K., Guilak, F., Crawford, G., Hartemink, A., & Gersbach, C. (2013) “Synergistic and tunable gene activation in human cells by combinations of synthetic transcription factors.” Nature Methods, 10, 3 February 2013. pp. 239–242. [Supp. Info.]
29. Luo, K. & Hartemink, A. (2013) “Using DNase digestion data to accurately identify transcription factor binding sites.” In Pacific Symposium on Biocomputing 2013 (PSB13), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 80–91. [Supp. Info.] [Code]
30. Landt, S., Marinov, G., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B., Bickel, P., Brown, B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Euskirchen, G., Fisher-Aylor, K., Gerstein, M., Gertz, J., Hartemink, A., Hoffman, M., Iyer, V., Jung, Y., Karmakar, S., Kellis, M., Kharchenko, P., Li, Q., Liu, T., Liu, X., Ma, L., Milosavljevic, A., Myers, R., Park, P., Pazin, M., Perry, M., Raha, D., Reddy, T., Rozowsky, J., Shoresh, N., Sidow, A., Slattery, M., Stammatoyonnopoulous, J., Tolstorukov, M., White, K., Xi, S., Farnham, P., Lieb, J., Wold, B., & Snyder, M. (2012) “ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.” Genome Research, 22, September 2012. pp. 1813–1831.
31. Mayhew, M., Guo, X., Haase, S., & Hartemink, A. (2012) “Close encounters of the collaborative kind.” IEEE Computer, Special Issue on Computationally Driven Experimental Biology, 45, March 2012. pp. 24–30. [Cover Feature]
32. Guo, X., Bulyk, M., & Hartemink, A. (2012) “Intrinsic disorder within and flanking the DNA-binding domains of human transcription factors.” In Pacific Symposium on Biocomputing 2012 (PSB12), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 104–115.
33. Meyer, P., Alexopoulos, L., Bonk, T., Califano, A., Cho, C., de la Fuente, A., de Graaf, D., Hartemink, A., Hoeng, J., Ivanov, N., Koeppl, H., Linding, R., Marbach, D., Norel, R., Peitsch, M., Rice, J., Royyuru, A., Schacherer, F., Sprengel, J., Stolle, K., Vitkup, D., & Stolovitzky, G. (2011) “Verification of systems biology research in the age of collaborative competition.” Nature Biotechnology, 29, September 2011. pp. 811–815.
34. Mayhew, M., Robinson, J., Jung, B., Haase, S., & Hartemink, A. (2011) “A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.” Intelligent Systems in Molecular Biology 2011 (ISMB11), Bioinformatics, 27, July 2011. pp. i295–i303.
35. Miller, H., Robinson, T., Gordân, R., Hartemink, A., & Garcia-Blanco, M. (2011) “Identification of Tat-SF1 cellular targets by exon array analysis reveals dual roles in transcription and splicing.” RNA, 17, April 2011. pp. 665–674.
36. Robinson, J. & Hartemink, A. (2010) “Learning non-stationary dynamic Bayesian networks.” Journal of Machine Learning Research, 11, December 2010. pp. 3647–3680.
37. Gordân, R., Narlikar, L., & Hartemink, A. (2010) “Finding regulatory DNA motifs using alignment-free evolutionary conservation information.” Nucleic Acids Research, 38, April 2010. p. e90. [Supp. Info.]
38. MacAlpine, H., Gordân, R., Powell, S., Hartemink, A., & MacAlpine, D. (2010) “Drosophila ORC localizes to open chromatin and marks sites of cohesin complex loading.” Genome Research, 20, February 2010. pp. 201–211.
39. Orlando, D., Iversen, E., Hartemink, A., & Haase, S. (2009) “A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.” Annals of Applied Statistics, 3, December 2009. pp. 1521–1541.
40. Wasson, T. & Hartemink, A. (2009) “An ensemble model of competitive multi-factor binding of the genome.” Genome Research, 19, November 2009. pp. 2101–2112.
41. Gordân, R., Hartemink, A., & Bulyk, M. (2009) “Distinguishing direct versus indirect transcription factor-DNA interactions.” Genome Research, 19, November 2009. pp. 2090–2100. [Supp. Info.]
42. Guo, X. & Hartemink, A. (2009) “Domain-oriented edge-based alignment of protein interaction networks.” Intelligent Systems in Molecular Biology 2009 (ISMB09), Bioinformatics, 25, 15 June 2009. pp. i240–i246.
43. Robinson, J. & Hartemink, A. (2009) “Non-stationary dynamic Bayesian networks.” In Advances in Neural Information Processing Systems 21 (NIPS08), Koller, D., Schuurmans, D., Bengio, Y., & Bottou, L., eds. MIT Press: Cambridge, MA. pp. 1369–1376. [Appendix]
44. 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, 12 June 2008. pp. 944–947. [Supp. Info.]
45. Gordân, R., Narlikar, L., & Hartemink, A. (2008) “A fast, alignment-free, conservation-based method for transcription factor binding site discovery.” Research in Computational Molecular Biology 2008 (RECOMB08), Lecture Notes in Bioinformatics, Vingron, M. & Wong, L., eds. 4955, April 2008. pp. 98–111. [Supp. Info.]
46. Gordân, R. & Hartemink, A. (2008) “Using DNA duplex stability information for transcription factor binding site discovery.” In Pacific Symposium on Biocomputing 2008 (PSB08), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 453–464. [Supp. Info.]
47. Lüdi, P., Dietrich, F., Weidman, J., Bosko, J., Jirtle, R., & Hartemink, A. (2007) “Computational and experimental identification of novel human imprinted genes.” Genome Research, 17, December 2007. pp. 1723–1730. [Supp. Info.] [Cover] [Nature Reviews Genetics] [Science] [AP] [Wired]
48. Narlikar, L., Gordân, R., & Hartemink, A. (2007) “A nucleosome-guided map of transcription factor binding sites in yeast.” PLoS Computational Biology, 3, November 2007. pp. 2199–2208.
49. Bernard, A., Vaughn, D., & Hartemink, A. (2007) “Reconstructing the topology of protein complexes.” Research in Computational Molecular Biology 2007 (RECOMB07), Lecture Notes in Bioinformatics, Speed, T. & Huang, H., eds. 4453, April 2007. pp. 32–46.
50. Narlikar, L., Gordân, R., & Hartemink, A. (2007) “Nucleosome occupancy information improves de novo motif discovery.” Research in Computational Molecular Biology 2007 (RECOMB07), Lecture Notes in Bioinformatics, Speed, T. & Huang, H., eds. 4453, April 2007. pp. 107–121. [Supp. Info.]
51. Orlando, D., Lin, C., Bernard, A., Iversen, E., Hartemink, A., & Haase, S. (2007) “A probabilistic model for cell cycle distributions in synchrony experiments.” RECOMB Satellite Conference on Systems Biology 2006, Cell Cycle, 6, February 2007. pp. 478–488.
52. Smith, V., Yu, J., Smulders, T., Hartemink, A., & Jarvis, E. (2006) “Computational inference of neural information flow networks.” PLoS Computational Biology, 2, November 2006. pp. 1436–1449. [Supp. Info.] [Code] [Most Viewed Research Article at PLoS Computational Biology]
53. Bernard, A. & Hartemink, A. (2006) “Evaluating algorithms for learning biological networks.” DREAM Workshop, September 2006.
54. Narlikar, L., Gordân, R., Ohler, U., & Hartemink, A. (2006) “Informative priors based on transcription factor structural class improve de novo motif discovery.” Intelligent Systems in Molecular Biology 2006 (ISMB06), Bioinformatics, 22, July 2006. pp. e384–e392. [Supp. Info.] [Code] [Input Data]
55. Hartemink, A. (2006) “Bayesian networks and informative priors: Transcriptional regulatory network models.” In Bayesian Inference for Gene Expression and Proteomics, Do, K.-A., Müller, P., & Vannucci, M., eds. Cambridge University Press: Cambridge, UK. pp. 401–424.
56. Narlikar, L. & Hartemink, A. (2006) “Sequence features of DNA binding sites reveal structural class of associated transcription factor.” Bioinformatics, 22, January 2006. pp. 157–163.
57. Pratapa, P., Patz, E., & Hartemink, A. (2006) “Finding diagnostic biomarkers in proteomic spectra.” In Pacific Symposium on Biocomputing 2006 (PSB06), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 279–290. [Larger Figs.]
58. Krishnapuram, B., Williams, D., Xue, Y., Carin, L., Figueiredo, M., & Hartemink, A. (2005) “Active learning of features and labels.” Learning with Multiple Views Workshop at ICML05, August 2005.
59. Lüdi, P., Hartemink, A., & Jirtle, R. (2005) “Genome-wide prediction of imprinted murine genes.” Genome Research, 15, June 2005. pp. 875–884.
60. Krishnapuram, B., Figueiredo, M., Carin, L., & Hartemink, A. (2005) “Sparse multinomial logistic regression: Fast algorithms and generalization bounds.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27, June 2005. pp. 957–968. [Code]
61. Hartemink, A. (2005) “Reverse engineering gene regulatory networks.” Nature Biotechnology, 23, May 2005. pp. 554–555.
62. Yin, P. & Hartemink, A. (2005) “Theoretical and practical advances in genome halving.” Bioinformatics, 21, April 2005. pp. 869–879.
63. Bernard, A. & Hartemink, A. (2005) “Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data.” In Pacific Symposium on Biocomputing 2005 (PSB05), Altman, R., Dunker, A.K., Hunter, L., Jung, T., & Klein, T., eds. World Scientific: New Jersey. pp. 459–470. [Supp. Info.]
64. Krishnapuram, B., Williams, D., Xue, Y., Hartemink, A., Carin, L., & Figueiredo, M. (2005) “On semi-supervised classification.” In Advances in Neural Information Processing Systems 17 (NIPS04), Saul, L., Weiss, Y., & Bottou, L., eds. MIT Press: Cambridge, MA. pp. 721–728.
65. Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2004) “Advances to Bayesian network inference for generating causal networks from observational biological data.” Bioinformatics, 20, December 2004. pp. 3594–3603.
66. Krishnapuram, B., Hartemink, A., Carin, L., & Figueiredo, M. (2004) “A Bayesian approach to joint feature selection and classifier design.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 26, September 2004. pp. 1105–1111.
67. Krishnapuram, B., Carin, L., & Hartemink, A. (2004) “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.” Journal of Computational Biology, 11, March 2004. pp. 227–242.
68. Krishnapuram, B., Carin, L., & Hartemink, A. (2004) “Gene expression analysis: Joint feature selection and classifier design.” In Kernel Methods in Computational Biology, Schölkopf, B., Tsuda, K., & Vert, J.-P., eds. MIT Press: Cambridge, MA. pp. 299–318.
69. Liu, Q., Krishnapuram, B., Pratapa, P., Liao, X., Hartemink, A., & Carin, L. (2003) “Identification of differentially expressed proteins using MALDI-TOF mass spectra.” ASILOMAR Conference: Biological Aspects of Signal Processing, November 2003.
70. Krishnapuram, B., Carin, L., & Hartemink, A. (2003) “Joint classifier and kernel design.” Kernel Methods in Bioinformatics Workshop at RECOMB03, April 2003.
71. Krishnapuram, B., Carin, L., & Hartemink, A. (2003) “Joint classifier and feature optimization for cancer diagnosis using gene expression data.” In Research in Computational Molecular Biology 2003 (RECOMB03), Vingron, M., Pevzner, P., Istrail, S. & Waterman, M., eds. ACM: New York. pp. 167–175.
72. Smith, V., Jarvis, E., & Hartemink, A. (2003) “Influence of network topology and data collection on network inference.” In Pacific Symposium on Biocomputing 2003 (PSB03), Altman, R., Dunker, A.K., Hunter, L., Jung, T., & Klein, T., eds. World Scientific: New Jersey. pp. 164–175.
73. Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2002) “Using Bayesian network inference algorithms to recover molecular genetic regulatory networks.” International Conference on Systems Biology 2002 (ICSB02), December 2002.
74. Jarvis, E., Smith, V., Wada, K., Rivas, M., McElroy, M., Smulders, T., Carninci, P., Hayashisaki, Y., Dietrich, F., Wu, X., McConnell, P., Yu, J., Wang, P., Hartemink, A., & Lin, S. (2002) “A framework for integrating the songbird brain.” Journal of Comparative Physiology A, 188, December 2002. pp. 961–980.
75. Krishnapuram, B., Hartemink, A., & Carin, L. (2002) “Applying logistic regression and RVM to achieve accurate probabilistic cancer diagnosis from gene expression profiles.” GENSIPS: Workshop on Genomic Signal Processing and Statistics, October 2002.
76. Smith, V., Jarvis, E., & Hartemink, A. (2002) “Evaluating functional network inference using simulations of complex biological systems.” Intelligent Systems in Molecular Biology 2002 (ISMB02), Bioinformatics, 18:S1. pp. S216–S224.
77. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2002) “Bayesian methods for elucidating genetic regulatory networks.” IEEE Intelligent Systems, special issue on Intelligent Systems in Biology, 17, March/April 2002. pp. 37–43.
78. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2002) “Combining location and expression data for principled discovery of genetic regulatory networks.” In Pacific Symposium on Biocomputing 2002 (PSB02), Altman, R., Dunker, A.K., Hunter, L., Lauderdale, K., & Klein, T., eds. World Scientific: New Jersey. pp. 437–449.
79. Hartemink, A. (2001) “Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks.” Massachusetts Institute of Technology, Ph.D. dissertation.
80. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2001) “Maximum likelihood estimation of optimal scaling factors for expression array normalization.” SPIE International Symposium on Biomedical Optics 2001 (BiOS01). In Microarrays: Optical Technologies and Informatics, Bittner, M., Chen, Y., Dorsel, A., & Dougherty, E., eds. Proceedings of SPIE, 4266. pp. 132–140.
81. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2001) “Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.” In Pacific Symposium on Biocomputing 2001 (PSB01), Altman, R., Dunker, A.K., Hunter, L., Lauderdale, K., & Klein, T., eds. World Scientific: New Jersey. pp. 422–433.
82. Hartemink, A., Mikkelsen, T., & Gifford, D. (2000) “Simulating biological reactions: A modular approach.” DNA Based Computers V. Winfree, E. & Gifford, D., eds. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 54, American Mathematical Society. pp. 111–121.
83. Schechter, S., Parnell, T., & Hartemink, A. (1999) “Anonymous authentication of membership in dynamic groups.” Financial Cryptography '99. Franklin, M., ed. Lecture Notes in Computer Science, 1648, Springer-Verlag. pp. 184–195.
84. Hartemink, A., Gifford, D., & Khodor, J. (1999) “Automated constraint-based nucleotide sequence selection for DNA computation.” Biosystems, 52, October 1999, Elsevier Press. pp. 227–235.
85. Hartemink, A. & Gifford, D. (1999) “Thermodynamic simulation of deoxyoligonucleotide hybridization for DNA computation.” DNA Based Computers III. Rubin, H. & Wood, D., eds. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 48, American Mathematical Society. pp. 25–38.