Alexander J. Hartemink
Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection
Current Appointments & Affiliations
- Professor in the Department of Computer Science, Computer Science, Trinity College of Arts & Sciences 2013
- Professor of Biology, Biology, Trinity College of Arts & Sciences 2013
- Bass Fellow, Computer Science, Trinity College of Arts & Sciences 2014
Contact Information
- 308 Research Drive, LSRC D239, Durham, NC 27708-0129
- Box 90129, Durham, NC 27708-0129
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amink@cs.duke.edu
(919) 660-6514
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ORC ID
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http://www.cs.duke.edu/~amink/
- Background
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Education, Training, & Certifications
- Ph.D., Massachusetts Institute of Technology 2001
- M.Phil., University of Oxford (United Kingdom) 1996
- B.S., Duke University 1994
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Duke Appointment History
- Professor in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2013 - 2016
- Director of the Computational Biology and Bioinformatics Program, Institutes and Centers, School of Medicine 2012 - 2015
- Investigator in the Institute for Genome Sciences & Policy, University Institutes and Centers, Institutes and Provost's Academic Units 2008 - 2014
- Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2008 - 2013
- Associate Professor in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2010 - 2013
- Associate Professor of Biology, Biology, Trinity College of Arts & Sciences 2012 - 2013
- Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2001 - 2008
- Recognition
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In the News
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FEB 22, 2016 -
JAN 6, 2015 -
DEC 10, 2014 -
MAR 24, 2014
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Awards & Honors
- Expertise
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Subject Headings
- Algorithms
- Alternative Splicing
- Amino Acid Motifs
- Artificial Intelligence
- Auditory Cortex
- Auditory Pathways
- Auditory Perception
- Base Sequence
- Bayes Theorem
- Binding Sites
- Binding, Competitive
- Biological Clocks
- Biological Markers
- Brain Mapping
- Cell Cycle
- Cell Cycle Proteins
- Cell Division
- Cell Line
- Chromatin
- Chromatin Immunoprecipitation
- Chromosomal Proteins, Non-Histone
- Chromosome Mapping
- Cluster Analysis
- Computational Biology
- Computer Simulation
- Conserved Sequence
- Cooperative Behavior
- Cyclin-Dependent Kinases
- Cyclins
- DNA
- DNA, Fungal
- DNA-Binding Proteins
- Data Interpretation, Statistical
- Databases, Genetic
- Databases, Protein
- Deoxyribonucleases
- Diagnosis, Computer-Assisted
- Drosophila Proteins
- Drosophila melanogaster
- Electrophysiology
- Evoked Potentials, Auditory
- Evolution, Molecular
- Exons
- Finches
- Flow Cytometry
- Forkhead Transcription Factors
- G1 Phase
- Galactose
- Gene Expression Profiling
- Gene Expression Regulation
- Gene Expression Regulation, Developmental
- Gene Expression Regulation, Fungal
- Gene Library
- Gene Targeting
- Genes, Fungal
- Genetic Engineering
- Genetic Linkage
- Genetic Variation
- Genome
- Genome, Fungal
- Genome, Human
- Genome-Wide Association Study
- Genomic Imprinting
- Genomics
- Helix-Loop-Helix Motifs
- High-Throughput Nucleotide Sequencing
- Histones
- Homeodomain Proteins
- Information Storage and Retrieval
- Information Theory
- Learning
- Linear Models
- Logistic Models
- Markov Chains
- Models, Biological
- Models, Genetic
- Models, Molecular
- Models, Neurological
- Models, Statistical
- Molecular Sequence Data
- Motor Activity
- Multivariate Analysis
- Mutation
- Neoplasms
- Neural Networks (Computer)
- Nucleic Acid Conformation
- Nucleosomes
- Oligonucleotide Array Sequence Analysis
- Origin Recognition Complex
- Pattern Recognition, Automated
- Periodicity
- Plasmids
- Probability
- Promoter Regions, Genetic
- Protein Array Analysis
- Protein Binding
- Protein Interaction Domains and Motifs
- Protein Interaction Mapping
- Protein Structure, Tertiary
- Proteins
- Proteome
- Proteomics
- Proto-Oncogene Proteins c-myc
- RNA
- Real-Time Polymerase Chain Reaction
- Regression Analysis
- Regulatory Sequences, Nucleic Acid
- Reproducibility of Results
- S Phase
- Saccharomyces cerevisiae
- Saccharomyces cerevisiae Proteins
- Sensitivity and Specificity
- Sequence Alignment
- Sequence Analysis, DNA
- Sequence Homology, Nucleic Acid
- Signal Transduction
- Software
- Songbirds
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
- Support Vector Machine
- Synaptic Transmission
- Systems Biology
- Thermodynamics
- Trans-Activators
- Transcription Factors
- Transcription, Genetic
- Transcriptional Activation
- Transcriptome
- Transfection
- Tumor Markers, Biological
- Vocalization, Animal
- Zinc Fingers
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Global Scholarship
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Expertise
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Teaching
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- Research
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Selected Grants
- IRES Track 1 IRTG Engaged in Dissecting and Reengineering the Regulatory Genome awarded by National Science Foundation 2019 - 2022
- Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health 2005 - 2021
- Exploring the Role of Dynamic Chromatin Occupancy in Transcriptional Regulation - Non-competing renewal III awarded by National Institutes of Health 2016 - 2021
- Decoding and Reprogramming the Corticosteroid Transcriptional Regulatory Network awarded by National Institutes of Health 2015 - 2018
- CRCNS: Neural Flow Networks in Songbirds awarded by National Institutes of Health 2005 - 2012
- Identification and Characterization of Epigenetically Labile Genes awarded by National Institutes of Health 2006 - 2010
- High-performance Computing System for Bioinformatics awarded by National Institutes of Health 2009 - 2010
- CAREER: Computational Methods for Learning Dynamic Networks of Regulation and Control awarded by National Science Foundation 2004 - 2009
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External Relationships
- Apostle, Inc.
- Publications & Artistic Works
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Selected Publications
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Books
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Welch, J. D., A. Hartemink, and J. F. Prins. E pluribus unum: United states of single cells. Vol. 10229 LNCS, 2017.
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Academic Articles
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McDowell, Ian C., Alejandro Barrera, Anthony M. D’Ippolito, Christopher M. Vockley, Linda K. Hong, Sarah M. Leichter, Luke C. Bartelt, et al. “Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding.” Genome Res 28, no. 9 (September 2018): 1272–84. https://doi.org/10.1101/gr.233346.117.Full Text Link to Item
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Welch, Joshua D., Alexander J. Hartemink, and Jan F. Prins. “MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.” Genome Biology 18, no. 1 (July 24, 2017): 138. https://doi.org/10.1186/s13059-017-1269-0.Full Text
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Mayhew, Michael B., Edwin S. Iversen, and Alexander J. Hartemink. “Characterization of dependencies between growth and division in budding yeast.” Journal of the Royal Society, Interface 14, no. 127 (February 2017). https://doi.org/10.1098/rsif.2016.0993.Full Text
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Sparks, Erin E., Colleen Drapek, Allison Gaudinier, Song Li, Mitra Ansariola, Ning Shen, Jessica H. Hennacy, et al. “Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors.” Dev Cell 39, no. 5 (December 5, 2016): 585–96. https://doi.org/10.1016/j.devcel.2016.09.031.Full Text Link to Item
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Welch, Joshua D., Alexander J. Hartemink, and Jan F. Prins. “SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Genome Biology 17, no. 1 (May 23, 2016): 106. https://doi.org/10.1186/s13059-016-0975-3.Full Text Open Access Copy
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Zhong, Jianling, Kaixuan Luo, Peter S. Winter, Gregory E. Crawford, Edwin S. Iversen, and Alexander J. Hartemink. “Mapping nucleosome positions using DNase-seq.” Genome Res 26, no. 3 (March 2016): 351–64. https://doi.org/10.1101/gr.195602.115.Full Text Link to Item
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Zhang, Y., R. Henao, L. Carin, J. Zhong, and A. J. Hartemink. “Learning a hybrid architecture for sequence regression and annotation.” 30th Aaai Conference on Artificial Intelligence, Aaai 2016, January 1, 2016, 1415–21.Open Access Copy
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Belsky, Jason A., Heather K. MacAlpine, Yoav Lubelsky, Alexander J. Hartemink, and David M. MacAlpine. “Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assembly.” Genes Dev 29, no. 2 (January 15, 2015): 212–24. https://doi.org/10.1101/gad.247924.114.Full Text Link to Item
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Pfenning, Andreas R., Erina Hara, Osceola Whitney, Miriam V. Rivas, Rui Wang, Petra L. Roulhac, Jason T. Howard, et al. “Convergent transcriptional specializations in the brains of humans and song-learning birds.” Science 346, no. 6215 (December 12, 2014): 1256846. https://doi.org/10.1126/science.1256846.Full Text Open Access Copy Link to Item
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Whitney, Osceola, Andreas R. Pfenning, Jason T. Howard, Charles A. Blatti, Fang Liu, James M. Ward, Rui Wang, et al. “Core and region-enriched networks of behaviorally regulated genes and the singing genome.” Science 346, no. 6215 (December 12, 2014): 1256780. https://doi.org/10.1126/science.1256780.Full Text Open Access Copy Link to Item
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Zhong, Jianling, Todd Wasson, and Alexander J. Hartemink. “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” Bioinformatics (Oxford, England) 30, no. 20 (October 2014): 2868–74. https://doi.org/10.1093/bioinformatics/btu408.Full Text
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Meyer, Pablo, Geoffrey Siwo, Danny Zeevi, Eilon Sharon, Raquel Norel, Raquel DREAM6 Promoter Prediction Consortium, Eran Segal, and Gustavo Stolovitzky. “Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach.” Genome Research 23, no. 11 (November 2013): 1928–37. https://doi.org/10.1101/gr.157420.113.Full Text
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Mordelet, Fantine, John Horton, Alexander J. Hartemink, Barbara E. Engelhardt, and Raluca Gordân. “Stability selection for regression-based models of transcription factor-DNA binding specificity.” Bioinformatics 29, no. 13 (July 1, 2013): i117–25. https://doi.org/10.1093/bioinformatics/btt221.Full Text Open Access Copy Link to Item
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Guo, Xin, Allister Bernard, David A. Orlando, Steven B. Haase, and Alexander J. Hartemink. “Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.” Proceedings of the National Academy of Sciences of the United States of America 110, no. 10 (March 2013): E968–77. https://doi.org/10.1073/pnas.1120991110.Full Text
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Perez-Pinera, Pablo, David G. Ousterout, Jonathan M. Brunger, Alicia M. Farin, Katherine A. Glass, Farshid Guilak, Gregory E. Crawford, Alexander J. Hartemink, and Charles A. Gersbach. “Synergistic and tunable human gene activation by combinations of synthetic transcription factors.” Nat Methods 10, no. 3 (March 2013): 239–42. https://doi.org/10.1038/nmeth.2361.Full Text Link to Item
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Hartemink, A. J., M. Kellis, W. S. Noble, and Z. Weng. “Epigenomics.” Pac Symp Biocomput, 2013, 65–68.Link to Item
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Luo, Kaixuan, and Alexander J. Hartemink. “Using DNase digestion data to accurately identify transcription factor binding sites.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2013, 80–91.
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Mayhew, M. B., and A. J. Hartemink. “Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae.” Proceedings International Symposium on Biomedical Imaging, January 1, 2013, 1062–65. https://doi.org/10.1109/ISBI.2013.6556661.Full Text
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Landt, Stephen G., Georgi K. Marinov, Anshul Kundaje, Pouya Kheradpour, Florencia Pauli, Serafim Batzoglou, Bradley E. Bernstein, et al. “ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.” Genome Res 22, no. 9 (September 2012): 1813–31. https://doi.org/10.1101/gr.136184.111.Full Text Link to Item
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Mayhew, M. B., X. Guo, S. B. Haase, and A. J. Hartemink. “Close encounters of the collaborative kind.” Computer 45, no. 3 (March 1, 2012): 24–30. https://doi.org/10.1109/MC.2012.85.Full Text
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Guo, Xin, Martha L. Bulyk, and Alexander J. Hartemink. “Intrinsic disorder within and flanking the DNA-binding domains of human transcription factors.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2012, 104–15.
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Meyer, Pablo, Leonidas G. Alexopoulos, Thomas Bonk, Andrea Califano, Carolyn R. Cho, Alberto de la Fuente, David de Graaf, et al. “Verification of systems biology research in the age of collaborative competition.” Nature Biotechnology 29, no. 9 (September 8, 2011): 811–15. https://doi.org/10.1038/nbt.1968.Full Text
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Mayhew, Michael B., Joshua W. Robinson, Boyoun Jung, Steven B. Haase, and Alexander J. Hartemink. “A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.” Bioinformatics (Oxford, England) 27, no. 13 (July 2011): i295–303. https://doi.org/10.1093/bioinformatics/btr244.Full Text
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Miller, Heather B., Timothy J. Robinson, Raluca Gordân, Alexander J. Hartemink, and Mariano A. Garcia-Blanco. “Identification of Tat-SF1 cellular targets by exon array analysis reveals dual roles in transcription and splicing.” Rna 17, no. 4 (April 2011): 665–74. https://doi.org/10.1261/rna.2462011.Full Text Link to Item
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Robinson, J. W., and A. J. Hartemink. “Learning non-stationary dynamic bayesian networks.” Journal of Machine Learning Research 11 (December 1, 2010): 3647–80.
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Gordân, Raluca, Leelavati Narlikar, and Alexander J. Hartemink. “Finding regulatory DNA motifs using alignment-free evolutionary conservation information.” Nucleic Acids Res 38, no. 6 (April 2010): e90. https://doi.org/10.1093/nar/gkp1166.Full Text Open Access Copy Link to Item
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MacAlpine, Heather K., Raluca Gordân, Sara K. Powell, Alexander J. Hartemink, and David M. MacAlpine. “Drosophila ORC localizes to open chromatin and marks sites of cohesin complex loading.” Genome Res 20, no. 2 (February 2010): 201–11. https://doi.org/10.1101/gr.097873.109.Full Text Link to Item
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Gordân, R., L. Narlikar, and A. J. Hartemink. “Finding regulatory DNA motifs using alignment-free evolutionary conservation information.” Nucleic Acids Research 38, no. 6 (January 4, 2010). https://doi.org/10.1093/nar/gkp1166.Full Text
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Robinson, J. W., and A. J. Hartemink. “Non-stationary dynamic Bayesian networks.” Advances in Neural Information Processing Systems 21 Proceedings of the 2008 Conference, December 1, 2009, 1369–76.
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Gordân, Raluca, Alexander J. Hartemink, and Martha L. Bulyk. “Distinguishing direct versus indirect transcription factor-DNA interactions.” Genome Res 19, no. 11 (November 2009): 2090–2100. https://doi.org/10.1101/gr.094144.109.Full Text Link to Item
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Wasson, Todd, and Alexander J. Hartemink. “An ensemble model of competitive multi-factor binding of the genome.” Genome Research 19, no. 11 (November 2009): 2101–12. https://doi.org/10.1101/gr.093450.109.Full Text
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Guo, Xin, and Alexander J. Hartemink. “Domain-oriented edge-based alignment of protein interaction networks.” Bioinformatics (Oxford, England) 25, no. 12 (June 2009): i240–46. https://doi.org/10.1093/bioinformatics/btp202.Full Text Open Access Copy
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Orlando, David A., Edwin S. Iversen, Alexander J. Hartemink, and Steven B. Haase. “A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.” The Annals of Applied Statistics 3, no. 4 (January 2009): 1521–41. https://doi.org/10.1214/09-aoas264.Full Text Open Access Copy
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Bulyk, M. L., A. J. Hartemink, E. Fraenkel, and Y. Mandel-Gutfreund. “Protein-nucleic acid interactions: Integrating structure, sequence, and function.” Pacific Symposium on Biocomputing 2008, Psb 2008, December 1, 2008, 438–40.
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Gordân, R., L. Narlikar, and A. J. Hartemink. “A fast, alignment-free, conservation-based method for transcription factor binding site discovery.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4955 LNBI (July 21, 2008): 98–111. https://doi.org/10.1007/978-3-540-78839-3_9.Full Text
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Orlando, David A., Charles Y. Lin, Allister Bernard, Jean Y. Wang, Joshua E. S. Socolar, Edwin S. Iversen, Alexander J. Hartemink, and Steven B. Haase. “Global control of cell-cycle transcription by coupled CDK and network oscillators.” Nature 453, no. 7197 (June 2008): 944–47. https://doi.org/10.1038/nature06955.Full Text
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Gordân, Raluca, and Alexander J. Hartemink. “Using DNA duplex stability information for transcription factor binding site discovery.” Pac Symp Biocomput, 2008, 453–64.Link to Item
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Luedi, Philippe P., Fred S. Dietrich, Jennifer R. Weidman, Jason M. Bosko, Randy L. Jirtle, and Alexander J. Hartemink. “Computational and experimental identification of novel human imprinted genes.” Genome Res 17, no. 12 (December 2007): 1723–30. https://doi.org/10.1101/gr.6584707.Full Text Link to Item
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Narlikar, Leelavati, Raluca Gordân, and Alexander J. Hartemink. “A nucleosome-guided map of transcription factor binding sites in yeast.” Plos Comput Biol 3, no. 11 (November 2007): e215. https://doi.org/10.1371/journal.pcbi.0030215.Full Text Open Access Copy Link to Item
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Orlando, David A., Charles Y. Lin, Allister Bernard, Edwin S. Iversen, Alexander J. Hartemink, and Steven B. Haase. “A probabilistic model for cell cycle distributions in synchrony experiments.” Cell Cycle (Georgetown, Tex.) 6, no. 4 (February 12, 2007): 478–88. https://doi.org/10.4161/cc.6.4.3859.Full Text
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Bernard, A., D. S. Vaughn, and A. J. Hartemink. “Reconstructing the topology of protein complexes.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4453 LNBI (January 1, 2007): 32–46. https://doi.org/10.1007/978-3-540-71681-5_3.Full Text
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Narlikar, L., R. Gordân, and A. J. Hartemink. “Nucleosome occupancy information improves de novo motif discovery.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4453 LNBI (January 1, 2007): 107–21. https://doi.org/10.1007/978-3-540-71681-5_8.Full Text
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Smith, V Anne, Jing Yu, Tom V. Smulders, Alexander J. Hartemink, and Erich D. Jarvis. “Computational inference of neural information flow networks.” Plos Comput Biol 2, no. 11 (November 24, 2006): e161. https://doi.org/10.1371/journal.pcbi.0020161.Full Text Open Access Copy Link to Item
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Narlikar, Leelavati, Raluca Gordân, Uwe Ohler, and Alexander J. Hartemink. “Informative priors based on transcription factor structural class improve de novo motif discovery.” Bioinformatics 22, no. 14 (July 15, 2006): e384–92. https://doi.org/10.1093/bioinformatics/btl251.Full Text Link to Item
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Narlikar, Leelavati, and Alexander J. Hartemink. “Sequence features of DNA binding sites reveal structural class of associated transcription factor.” Bioinformatics (Oxford, England) 22, no. 2 (January 2006): 157–63. https://doi.org/10.1093/bioinformatics/bti731.Full Text
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Pratapa, Pallavi N., Edward F. Patz, and Alexander J. Hartemink. “Finding diagnostic biomarkers in proteomic spectra.” Pac Symp Biocomput, 2006, 279–90.Link to Item
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Krishnapuram, Balaji, Lawrence Carin, Mário A. T. Figueiredo, and Alexander J. Hartemink. “Sparse multinomial logistic regression: fast algorithms and generalization bounds.” Ieee Transactions on Pattern Analysis and Machine Intelligence 27, no. 6 (June 2005): 957–68. https://doi.org/10.1109/tpami.2005.127.Full Text
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Luedi, Philippe P., Alexander J. Hartemink, and Randy L. Jirtle. “Genome-wide prediction of imprinted murine genes.” Genome Research 15, no. 6 (June 2005): 875–84. https://doi.org/10.1101/gr.3303505.Full Text
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Hartemink, Alexander J. “Reverse engineering gene regulatory networks.” Nature Biotechnology 23, no. 5 (May 2005): 554–55. https://doi.org/10.1038/nbt0505-554.Full Text
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Yin, Peng, and Alexander J. Hartemink. “Theoretical and practical advances in genome halving.” Bioinformatics (Oxford, England) 21, no. 7 (April 2005): 869–79. https://doi.org/10.1093/bioinformatics/bti107.Full Text
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Bernard, Allister, and Alexander J. Hartemink. “Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2005, 459–70.
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Hartemink, Alexander J., and Eran Segal. “Joint learning from multiple types of genomic data.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2005, 445–46.
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Yu, Jing, V Anne Smith, Paul P. Wang, Alexander J. Hartemink, and Erich D. Jarvis. “Advances to Bayesian network inference for generating causal networks from observational biological data.” Bioinformatics 20, no. 18 (December 12, 2004): 3594–3603. https://doi.org/10.1093/bioinformatics/bth448.Full Text Open Access Copy Link to Item
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Krishnapuram, Balaji, Alexander J. Hartemink, Lawrence Carin, and Mário A. T. Figueiredo. “A Bayesian approach to joint feature selection and classifier design.” Ieee Transactions on Pattern Analysis and Machine Intelligence 26, no. 9 (September 2004): 1105–11. https://doi.org/10.1109/tpami.2004.55.Full Text
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Krishnapuram, Balaji, Lawrence Carin, and Alexander J. Hartemink. “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.” Journal of Computational Biology : A Journal of Computational Molecular Cell Biology 11, no. 2–3 (January 2004): 227–42. https://doi.org/10.1089/1066527041410463.Full Text
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Liu, Q., B. Krishnapuram, P. Pratapa, X. Liao, A. Hartemink, and L. Carin. “Identification of differentially expressed proteins using MALDI-TOF mass spectra.” Conference Record of the Asilomar Conference on Signals, Systems and Computers 2 (December 1, 2003): 1323–27.
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Krishnapuram, B., L. Carin, and A. J. Hartemink. “Joint classifier and feature optimization for cancer diagnosis using gene expression data.” Proceedings of the Annual International Conference on Computational Molecular Biology, Recomb, January 1, 2003, 167–75. https://doi.org/10.1145/640075.640097.Full Text
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Smith, V Anne, Erich D. Jarvis, and Alexander J. Hartemink. “Influence of network topology and data collection on network inference.” Pac Symp Biocomput, 2003, 164–75.Open Access Copy Link to Item
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Jarvis, E. D., V. A. Smith, K. Wada, M. V. Rivas, M. McElroy, T. V. Smulders, P. Carninci, et al. “A framework for integrating the songbird brain.” J Comp Physiol a Neuroethol Sens Neural Behav Physiol 188, no. 11–12 (December 2002): 961–80. https://doi.org/10.1007/s00359-002-0358-y.Full Text Open Access Copy Link to Item
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Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. “Bayesian methods for elucidating genetic regulatory networks.” Ieee Intelligent Systems and Their Applications 17, no. 2 (March 1, 2002): 37–43. https://doi.org/10.1109/5254.999218.Full Text
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Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. “Bayesian Methods for Elucidating Genetic Regulatory Networks.” Ieee Intelligent Systems 17, no. 2 (January 1, 2002): 37–43. https://doi.org/10.1109/MIS.2002.999218.Full Text
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Hartemink, Alexander J., David K. Gifford, Tommi S. Jaakkola, and Richard A. Young. “Combining location and expression data for principled discovery of genetic regulatory network models.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2002, 437–49.
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Smith, V Anne, Erich D. Jarvis, and Alexander J. Hartemink. “Evaluating functional network inference using simulations of complex biological systems.” Bioinformatics 18 Suppl 1 (2002): S216–24. https://doi.org/10.1093/bioinformatics/18.suppl_1.s216.Full Text Open Access Copy Link to Item
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Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. “Maximum likelihood estimation of optimal scaling factors for expression array normalization.” Proceedings of Spie the International Society for Optical Engineering 4266 (January 1, 2001): 132–40. https://doi.org/10.1117/12.427981.Full Text
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Hartemink, A. J., D. K. Gifford, T. S. Jaakkola, and R. A. Young. “Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 2001, 422–33. https://doi.org/10.1142/9789814447362_0042.Full Text
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Hartemink, A. J., D. K. Gifford, and J. Khodor. “Automated constraint-based nucleotide sequence selection for DNA computation.” Bio Systems 52, no. 1–3 (October 1999): 227–35. https://doi.org/10.1016/s0303-2647(99)00050-7.Full Text
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Conference Papers
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Mitra, S., J. Zhong, D. M. MacAlpine, and A. J. Hartemink. “Robocop: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12074 LNBI:136–51, 2020. https://doi.org/10.1007/978-3-030-45257-5_9.Full Text
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Zhong, J., T. Wasson, and A. J. Hartemink. “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8394 LNBI:433–47, 2014. https://doi.org/10.1007/978-3-319-05269-4_34.Full Text
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Perez-Pinera, Pablo, David G. Ousterout, Jonathan M. Brunger, Alicia M. Farin, Katherine A. Glass, Farshid Guilak, Gregory E. Crawford, Alexander J. Hartemink, and Charles A. Gersbach. “Synergistic and Tunable Gene Activation by Combinations of Synthetic Transcription Factors.” In Molecular Therapy, 21:S93–S93. NATURE PUBLISHING GROUP, 2013.Link to Item
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Hartemink, A. J., M. Kellis, W. S. Noble, and Z. Weng. “Epigenomics.” In Pacific Symposium on Biocomputing, 2013.
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Hartemink, Alexander J., Manolis Kellis, William Stafford Noble, and Zhiping Weng. “Session Introduction.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, A Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E. Klein, 65–68. World Scientific Publishing, 2013.
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Gordân, Raluca, Alexander J. Hartemink, and Martha L. Bulyk. “Distinguishing Direct versus Indirect Transcription Factor-DNA Interactions.” In Recomb, edited by Bonnie Berger, 6044:574–574. Springer, 2010.
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Bulyk, Martha L., Ernest Fraenkel, Alexander J. Hartemink, and Yael Mandel-Gutfreund. “Session Introduction.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, A Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E. Klein, 438–40. World Scientific, 2008.
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Bulyk, M. L., A. J. Hartemink, E. Fraenkel, and G. Stormo. “DNA-protein interactions: Integrating structure, sequence, and function.” In Pacific Symposium on Biocomputing 2007, Psb 2007, 470–71, 2007.
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Bulyk, Martha L., Alexander J. Hartemink, Ernest Fraenkel, and Gary D. Stormo. “Session Introduction.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, A Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E. Klein, 470–71. World Scientific, 2007.
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Hartemink, Alexander J., and Eran Segal. “Session Introduction: Joint Learning from Multiple Types of Genomic Data.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, Tiffany A. Jung, Teri E. Klein, A Keith Dunker, and Lawrence Hunter. World Scientific, 2005.
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Krishnapuram, B., D. Williams, Y. Xue, A. Hartemink, L. Carin, and M. A. T. Figueiredo. “On semi-supervised classification.” In Advances in Neural Information Processing Systems, 2005.
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Hartemink, Alexander J., and Eran Segal. “Session Introduction.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, A Keith Dunker, Lawrence Hunter, Tiffany A. Jung, and Teri E. Klein, 262–63. World Scientific, 2004.
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Karp, Peter D., Eric P. Neumann, Alexander J. Hartemink, and Pedro Romero. “Genome, Pathway, and Interaction Bioinformatics - Session Introduction.” In Pacific Symposium on Biocomputing, edited by Russ B. Altman, A Keith Dunker, Lawrence Hunter, and Teri E. Klein, 101–3, 2003.
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Hartemink, Alexander J., Tarjei S. Mikkelsen, and David K. Gifford. “Simulating biological reactions: A modular approach.” In Dna Based Computers, edited by Erik Winfree and David K. Gifford, 54:111–22. DIMACS/AMS, 1999.
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Schechter, S., T. Parnell, and A. Hartemink. “Anonymous authentication of membership in dynamic groups.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1648:184–95, 1999. https://doi.org/10.1007/3-540-48390-x_14.Full Text
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Hartemink, Alexander J., and David K. Gifford. “Thermodynamic simulation of deoxyoligonucleotide hybridization for DNA computation.” In Dna Based Computers, edited by Harvey Rubin and David Harlan Wood, 48:25–38. DIMACS/AMS, 1997.
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Datasets
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Mitra, Sneha, David MacAlpine, Jianling Zhong, and Alexander Hartemink. “Data from: RoboCOP: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy,” January 24, 2020. https://doi.org/10.7924/r4hx1b43s.Data Access
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- Teaching & Mentoring
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Recent Courses
- COMPSCI 260: Introduction to Computational Genomics 2021
- COMPSCI 393: Research Independent Study 2021
- COMPSCI 260: Introduction to Computational Genomics 2020
- COMPSCI 391: Independent Study 2020
- COMPSCI 393: Research Independent Study 2020
- COMPSCI 394: Research Independent Study 2020
- ETHICS 89S: Special Topic: First-Year Seminar in Ethics 2020
- COMPSCI 260: Introduction to Computational Genomics 2019
- COMPSCI 393: Research Independent Study 2019
- COMPSCI 394: Research Independent Study 2019
- ETHICS 89S: Special Topic: First-Year Seminar in Ethics 2019
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