Christopher Oehmen

  • Program Years: 1999-2003
  • Academic Institution: University of Memphis/University of Tennessee, HSC
  • Field of Study: Biomedical Engineering
  • Academic Advisor: Semahat Demir
  • Practicum(s):
    Lawrence Livermore National Laboratory (2000)
  • Degree(s):
    Ph.D. Biomedical Engineering, University of Memphis/University of Tennessee Health Science Center, 2003
    M.S. Biomedical Engineering, University of Memphis/UT Health Science Center, 1999
    B.A. Physics and Mathematics, St. Louis University, 1995

Current Status

  • Status: Senior Research Scientist, Computational Biology and Bioinformatics Group, PNNL
  • Research Area: High Performance Computing in Computational Biology
  • Personal URL:
  • Contact Information:
  • Comments

    High performance computing has many possible areas of application in life sciences. Currently, I am interested in

    1) identifying peptides from high-throughput tandem MS data,

    2) high performance searching methodologies for basic sequence alignment of genomic and proteomic data,

    3) developing models for cell signalling pathways,

    4) high performance integrative modeling of physiological systems from genomic data to tissues and beyond and

    5) fault-tolerant high performance programming methodologies for scientific computing requiring thousands of processors or more.


    Refereed Journal Publications:

    1. Webb-Robertson BM, KG Ratuiste and CS Oehmen. (2010) Physicochemical property distributions for accurate and rapid pairwise protein homology detection. BMC Bioinformatics.11(1):145.

    2. Webb-Robertson BM, CS Oehmen and AR Shah (2008) A Feature Vector Integration Approach for a Generalized Support Vector Machine Pairwise Homology Algorithm. Computational Biology and Chemistry, 32(6): 458-61.

    3. Webb-Robertson BM, WR Cannon WR, CS Oehmen, AR Shah, V Gurumoorthi, MS Lipton, and KM Waters. (2008) A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. Bioinformatics 24(13), 1503-9.

    4. Shah AR, CS Oehmen and BM Webb-Robertson, (2008) SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics 24(6):783-790

    5. Webb-Robertson BM, ES Peterson, M Singhal, KR Klicker, CS Oehmen, JN Adkins and SL Havre (2007) PQuad – a visual analysis platform for proteomics data exploration of microbial organisms. Bioinformatics; 23(13): 1705-1707.

    6. Shah AR, CS Oehmen and BM Webb-Robertson (2007) Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms. Computational Biology and Chemistry; 31(2): 138-142.

    7. Oehmen CS and J Nieplocha (2006) ScalaBLAST: A scalable implementation of BLAST for high-performance data-intensive bioinformatics analysis, IEEE Transactions on Parallel and Distributed Systems, 17(8): 740-9.

    8. Oehmen CS, T Straatsma, GA Anderson, G Orr, BM Webb-Robertson, RC Taylor, RW Mooney, DJ Baxter, DR Jones and DA Dixon (2006) New challenges facing integration biological sciences in the post-genomic era. Journal of Biological Systems, 14(2): 275-293.

    9. Webb-Robertson BM, CS Oehmen and MM Matzke (2005) SVM-BALSA: remote homology detection based on Bayesian sequence alignment. Computational Biology and Chemistry, 29(6): 440-443.

    10. Cannon WR, KH Jarman, BM Webb-Robertson, DJ Baxter, CS Oehmen, KD Jarman, A Heredia-Langner, KJ Auberry and GA Anderson (2005) A comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data. Journal of Proteome Research, 4(5): 1687-1698.

    11. Oehmen CS, W Giles, and S Demir (2002) Mathematical model of the rapidly activating delayed rectifier potassium current, IKr, in rabbit sinoatrial node. Journal of Cardiovascular Electrophysiology, 13, 1131-1140.

    Peer-reviewed and invited conference proceedings:

    1. Oehmen CS, S Dowson and ES Peterson (2010). An Organic Model for Detecting Cyber-Events. The 6th Annual Cyber IEEE Security and Information Intelligence Research Workshop (accepted).

    2. Webb-Robertson BM, MM Matzke and CS Oehmen (2008) Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification. The Seventh International Conference on Machine Learning and Applications (ICMLA '08), December 11-13, 2008, San Diego, CA, 457-462.

    3. Oehmen CS and WR Cannon. (2008) Bringing high performance computing to the biologist's workbench: approaches, applications and challenges. J. Phys.: Conf Ser 125 012052.

    4. Curtis DS, ES Peterson and CS Oehmen (2008). A Secure Web Application Providing Public Access to High-Performance Data Intensive Scientific Resources. In 4th Annual International Conference on Web Information Systems and Technologies.

    5. Shah A, VM Markowitz and CS Oehmen (2007) High-throughput computation of pairwise sequence similarities for multiple genome comparisons using ScalaBLAST, Proceedings of IEEE-NIH Life Science Systems and Applications (LISSA 2007).

    6. Webb-Robertson BM, WR Cannon and CS Oehmen (2007) Support vector machine classification of probability models and peptide features for improved peptide identification from shotgun proteomics. The Sixth International Conference on Machine Learning and Applications (ICMLA '07), December 13-15, 2007, Cincinnati Ohio, 500-505.

    7. Garzon M and CS Oehmen (2001) Biomolecular computation in virtual test tubes. Proceedings of the 7th International Meeting on DNA Based computers: Lecture Notes in Computer Science, 117-128.

    8. Garzon M, and CS Oehmen (2001) Distributed virtual test tubes. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2001, Publisher Morgan-Kaufmann, 997-1004.

    Book Chapters and non Peer-Reviewed Journals:

    1. Gorton I, CS Oehmen and JE McDermott (2008) It takes glue to tango. Scientific Computing HPC, November 2008, 16-24.

    2. Oehmen CS and BM Webb-Robertson (2008) Evaluating the Computational Requirements of using SVM software to train Data-Intensive Problems. In Machine Learning Research Progress. H. Peters and M. Vogel, eds. Nova Science Publishers, New York, 2008.

    3. Cannon WR, BM Webb-Robertson, M Singhal, LA McCue, JE McDermott, RC Taylor, KM Waters and CS Oehmen (2007) An Integrative Computational Framework for Hypotheses-Driven Systems Biology Research in Proteomics and Genomics. In Computational and Systems Biology: Methods and Applications