Associate Professor, Chair
11:00 to 12:00 Monday, Wednesday, and Friday, or by appointment
CSCI 451 Computational Biology (also listed at graduate level as CSCI 558 — Introduction to Bioinformatics)
Designed for attendance by both computer scientists and biologists. The course will explore the importance of interdisciplinary partnerships between these two fields. Students will learn to use various existing computational tools for investigating genomic and other biological data. This will include tools for performing sequence alignments and searches, building phylogenetic trees, predicting RNA secondary structure, and predicting protein tertiary structure. The underlying algorithmic approaches taken by these tools will be discussed, and in some cases, actually implemented by the class participants. The course will examine the data repositories where genomic and other biological data are stored. There will be some light programming required using Perl as the language of choice. It is assumed that the class participants have no experience programming in Perl and will learn this skill as part of the course.
CSCI 460 Operating Systems
Prereq: CSCI 232 data structures, 205 programming languages, CSCI 361 architecture, or consent of instr. Operating system design principles. Processes, threads, synchronization, deadlock, memory management, file management and file systems, protection, and security. Comparison of commonly used existing operating systems. Writing programs that make use of operating system services.
CSCI 205 Programming Languages
Concepts and principles of programming languages with an emphasis on C, C++, and object-oriented programming. Syntax and semantics of object-oriented languages. Principles and implementation of late binding, memory allocation and de-allocation, type-checking, scope, polymorphism, inheritance.
CSCI 447/557 Introduction to Machine Learning
Prereq., CSCI 232 Data Structures. Introduction to the framework of learning from examples, various learning algorithms such as neural networks, and generic learning principles such as inductive bias, Occam's Razor, and data mining.
CSCI 448/548 Pattern Recognition
Prereq., Upper division status (Junior or Senior or Graduate Student) or consent of instr. Introduction to the framework of unsupervised learning techniques such as clustering (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation. Emphasis will be on the underlying algorithms and their implementation.
CSCI 466 Networks
Wright State University Computer Science & Engineering Ph.D. 2008
Field of Study
Identification of Ribonucleac Acid Functional Structure
Use of data mining techniques on sparse metagenomic data
In collaboration with partners in the Division of Biological Sciences, one of the graduate students studying with me will begin investigating the relationship between geochemical, environmental nutrient composition, and physical conditions with the functional diversity of the localized organismal population. The investigation will involve the identification of metabolic capabilities common to the inhabitants of each of a diverse set of geochemically distinct geothermal environments. This will be accomplished through genomic analysis aimed at metabolic pathway detection and characterization. The analysis will be performed from a systems biology perspective and will be made challenging by the fact that the metagenomic data is incomplete. The data are available as a result of the Yellowstone Metagenome Project, and they represent between 35-50 megabases of sequence data per site. This data, while extensive, does not represent full coverage of all genomes represented in the data; therefore, the analysis must be predictive (given the incomplete nature of the data) and must include some measure of statistical assurance in the findings. The general approach will be to identify, and be able to predict, functional diversity by building a library of genes indicative of each lifestyle, and to develop tools that will be able to recognize the presence of these functional lifestyle-predictive biomarkers. Since we will be dealing with incomplete data, this will require the development of inferential tools where the presence of certain genes can be inferred by the presence of others. The approach will be to mine existing complete genome data for associative rules that can be used make such inferences, and make them in a statistically sound way.
Kotamarti, Rao M., Hahsler, Michael, Raiford, Douglas W., & Dunham, Margaret H. 2010 (May 25). Sequence transformation to a complex signature form for consistent phylogeny using extensible Markov model. Accepted for publication In: Proceedings for 2010 IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB) May 2-5.
Heizer Jr, Esley M., Raiford, Douglas W., Raymer, Michael L., & Krane, Dan E. 2010 (June 1517). Perceived cost of auxotrophic amino acids in two bacterial species. Pages 119-122 of: Proceedings for the 4th annual Ohio Collaborative Conference on Bioinformatics (OCCBIO 2010). Case Western Reserve University, Cleveland, OH, USA.
Kotamarti, Rao M., Raiford, Douglas W., Raymer, Michael L., & Dunham, Margaret H. 2009. A data mining approach to predicting phylum for microbial organisms using genome-wide sequence data. In: Proceedings of the 9th IEEE international conference on bioinformatics and bioengineering (BIBE 2009). Taichung, Taiwan: IEEE Computer Society.
Raiford, D. W., Krane, D. E., Doom, T. E., and Raymer, M. L. (2007). A multi-objective genetic algorithm that employs a hybrid approach for isolating codon usage bias indicative of translational efficiency. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2007), volume I, pages 278–285, Boston, Massachusetts (Conference Center at Harvard Medical School). Awarded Honorary Mention for the Best Paper Award (acceptance rate <12%)
Raiford, D. W., Krane, D. E., Doom, T. E., and Raymer, M. L. (2006). Isolation and visualization of codon usage biases. In Proceedings of the Sixth IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2006), pages 179-182, Washington D.C. (acceptance rate 28%)
Raiford, D. W., Doom, T. E., Krane, D. E., & Raymer, M. L. 2006 (June 26-28). An investigation of codon usage bias including visualization and quantification in organisms exhibiting multiple biases. In: Proceedings for the Ohio Collaborative Conference on Bioinformatics (OCCBIO).
Anderson, P. E., Raiford, D. W., Sweeney, D. J., Doom, T. E., and Raymer, M. L. (2005). Stochastic model of protease-ligand reactions. In Proceedings of the 5th IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2005), pages 306–310, Minneapolis, Minnesota (acceptance rate 29%)
Deole, R., Challacombe, J., Raiford, D. W., & Hoff, W. D. (2013). An extremely halophilic proteobacterium combines a highly acidic proteome with a low cytoplasmic potassium content. J Biol Chem, 288(1), 581-588.
Raiford, Douglas W., Heizer Jr., Esley M., Millerz, Robert V., Doom, Travis E., Raymer, Michael L., & Krane, Dan E. . (2012). Metabolic and Translational Efficiency in Microbial Organisms. Journal of Molecular Evolution 74(3), 206-216.
Raiford, D. W., Doom, T. E., Krane, D. E., and Raymer, M. E. (2011). A genetic optimization approach for isolating translational efficiency bias. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(2), 342-352.
Raiford, Douglas W., Krane, Dan E., Doom, Travis E., Raymer, Michael L. (2010) "Automated Isolation of Translational Efficiency Bias that Resists the Confounding Effect of GC(AT)-Content," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 7(2), 238-250
Kotamarti, R. M., Hahsler, M., Raiford, D., McGee, M., & Dunham, M. H. (2010). Analyzing taxonomic classification using extensible Markov models. Bioinformatics, 26(18), 2235-2241.
Raiford, D. W., Heizer Jr., E. M., Miller, R. V., Akashi, H., Raymer, M. L., and Krane, D. E. (2008). Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae? J. Mol. Evol., 67(6)(Dec), 621-30..
Heizer Jr., E., Raiford, D. W., Raymer, M., Doom, T., Miller, R., & Krane, Dan. 2006. Amino acid cost and codon usage biases in six prokaryotic genomes: A whole-genome analysis. Molecular Biology and Evolution, 23(9), 1670–1680.
Department of Computer Science
University of Montana | Social Sciences Bldg. Room 401 | Missoula, MT 59812
Department Chair: Yolanda Reimer | (406) 243-4618 | email@example.com
Office Contact: Robyn Berg | (406) 243-2883 | firstname.lastname@example.org