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Joint CMU-Pitt Ph.D. Program in Computational Biology
Robert F. Murphy and Ivet Bahar, Directors
Computational Genomics Specialization Area
Computational Genomics entails efforts to digest the daunting quantity of genomic and proteomic data now available by systematic development and application of probability and statistics theories, information technologies and data mining techniques. Linguistics methods are viewed as promising tools towards elucidating sequence-structure-function relations, and complementing computational genomics studies. Computational genomics targets understanding gene/protein function, identifying and characterizing cellular regulatory networks and discerning the link between genes and diseases. Discovery and processing of this information is pivotal in the development of novel gene therapy strategies and tools.
Required Life Science Elective
CMU 03-730 Advanced Genetics CMU 03-740 Advanced Biochemistry CMU 03-741 Advanced Cell Biology CMU 03-742 Molecular Biology of Eukaryotes Pitt BIOSC 2100 Advanced Topics in Cell Biology Required Quantitative Elective (one of the following)
Pitt BIOINF 2101
Probabilistic Models for Computer-Based Decision Support
Pitt BIOST 2015 Elements of Statistical Learning Pitt BIOST 2063 Bayesian and Empirical Statistical Methods Pitt BIOST 2064 Bayesian and Empirical Bayes Computational Methods Pitt CS 2550 Principles of Database Design CMU 15-750 Algorithms CMU 15-853 Algorithms in the Real World CMU 10-702 Statistical Machine Learning CMU 10-708 Probabilistic Graphical Models CMU 36-705 Intermediate Statistics
Specialization Electives (two of the following)
Pitt BIOINF 2054 Statistical Foundations for Bioinformatics Data Mining
Pitt BIOINF 2055 Practical Analysis of High-Throughput Genomic & Proteomic Data
Pitt MSMPHL 3315 Bioinformatics of Cancer Biology & Therapeutics
Pitt BIOST 2070 Statistical Methods and Data Mining in Microarray Analysis
Pitt MSCBIO 2020 Bioinformatics of Gene Regulation
CMU 03-711 Computational Molecular Biology and Genomics
