Curriculum: 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 (3 credits/9 units)
| CMU 03-730 | Advanced Genetics |
Advanced Biochemistry |
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Advanced Cell Biology |
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Molecular Biology |
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Pitt BIOSC 2100 |
Advanced Topics in Cell Biology |
Specialization Electives (3 credits/9 units)
| CMU 03-711 | Computational Molecular Biology and Genomics |
| CMU 03-712 | Computational Methods for Biological Modeling and Simulation |
Statistical Foundations for Bioinformatics Data Mining |
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Practical Analysis of High-Throughput Genomic & Proteomic Data |
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Statistical Methods and Data Mining in Microarray Analysis |
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Human Population Genetics |
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Quantitative Genetics |
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Linkage Analysis |
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Bioinformatics of Gene Regulation |
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Bioinformatics of Cancer Biology & Therapeutics |

