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!CS/ST580: bioinformatics algorithms
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!CS/ST548: bioinformatics algorithms
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Fall 2011
!!!Course description
!!!Course description
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!!!Fall 2011
!!Announcements
Class was moved from engineering E206 to CSB 325
!!Course description
!!Announcements
Class was moved from engineering E206 to CSB 325
!!Course description
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In homework assignments students will implement and use methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
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In homework assignments students will implement and use methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving.
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The course is aimed at graduate students interested in bioinformatics. It is expected that students from computer science, statistics, and the life sciences will benefit from such a course. It will provide a broad overview of the computational and statistical techniques currently used in bioinformatics. Students will obtain in-depth understanding of standard bioinformatics tools and learn to use various sources of sequence data. The hands-on lab component of the course will consist of implementing and using methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
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The course is aimed at graduate students interested in bioinformatics. It is expected that students from computer science, statistics, and the life sciences will benefit from such a course. It will provide a broad overview of the computational and statistical techniques currently used in bioinformatics. Students will obtain in-depth understanding of standard bioinformatics tools and learn to use various sources of sequence data.
In homework assignments students will implement and use methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
In homework assignments students will implement and use methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
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!CS200: bioinformatics algorithms
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!CS/ST580: bioinformatics algorithms
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!CS200: algorithms and data structures
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!CS200: bioinformatics algorithms
Spring 2009
Spring 2009
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The goal of this course is to convey an understanding of abstract data types, common data structures, necessary discrete structures and complexity analysis. The course is taught using the Java language and emphasizes an object oriented approach to data structures. Specific topics in data structures/algorithms include advanced sorting, queues, stacks, hashing, trees, and graph algorithms. Complementary topics from theory include complexity analysis, recurrence relations, trees, and graphs.
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The past decade has seen an explosion of biological data, including the DNA sequence of a number of organisms. Computer science and statistics have an important role in analyzing these data: from assembling the genomic sequence, to predicting the protein-coding regions, their function and the manner in which they are regulated. The course will focus on biological sequence analysis, starting with the biological background, motivating the various computational problems, and methods for their solution. Computational techniques covered in the course include dynamic programming for sequence alignment, hidden Markov models for gene finding, and Gibbs sampling for motif discovery.
The course is aimed at graduate students interested in bioinformatics. It is expected that students from computer science, statistics, and the life sciences will benefit from such a course. It will provide a broad overview of the computational and statistical techniques currently used in bioinformatics. Students will obtain in-depth understanding of standard bioinformatics tools and learn to use various sources of sequence data. The hands-on lab component of the course will consist of implementing and using methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
The course is aimed at graduate students interested in bioinformatics. It is expected that students from computer science, statistics, and the life sciences will benefit from such a course. It will provide a broad overview of the computational and statistical techniques currently used in bioinformatics. Students will obtain in-depth understanding of standard bioinformatics tools and learn to use various sources of sequence data. The hands-on lab component of the course will consist of implementing and using methods for analysis of protein/DNA sequences and other genomic information. The project component of the course will be centered around real biological data analysis problems, giving the student realistic experience in bioinformatics problem solving. When possible, students will be encouraged to work on their projects in interdisciplinary teams.
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!CS200: algorithms and data structures
!!!Course description
The goal of this course is to convey an understanding of abstract data types, common data structures, necessary discrete structures and complexity analysis. The course is taught using the Java language and emphasizes an object oriented approach to data structures. Specific topics in data structures/algorithms include advanced sorting, queues, stacks, hashing, trees, and graph algorithms. Complementary topics from theory include complexity analysis, recurrence relations, trees, and graphs.
!!!Course description
The goal of this course is to convey an understanding of abstract data types, common data structures, necessary discrete structures and complexity analysis. The course is taught using the Java language and emphasizes an object oriented approach to data structures. Specific topics in data structures/algorithms include advanced sorting, queues, stacks, hashing, trees, and graph algorithms. Complementary topics from theory include complexity analysis, recurrence relations, trees, and graphs.