This thesis aims to fill this gap in two ways.
Foundations of Bioinformatics I. Introduction to script programming and basic biomolecular sequence analysis. Topics covered include sequence alignment, dynamic programming algorithms, hidden Markov models, and their implementation with a scripting language.
Foundations of Bioinformatics II. Topics in bioinformatics such as phylogeny reconstruction, genome-wide association study analysis, structure and sequence analysis, and machine learning and statistical approaches.
Focus of the course is on a hands-on project on a contemporary bioinformatics problem. Data Analysis in Bioinformatics.
Students will learn machine learning methods. They will apply the methods to various problems in bioinformatics using the Python scikit machine learning library. Previous programming experience is required, previous knowledge of Python is a plus.
This course will introduce students to the practice of analyzing large-scale genomic data generated by recent high throughput bio-techniques.
It will cover microarray data and short-read sequencing data. It presents widely used analytical methods and software.
The course includes several case studies on real large-scale genomics datasets. Students will gain practical experience in large-scale data analysis, which is highly desirable by both industry and academia employers.
Data Mining and Management in Bioinformatics. Concepts and principles of data management in bioinformatics. Presents methods for indexing, querying, and mining data obtained from molecular and evolutionary biology. Programming, Data Structures, and Algorithms.
Computer science students cannot use this course for graduate degree credit.
Intensive introduction to computer science principles: Programming assignments are included. Foundations of Computer Science.
Cannot be used for graduate credit towards the M. Introduction to the concepts of iteration, asymptotic performance analysis of algorithms, recursion, recurrence relations, graphs, automata and logic, and also surveys the main data models used in computer science including trees, lists, sets, and relations.Cataloged from PDF version of thesis.
Includes bibliographical references (pages ).
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Improving a plant’s operations by applying lean manufacturing on the material flow and layout design Alfa Laval Anton Kamne Anton Sjöberg Master Thesis . About Olin Business School. Washington University's Olin Business School is a place where students discover and develop their talents.
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