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Pavel Pevzner
and
Phillip Compeau
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.
Read more
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.
Read more
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.
In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data.
In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data.
Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering.
Register for this course and see more details by visiting:
OpenCourser.com/course/hdeb6t/genomic
What's inside
Syllabus
Week 1: Introduction to Clustering Algorithms
Welcome to class!
At the beginning of the class, we will see how algorithms for clustering a set of data points will help us determine how yeast became such good wine-makers. At the bottom of this email is the Bioinformatics Cartoon for this chapter, courtesy of Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. How did the monkey lose a wine-drinking contest to a tiny mammal? Why have Pavel and Phillip become cavemen? And will flipping a coin help them escape their eternal boredom until they can return to the present? Start learning to find out!
Read more
Syllabus
Week 1: Introduction to Clustering Algorithms
Welcome to class!
At the beginning of the class, we will see how algorithms for clustering a set of data points will help us determine how yeast became such good wine-makers. At the bottom of this email is the Bioinformatics Cartoon for this chapter, courtesy of Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. How did the monkey lose a wine-drinking contest to a tiny mammal? Why have Pavel and Phillip become cavemen? And will flipping a coin help them escape their eternal boredom until they can return to the present? Start learning to find out!
Read more
Week 2: Advanced Clustering Techniques
Welcome to week 2 of class!
This week, we will see how we can move from a "hard" assignment of points to clusters toward a "soft" assignment that allows the boundaries of the clusters to blend. We will also see how to adapt the Lloyd algorithm that we encountered in the first week in order to produce an algorithm for soft clustering. We will also see another clustering algorithm called "hierarchical clustering" that groups objects into larger and larger clusters.
Week 3: Introductory Algorithms in Population Genetics
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores various clustering and principal components analysis techniques, which are established in data science
Demonstrates applications of clustering techniques in both gene expression analysis and human migration studies
Covers fundamental algorithms in population genetics
Provides foundational knowledge for further exploration in these scientific domains
Save this course
Save Genomic Data Science and Clustering (Bioinformatics V) to your list so you can find it easily later:
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Reviews summary
Genomic data science and bioinformatics
Learners say this course covers genomic data science and bioinformatics. Students largely agree that the lectures are engaging, find the assignments helpful, and appreciate the practical exercises. Many who mentioned difficulty with the course also noted the instructor's clear explanations and stated that they learned challenging material well. Students who had a positive experience recommend this course.
Concepts linked to real-world applications
"Good course to exercise you data science skills"
"clustering chapters and the practical exercises with the yeast dataset"
"Highly recommend the course and the specializations to all learners who are serious about learning algorithms"
Clear explanations of challenging concepts
"explanation to the content was very well given"
"They show an algorithm but don't show an example problem using that algorithm very often."
"I felt like there was a textbook I was missing, but I read every slide and watched every video"
Course can be challenging
"Too hard, need more instructor guidance please."
"Very tough course."
"I also found difficulties in solving quiz 3."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Genomic Data Science and Clustering (Bioinformatics V) with these
activities:
Mentoring Younger Students in Clustering Concepts
Show steps
Reinforce your knowledge by sharing it with others and fostering their understanding.
Browse courses on
Clustering Algorithms
Show steps
-
Volunteer to mentor younger students who are learning about clustering concepts.
-
Provide guidance on understanding the algorithms, their applications, and the interpretation of results.
-
Answer questions and help them overcome any challenges they face.
-
Receive feedback from your mentees to enhance your own understanding.
Organize and Review Course Materials
Show steps
Stay organized and optimize your learning by compiling and reviewing course materials regularly.
Show steps
-
Create a system for organizing course notes, assignments, and readings
-
Review course materials regularly to reinforce your understanding
-
Identify any areas where you need additional support or clarification
Review Basic Clustering Algorithms
Show steps
Refresh your understanding of the foundational clustering algorithms to prepare for the course.
Browse courses on
K-Means Clustering
Show steps
-
Review the K-Means algorithm
-
Review the Gaussian Mixture Models algorithm
-
Review the Hierarchical Clustering algorithm
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review Basic Statistics
Show steps
Review the fundamentals of statistics to strengthen your understanding of the course material.
Browse courses on
Statistics
Show steps
-
Revisit textbooks or online resources on basic statistical concepts.
-
Solve practice problems to test your understanding.
Read: Bioinformatics Algorithms
Show steps
This book provides an in-depth exploration of bioinformatics algorithms, including those used for clustering.
View
BIOINFORMATICS ALGORITHMS
on Amazon
Show steps
-
Read the chapters on clustering algorithms
-
Work through the practice exercises in the book
Study Group for Algorithm Implementation
Show steps
Enhance your understanding and implementation skills through collaborative learning.
Browse courses on
Clustering Algorithms
Show steps
-
Form a study group with classmates.
-
Assign different clustering algorithms to each member to implement.
-
Discuss the implementation details and troubleshoot any challenges together.
-
Share and review each other's code to provide feedback and improve understanding.
Clustering Practice Problems
Show steps
Work through practice problems to develop your skills in applying clustering algorithms.
Browse courses on
Clustering Algorithms
Show steps
-
Find online resources or textbooks with clustering practice problems.
-
Solve the problems step-by-step, checking your answers against provided solutions.
-
Identify areas where you need more practice and focus on those topics.
Solve Clustering Algorithm Practice Problems
Show steps
Practice solving clustering algorithm problems to reinforce your understanding and improve your skills.
Show steps
-
Find online resources with clustering algorithm practice problems
-
Solve a variety of problems to cover different aspects of clustering
-
Review your solutions and identify areas for improvement
Mentor a Peer in Clustering Algorithms
Show steps
Share your knowledge and help others understand clustering algorithms.
Show steps
-
Identify a peer who needs assistance with clustering algorithms
-
Provide guidance and support on clustering concepts and techniques
-
Create exercises or practice problems for the mentee to reinforce their understanding
Advanced Techniques in Principal Component Analysis
Show steps
Enhance your understanding of principal component analysis and its applications.
Browse courses on
Principal Component Analysis
Show steps
-
Locate online tutorials or courses on advanced principal component analysis techniques.
-
Follow the tutorials, taking notes and practicing the concepts.
-
Apply the techniques to sample datasets to reinforce your learning.
Create a Visualization of Clustering Results
Show steps
Create a visualization to demonstrate your understanding of clustering algorithms and their results.
Show steps
-
Choose a clustering algorithm and dataset
-
Apply the algorithm to the dataset and analyze the results
-
Create a visualization that effectively conveys the clustering results
Blog Post on Clustering Applications
Show steps
Demonstrate your understanding of clustering algorithms by writing a blog post on their practical applications.
Browse courses on
Clustering Algorithms
Show steps
-
Research and select specific industries or domains where clustering algorithms are used.
-
Provide real-world examples and case studies to illustrate the benefits and outcomes of clustering.
-
Discuss the challenges and limitations of clustering algorithms and how to address them.
Attend a Workshop on Data Clustering
Show steps
Attend a workshop to learn about advanced clustering techniques and applications.
Show steps
-
Research and find a relevant workshop
-
Register and attend the workshop
-
Actively participate in the workshop and engage with the instructors
Clustering Project Using Bioinformatics Software
Show steps
Apply your knowledge to a real-world problem by using bioinformatics software to perform a clustering analysis.
Browse courses on
Bioinformatics
Show steps
-
Select a publicly available gene expression dataset related to a specific biological question.
-
Use appropriate bioinformatics software to preprocess the data, apply clustering algorithms, and interpret the results.
-
Write a report summarizing your findings and discussing the biological significance of the clusters identified.
Contribute to an Open-Source Clustering Library
Show steps
Contribute to an open-source clustering library to gain hands-on experience with clustering algorithms.
Show steps
-
Identify an open-source clustering library
-
Find a suitable issue or feature to work on
-
Implement the solution and submit a pull request
Mentoring Younger Students in Clustering Concepts
Show steps
Reinforce your knowledge by sharing it with others and fostering their understanding.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Volunteer to mentor younger students who are learning about clustering concepts.
- Provide guidance on understanding the algorithms, their applications, and the interpretation of results.
- Answer questions and help them overcome any challenges they face.
- Receive feedback from your mentees to enhance your own understanding.
Organize and Review Course Materials
Show steps
Stay organized and optimize your learning by compiling and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Create a system for organizing course notes, assignments, and readings
- Review course materials regularly to reinforce your understanding
- Identify any areas where you need additional support or clarification
Review Basic Clustering Algorithms
Show steps
Refresh your understanding of the foundational clustering algorithms to prepare for the course.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Review the K-Means algorithm
- Review the Gaussian Mixture Models algorithm
- Review the Hierarchical Clustering algorithm
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review Basic Statistics
Show steps
Review the fundamentals of statistics to strengthen your understanding of the course material.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Revisit textbooks or online resources on basic statistical concepts.
- Solve practice problems to test your understanding.
Read: Bioinformatics Algorithms
Show steps
This book provides an in-depth exploration of bioinformatics algorithms, including those used for clustering.
View
BIOINFORMATICS ALGORITHMS
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on clustering algorithms
- Work through the practice exercises in the book
Study Group for Algorithm Implementation
Show steps
Enhance your understanding and implementation skills through collaborative learning.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Form a study group with classmates.
- Assign different clustering algorithms to each member to implement.
- Discuss the implementation details and troubleshoot any challenges together.
- Share and review each other's code to provide feedback and improve understanding.
Clustering Practice Problems
Show steps
Work through practice problems to develop your skills in applying clustering algorithms.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find online resources or textbooks with clustering practice problems.
- Solve the problems step-by-step, checking your answers against provided solutions.
- Identify areas where you need more practice and focus on those topics.
Solve Clustering Algorithm Practice Problems
Show steps
Practice solving clustering algorithm problems to reinforce your understanding and improve your skills.
Show steps
Show steps
Show steps
- Find online resources with clustering algorithm practice problems
- Solve a variety of problems to cover different aspects of clustering
- Review your solutions and identify areas for improvement
Mentor a Peer in Clustering Algorithms
Show steps
Share your knowledge and help others understand clustering algorithms.
Show steps
Show steps
Show steps
- Identify a peer who needs assistance with clustering algorithms
- Provide guidance and support on clustering concepts and techniques
- Create exercises or practice problems for the mentee to reinforce their understanding
Advanced Techniques in Principal Component Analysis
Show steps
Enhance your understanding of principal component analysis and its applications.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Locate online tutorials or courses on advanced principal component analysis techniques.
- Follow the tutorials, taking notes and practicing the concepts.
- Apply the techniques to sample datasets to reinforce your learning.
Create a Visualization of Clustering Results
Show steps
Create a visualization to demonstrate your understanding of clustering algorithms and their results.
Show steps
Show steps
Show steps
- Choose a clustering algorithm and dataset
- Apply the algorithm to the dataset and analyze the results
- Create a visualization that effectively conveys the clustering results
Blog Post on Clustering Applications
Show steps
Demonstrate your understanding of clustering algorithms by writing a blog post on their practical applications.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Research and select specific industries or domains where clustering algorithms are used.
- Provide real-world examples and case studies to illustrate the benefits and outcomes of clustering.
- Discuss the challenges and limitations of clustering algorithms and how to address them.
Attend a Workshop on Data Clustering
Show steps
Attend a workshop to learn about advanced clustering techniques and applications.
Show steps
Show steps
Show steps
- Research and find a relevant workshop
- Register and attend the workshop
- Actively participate in the workshop and engage with the instructors
Clustering Project Using Bioinformatics Software
Show steps
Apply your knowledge to a real-world problem by using bioinformatics software to perform a clustering analysis.
Browse courses on
Bioinformatics
Show steps
Show steps
Show steps
- Select a publicly available gene expression dataset related to a specific biological question.
- Use appropriate bioinformatics software to preprocess the data, apply clustering algorithms, and interpret the results.
- Write a report summarizing your findings and discussing the biological significance of the clusters identified.
Contribute to an Open-Source Clustering Library
Show steps
Contribute to an open-source clustering library to gain hands-on experience with clustering algorithms.
Show steps
Show steps
Show steps
- Identify an open-source clustering library
- Find a suitable issue or feature to work on
- Implement the solution and submit a pull request
Career center
Learners who complete Genomic Data Science and Clustering (Bioinformatics V) will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
A Biostatistician applies statistical principles to the design of studies collecting biological data, as well as to the analysis and interpretation of such data. The Genomic Data Science and Clustering course provides foundational knowledge in algorithms for clustering data points into distinct clusters. This knowledge directly supports the analysis and interpretation of biological data, making this course highly relevant to the role of a Biostatistician. By taking this course, you will gain a strong foundation in the methodologies used by Biostatisticians to extract meaningful insights from complex biological datasets.
Population Geneticist
Population Geneticist
A Population Geneticist studies the genetic variation within and between populations. The Genomic Data Science and Clustering course provides foundational knowledge in algorithms for clustering data points into distinct clusters. This knowledge is directly applicable to the analysis of genetic variation, making this course highly relevant to the role of a Population Geneticist. By taking this course, you will gain a strong foundation in the methodologies used by Population Geneticists to study genetic diversity and evolution.
Computational Biologist
Computational Biologist
A Computational Biologist uses computational tools and techniques to analyze and interpret biological data. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are widely used in computational biology, making this course essential for anyone seeking to enter this field. By taking this course, you will gain foundational skills in data analysis and interpretation, which are critical for success in computational biology.
Geneticist
Geneticist
A Geneticist studies genes, their inheritance, and their role in health and disease. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are widely used in genetics to analyze and interpret genetic data. By taking this course, you will gain a strong foundation in data analysis and interpretation, which are essential for success in genetics.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for data scientists working with large and complex datasets. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are vital for success in data science.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for quantitative analysts working with large and complex datasets. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are vital for success as a quantitative analyst.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for machine learning engineers working with large and complex datasets. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are critical for success as a machine learning engineer.
Data Architect
Data Architect
A Data Architect designs and manages the architecture of data systems. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for data architects working on large and complex data systems. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are critical for success as a data architect.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for statisticians working with large and complex datasets. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are critical for success as a statistician.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses advanced analytical techniques to solve complex problems in various industries, including healthcare, transportation, and finance. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are commonly used by operations research analysts to analyze and interpret data. By taking this course, you will gain a strong foundation in data analysis and interpretation, which are essential for success as an operations research analyst.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for software engineers working on data-intensive applications. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are valuable for success as a software engineer.
Systems Analyst
Systems Analyst
A Systems Analyst analyzes and designs systems to meet the needs of an organization. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for systems analysts working on data-intensive systems. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are valuable for success as a systems analyst.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. The Genomic Data Science and Clustering course provides an introduction to algorithms for clustering data points into distinct clusters and principal components analysis, a technique for preprocessing multidimensional data before clustering. These techniques are essential for web developers working on data-driven websites and applications. By taking this course, you will gain a solid foundation in data analysis and interpretation, which are valuable for success as a web developer.
Research Scientist
Research Scientist
A Research Scientist conducts scientific research in a specific field. The Genomic Data Science and Clustering course provides foundational knowledge in algorithms for clustering data points into distinct clusters. This knowledge is applicable to various scientific disciplines. By taking this course, you will gain a strong foundation in data analysis and interpretation, which are essential for success as a Research Scientist.
Teacher
Teacher
A Teacher educates students in a specific subject area. The Genomic Data Science and Clustering course provides foundational knowledge in algorithms for clustering data points into distinct clusters. This knowledge can be applied to teaching students about data analysis and interpretation. By taking this course, you will gain a strong foundation in data analysis and interpretation, which can support your teaching in this area.
For more career information including salaries, visit:
OpenCourser.com/course/hdeb6t/genomic
Reading list
We've selected 20 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Genomic Data Science and Clustering (Bioinformatics V).
This is the companion textbook to the course. It offers a bit more depth than what is offered in the course.
This comprehensive textbook covers a wide range of bioinformatics topics, including sequence analysis, protein structure prediction, and gene expression profiling. It provides a thorough reference for both students and professionals.
This textbook delves into the algorithmic foundations of bioinformatics, covering advanced topics such as sequence alignment, phylogenetic tree construction, and gene finding. It provides a deeper understanding of the algorithms discussed in the course.
Provides a comprehensive overview of Bayesian data analysis, covering topics such as Bayesian inference, model selection, and Bayesian computation. It would be a valuable resource for students and researchers in the field.
Save
Provides a comprehensive overview of causal inference in statistics, covering topics such as causal models, causal effects, and causal inference methods. It would be a valuable resource for students and researchers in the field.
Provides a comprehensive overview of molecular biology, covering topics such as DNA structure and function, gene expression, and cell signaling. It would be a valuable resource for students and researchers in the field.
Provides a comprehensive overview of genetic analysis, covering topics such as Mendelian genetics, molecular genetics, and population genetics. It would be a valuable resource for students and researchers in the field.
Provides a practical guide to data analysis using regression and multilevel/hierarchical models, covering topics such as model building, model selection, and model checking. It would be a valuable resource for students and researchers in the field.
This textbook provides a comprehensive overview of machine learning algorithms and techniques, including supervised and unsupervised learning, dimensionality reduction, and model evaluation. It provides a broader context for understanding the machine learning methods used in bioinformatics.
This textbook provides a rigorous mathematical treatment of probabilistic models used in sequence analysis. It covers topics such as hidden Markov models, multiple sequence alignment, and phylogenetic tree reconstruction.
Provides a practical guide to reproducible research in bioinformatics, covering topics such as data management, statistical analysis, and visualization. It would be a valuable resource for students and researchers in the field.
Save
Provides a comprehensive overview of population genetics, which field that uses mathematical and statistical tools to study the genetic variation within populations.
Introduces machine learning concepts and techniques used in bioinformatics, such as supervised and unsupervised learning, feature selection, and model evaluation. It provides a broader perspective on data analysis methods beyond clustering.
Covers statistical methods commonly used in bioinformatics, such as hypothesis testing, regression analysis, and Bayesian inference. It provides a solid foundation for understanding the statistical principles underlying bioinformatics research.
Classic in the field of statistical learning. It would be a great resource to have.
Save
This textbook provides a comprehensive introduction to bioinformatics, covering core concepts and techniques used in the field. It provides a strong foundation for understanding the algorithms and techniques covered in the course.
Save
Provides a non-technical overview of evolution, covering topics such as natural selection, genetic drift, and the evolution of humans. It would be a valuable resource for students and researchers in the field.
This textbook provides a concise overview of essential bioinformatics concepts, including sequence analysis, gene expression analysis, and genome annotation. It provides a good starting point for those new to the field.
Provides a practical guide to bioinformatics programming using Python, with a focus on data analysis and visualization.
This great reference book for machine learning. It highly cited academic book.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hdeb6t/genomic
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Level
Intermediate
Via
Coursera
Institution
University of California San Diego
Instructors
Pavel Pevzner
Phillip Compeau
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Bioinformatics. It's comprised of seven courses:
- Molecular Evolution (Bioinformatics IV)
- Finding Mutations in DNA and Proteins (Bioinformatics VI)
- Finding Hidden Messages in DNA (Bioinformatics I)
- Comparing Genes, Proteins, and Genomes (Bioinformatics III)
- Genomic Data Science and Clustering (Bioinformatics V) (viewing)
- Genome Sequencing (Bioinformatics II)
- Bioinformatics Capstone: Big Data in Biology
Good to know
Explores various clustering and principal components analysis techniques, which are established in data science
Demonstrates applications of clustering techniques in both gene expression analysis and human migration studies
Covers fundamental algorithms in population genetics
Provides foundational knowledge for further exploration in these scientific domains
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