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Dynamic Programming

Applications In Machine Learning and Genomics

Phillip Compeau and Pavel Pevzner

If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

In the first part of the course, part of the Algorithms and Data Structures MicroMasters program, we will see how the dynamic programming paradigm can be used to solve a variety of different questions related to pairwise and multiple string comparison in order to discover evolutionary histories.

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If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

In the first part of the course, part of the Algorithms and Data Structures MicroMasters program, we will see how the dynamic programming paradigm can be used to solve a variety of different questions related to pairwise and multiple string comparison in order to discover evolutionary histories.

In the second part of the course, we will see how a powerful machine learning approach, using a Hidden Markov Model, can dig deeper and find relationships between less obviously related sequences, such as areas of the rapidly mutating HIV genome.

What you'll learn

  • Dynamic programming and how it applies to basic string comparison algorithms
  • Sequence alignment, including how to generalize dynamic programming algorithms to handle different cases
  • Hidden markov models
  • How to find the most likely sequence of events given a collection of outcomes and limited information
  • Machine learning in sequence alignment
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What's inside

Learning objectives

  • Dynamic programming and how it applies to basic string comparison algorithms
  • Sequence alignment, including how to generalize dynamic programming algorithms to handle different cases
  • Hidden markov models
  • How to find the most likely sequence of events given a collection of outcomes and limited information
  • Machine learning in sequence alignment

Syllabus

Week 1: Pairwise Sequence Alignment A review of dynamic programming, and applying it to basic string comparison algorithms.
Week 2: Advanced Sequence Alignment Learn how to generalize your dynamic programming algorithm to handle a number of different cases, including the alignment of multiple strings.
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Week 3: Introduction to Hidden Markov Models Learn what a Hidden Markov model is and how to find the most likely sequence of events given a collection of outcomes and limited information.
Week 4: Machine Learning in Sequence Alignment Formulate sequence alignment using a Hidden Markov model, and then generalize this model in order to obtain even more accurate alignments.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches dynamic programming and how it applies to basic string comparison algorithms, which is standard in bioinformatics
Examines sequence alignment, which is highly relevant to building a foundation for the study of bioinformatics
Taught by Phillip Compeau and Pavel Pevzner, who are recognized for their work in the bioinformatics space
Develops an understanding of hidden Markov models and how to find the most likely sequence of events given a collection of outcomes and limited information
Employs machine learning in sequence alignment, which can be useful for personal growth and development

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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 Dynamic Programming: Applications In Machine Learning and Genomics with these activities:
Review high school level statistics
Some knowledge of basic probability and statistics will reinforce your learning in this course.
Browse courses on Statistics
Show steps
  • Review your notes from a previous statistics course.
  • Use online resources to refresh your memory on basic concepts such as probability, mean, median, and standard deviation.
Follow tutorials on pairwise sequence alignment algorithms
This will help you understand the basics of sequence alignment, which is a fundamental technique used in bioinformatics.
Browse courses on Sequence Alignment
Show steps
  • Find a tutorial on pairwise sequence alignment algorithms, such as the Needleman-Wunsch algorithm.
  • Follow the tutorial step-by-step, making sure to understand the concepts and algorithms being discussed.
Create a diagram of the Hidden Markov Model (HMM)
This will help you visualize and understand the structure and operation of HMMs, which are used for sequence alignment and other tasks in bioinformatics.
Show steps
  • Review the basic concepts of HMMs.
  • Draw a diagram of an HMM, including the states, transitions, and emissions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice sequence alignment using the Needleman-Wunsch algorithm
This will help you develop proficiency in using the Needleman-Wunsch algorithm for sequence alignment, which is a key skill in bioinformatics.
Browse courses on Sequence Alignment
Show steps
  • Find a set of practice problems involving sequence alignment using the Needleman-Wunsch algorithm.
  • Solve the practice problems, making sure to understand the steps involved in the algorithm.
Join a study group to discuss advanced sequence alignment techniques
This will allow you to engage with peers, share knowledge, and learn from others about advanced sequence alignment techniques, including HMM-based approaches.
Browse courses on Sequence Alignment
Show steps
  • Find or create a study group with other students who are also interested in advanced sequence alignment techniques.
  • Meet regularly to discuss topics, share resources, and work on projects together.
Use Hidden Markov Models (HMMs) to analyze real-world biological data
This will provide you with hands-on experience in using HMMs for sequence alignment and other bioinformatics tasks.
Browse courses on Hidden Markov Models
Show steps
  • Find a dataset of biological sequences, such as DNA or protein sequences.
  • Use an HMM library or tool to analyze the sequences and identify patterns or motifs.
Participate in a bioinformatics hackathon or competition
This will challenge you to apply your skills in a competitive setting and learn from others in the field.
Browse courses on Sequence Alignment
Show steps
  • Find a bioinformatics hackathon or competition that aligns with your interests.
  • Form a team or work individually on a project that addresses a specific problem or challenge in bioinformatics.

Career center

Learners who complete Dynamic Programming: Applications In Machine Learning and Genomics will develop knowledge and skills that may be useful to these careers:
Computational Biologist
Computational Biology is an interdisciplinary field that uses computer science to solve biological problems. Computational biologists bridge the gap between biology, computer science, and mathematics. If you are interested in this field, Dynamic Programming: Applications In Machine Learning and Genomics is a great place to start as it will help you to build a solid foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which are widely used in computational biology.
See salaries and explore the career path for Computational Biologist
Bioinformatician
Bioinformatics is a branch of computational science that uses computers and software to solve biological problems. Bioinformaticians work on problems as varied as analyzing the structure of genes, tracking disease outbreaks, and discovering new drugs. If you are interested in a career in Bioinformatics, this course is a good place to start as it teaches the fundamentals of dynamic programming, machine learning, and hidden Markov Models, all of which are essential tools for the modern Bioinformatics scientist.
See salaries and explore the career path for Bioinformatician
Biostatistician
A Biostatistician is a statistician that specializes in the analysis of biological data. They are able to apply statistical methods to real world problems as varied as studying cancer outbreaks or the best way to care for a group of patients. Studying Dynamic Programming: Applications In Machine Learning and Genomics can help you build a foundation for a career as a Biostatistician, as dynamic programming and machine learning are widely used to extract meaning from complex biological data sets. You will gain experience with Hidden Markov Models, which are commonly used in computational biology, and you will learn how to find the most likely sequence of events given a collection of outcomes and limited information, a skill highly valuable to Biostatisticians.
See salaries and explore the career path for Biostatistician
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use dynamic programming and machine learning to develop trading strategies and risk management models. If you are interested in a career as a Quantitative Analyst, this course is a good place to start as it will help you to build a strong foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in financial time series analysis.
See salaries and explore the career path for Quantitative Analyst
Computer Scientist
Computer Scientists research and develop new computer software and hardware. They use dynamic programming and machine learning in a variety of applications, including artificial intelligence, robotics, and computer graphics. If you are interested in a career as a Computer Scientist, this course is a good place to start as it will help you to build a solid foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in speech recognition and natural language processing.
See salaries and explore the career path for Computer Scientist
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. They work closely with Data Scientists and other engineers to ensure that machine learning models are working properly and are meeting the needs of the business. If you are interested in a career as a Machine Learning Engineer, this course is a good place to start as it will help you to build a solid foundation in dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which are widely used in machine learning.
See salaries and explore the career path for Machine Learning Engineer
Systems Analyst
Systems Analysts design and implement computer systems that meet the needs of businesses and organizations. They use dynamic programming and machine learning to model systems and find the best way to allocate resources to maximize efficiency and profitability. If you are interested in a career as a Systems Analyst, this course will help you build a strong foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in supply chain management and logistics.
See salaries and explore the career path for Systems Analyst
Software Engineer
Software Engineers design, develop, and maintain software applications. They use dynamic programming and machine learning in a variety of applications, including computer graphics, data analysis, and robotics. If you are interested in a career as a Software Engineer, this course is a good place to start as it will help you to build a solid foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in speech recognition and natural language processing.
See salaries and explore the career path for Software Engineer
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing large data sets. They then use this data to build models that can be used to predict outcomes or make decisions. If you are interested in a career as a Data Scientist, this course is a good foundation as it will help you to build a strong foundation in dynamic programming and machine learning, two essential tools for analyzing large data sets. You will also gain experience with Hidden Markov Models, which has applications in speech recognition and weather forecasting.
See salaries and explore the career path for Data Scientist
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries, including finance, healthcare, and manufacturing. They use dynamic programming and machine learning to model systems and find the best way to allocate resources to maximize efficiency and profitability. If you are interested in a career as an Operations Research Analyst, this course will help you build a strong foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in supply chain management and logistics.
See salaries and explore the career path for Operations Research Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses and organizations make better decisions. They use dynamic programming and machine learning to model systems and find the best way to allocate resources to maximize efficiency and profitability. If you are interested in a career as a Data Analyst, this course will help you build a strong foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in supply chain management and logistics.
See salaries and explore the career path for Data Analyst
Medical Physicist
Medical Physicists use physics principles to diagnose and treat diseases. They use dynamic programming and machine learning to develop new medical imaging techniques and radiation therapy treatments. If you are interested in a career as a Medical Physicist, this course may be helpful as it will help you to build a foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in medical imaging and diagnosis.
See salaries and explore the career path for Medical Physicist
Biomedical Engineer
Biomedical Engineers design and develop medical devices and equipment. They use dynamic programming and machine learning to model biological systems and develop new treatments for diseases. If you are interested in a career as a Biomedical Engineer, this course may be helpful as it will help you to build a foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in medical imaging and diagnosis.
See salaries and explore the career path for Biomedical Engineer
Research Scientist
Research Scientists conduct research to develop new knowledge and technologies. They use dynamic programming and machine learning in a variety of research areas, including artificial intelligence, robotics, and computer graphics. If you are interested in a career as a Research Scientist, this course may be helpful as it will help you to build a foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in speech recognition and natural language processing.
See salaries and explore the career path for Research Scientist
Geneticist
Geneticists study genes and heredity. They use dynamic programming and machine learning to analyze DNA sequences and identify genetic variations that are associated with disease. If you are interested in a career as a Geneticist, this course may be helpful as it will help you to build a foundation in the fundamentals of dynamic programming and machine learning. You will also gain experience with Hidden Markov Models, which have applications in genome sequencing and analysis.
See salaries and explore the career path for Geneticist

Reading list

We've selected seven 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 Dynamic Programming: Applications In Machine Learning and Genomics.
Cover image
An Introduction to Bioinformatics Algorithms
Save
Provides a comprehensive introduction to bioinformatics algorithms, including those used in sequence alignment.
An Introduction to Bioinformatics Algorithms...
Hardcover
$$
An Introduction to Bioinformatics Algorithms...
Kindle Edition
$$
Pattern Recognition and Machine Learning
Save
Comprehensive introduction to machine learning algorithms, including those used in sequence alignment and bioinformatics.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Probabilistic Graphical Models
Save
Provides a comprehensive introduction to probabilistic graphical models, including those used in bioinformatics applications.
Probabilistic Graphical Models: Principles and...
Hardcover
$$$$
Probabilistic Graphical Models: Principles and...
Kindle Edition
$$$
Machine Learning
Save
Classic reference for machine learning algorithms, including those used in bioinformatics applications.
Machine Learning (McGraw-Hill International...
Paperback
$$$
Cover image
Introduction to Algorithms, fourth edition
Save
Classic reference for algorithms and is often used in computer science courses. It provides detailed explanations and step-by-step examples for a wide range of algorithms, including string comparison algorithms.
Introduction to Algorithms, fourth edition
Hardcover
$$$$
Introduction to Algorithms, fourth edition
Kindle Edition
$$$
Cover image
BIOINFORMATICS: A PRACTICAL GUIDE TO THE ANALYSIS...
Save
Provides a practical introduction to bioinformatics, including a chapter on sequence alignment.
Bioinformatics: A Practical Guide To The Analysis...
Paperback
$$
Cover image
Handbook of Natural Pesticides
Save
Provides a comprehensive introduction to molecular biology, including the basics of DNA and protein sequencing.
Handbook of Natural Pesticides: Volume VI: Insect...
Hardcover
$$$$
Handbook of Natural Pesticides: Insects, Part A,...
Hardcover
$$$$
Handbook of Natural Pesticides: Volume VI: Insect...
Kindle Edition
$$$

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Course image
Effort
8 to 10 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
The University of California, San Diego
Instructors
Phillip Compeau
Pavel Pevzner
Language
English
Transcript
English
This course belongs to a collection called Algorithms and Data Structures. It's comprised of nine courses:
  1. Algorithmic Design and Techniques
  2. Data Structures Fundamentals
  3. Graph Algorithms
  4. NP-Complete Problems
  5. String Processing and Pattern Matching Algorithms
  6. Dynamic Programming: Applications In Machine Learning and Genomics (viewing)
  7. Graph Algorithms in Genome Sequencing
  8. Algorithms and Data Structures Capstone
  9. Introduction to Algorithms
Good to know
Teaches dynamic programming and how it applies to basic string comparison algorithms, which is standard in bioinformatics
Examines sequence alignment, which is highly relevant to building a foundation for the study of bioinformatics
Taught by Phillip Compeau and Pavel Pevzner, who are recognized for their work in the bioinformatics space
Develops an understanding of hidden Markov models and how to find the most likely sequence of events given a collection of outcomes and limited information
Employs machine learning in sequence alignment, which can be useful for personal growth and development
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