LearnQuest
Capstone Project: Advanced AI for Drug Discovery
LearnQuest

Capstone Project: Advanced AI for Drug Discovery

This course is part of AI for Scientific Research Specialization

Taught in English

Some content may not be translated

Rajvir Dua
Neelesh Tiruviluamala

Instructors: Rajvir Dua

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyzing genome sequences to find similarities and identify target subsequences using predctive models.

Details to know

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the AI for Scientific Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

In this module, we'll start to get familiar with our dataset by performing some basic EDA and comparing genome sequences. By analyzing the mutations of the COVID-19 virus, we'll be able to identify some common properties of the genome that our drug should look to target.

What's included

4 videos1 programming assignment1 discussion prompt1 ungraded lab

In this module, we'll continue to work with out genome sequence data - using PCA to identify groups and delicate the most important features. After reducing the number of dimensions in the dataset, we'll be able to use K-means to form clusters and visualize the different areas in 2-D space.

What's included

2 videos1 reading1 programming assignment1 ungraded lab

In this module, we'll cluster the genome sequences using the K-means algorithm. We'll optimize the number of clusters by comparing silhouette scores across a wide variety of inputs to identify the greatest drop-off. Finally, we'll set ourselves up to using prediction pipelines to predict bit scores and drug therapies in the last module.

What's included

2 videos1 programming assignment1 discussion prompt1 ungraded lab

In this module, we'll test a variety of regressors to see which one performs best in predicting bit scores for each genome sequence. Then, we'll use our chosen model to find the genome equines that are most closely related and trace out a possible subsequence to target with a combative drug.

What's included

2 videos1 programming assignment1 ungraded lab

Instructors

Instructor ratings
1.8 (7 ratings)
Rajvir Dua
LearnQuest
8 Courses13,446 learners

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LearnQuest

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