Book Image

Machine Learning in Biotechnology and Life Sciences

By : Saleh Alkhalifa
Book Image

Machine Learning in Biotechnology and Life Sciences

By: Saleh Alkhalifa

Overview of this book

The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You’ll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share your thoughts
1
Section 1: Getting Started with Data
Section 1: Getting Started with Data
Free Chapter
2
Chapter 1: Introducing Machine Learning for Biotechnology
Chapter 1: Introducing Machine Learning for Biotechnology
Understanding the biotechnology field
Combining biotechnology and machine learning
Exploring machine learning software
Summary
3
Chapter 2: Introducing Python and the Command Line
Chapter 2: Introducing Python and the Command Line
Technical requirements
Introducing the command line
Discovering the Python language
Tutorial – getting started in Python
Tutorial – working with Rdkit and BioPython
Summary
4
Chapter 3: Getting Started with SQL and Relational Databases
Chapter 3: Getting Started with SQL and Relational Databases
Technical requirements
Exploring relational databases
Tutorial – getting started with MySQL
Summary
5
Chapter 4: Visualizing Data with Python
Chapter 4: Visualizing Data with Python
Technical requirements
Exploring the six steps of data visualization
Commonly used visualization libraries
Tutorial – visualizing data in Python
Summary
6
Section 2: Developing and Training Models
Section 2: Developing and Training Models
7
Chapter 5: Understanding Machine Learning
Chapter 5: Understanding Machine Learning
Technical requirements
Understanding ML
Overfitting and underfitting
Developing an ML model
Summary
8
Chapter 6: Unsupervised Machine Learning
Chapter 6: Unsupervised Machine Learning
Introduction to UL
Understanding clustering algorithms
Understanding DR
Summary
9
Chapter 7: Supervised Machine Learning
Chapter 7: Supervised Machine Learning
Understanding supervised learning
Measuring success in supervised machine learning
Understanding classification in supervised machine learning
Understanding regression in supervised machine learning
Summary
10
Chapter 8: Understanding Deep Learning
Chapter 8: Understanding Deep Learning
Understanding the field of deep learning
Selecting an activation function
Measuring progress with loss
Tutorial – protein sequence classification via LSTMs using Keras and MLflow
Tutorial – anomaly detection in manufacturing using AWS Lookout for Vision
Summary
11
Chapter 9: Natural Language Processing
Chapter 9: Natural Language Processing
Introduction to NLP
Getting started with NLP using NLTK and SciPy
Working with structured data
Tutorial – clustering and topic modeling
Working with unstructured data
Tutorial – developing a scientific data search engine using transformers
Summary
12
Chapter 10: Exploring Time Series Analysis
Chapter 10: Exploring Time Series Analysis
Understanding time series data
Exploring the components of a time series dataset
Tutorial – forecasting demand using Prophet and LSTM
Summary
13
Section 3: Deploying Models to Users
Section 3: Deploying Models to Users
14
Chapter 11: Deploying Models with Flask Applications
Chapter 11: Deploying Models with Flask Applications
Understanding API frameworks
Working with Flask and Visual Studio Code
Using Flask as an API and web application
Tutorial – Deploying a pretrained model using Flask
Summary
15
Chapter 12: Deploying Applications to the Cloud
Chapter 12: Deploying Applications to the Cloud
Exploring current cloud computing platforms
Understanding containers and images
Tutorial – deploying a container to AWS (Lightsail)
Tutorial – deploying an application to GCP (App Engine)
Tutorial – deploying an application's code to GitHub
Summary
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16
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Using Flask as an API and web application

In Chapter 9, Natural Language Processing, we explored the use of the transformers library for the purposes of running text similarity search engines. By using this technology, we could have explored other models and implementations, such as sentiment analysis, text classification, and many more. One particular type of model that has gained a great deal of traction when it comes to NLP is the summarization model.

We can think of summarization models as tasks designed to reduce several paragraphs of text down to a few sentences, thereby allowing users to reduce the amount of time required to read. Luckily for us, we can implement an out-of-the-box summarization model using the transformers library and install that in our app.py file. Not only will we need to cater to human users (by using a UI), but we will also need to cater to web applications (APIs) that may be interested in using our model. In order to accommodate these two cases, we...

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