Machine learning has enabled scientific breakthroughs in several fields.
Biotechnology is one of the most fascinating, as researchers could perform mindblowing tasks with the new tools.
Here are my favorite problems that machine learning helps to solve!
๐งต ๐๐ฝ
These are the topics we are going to talk about:
1. Predicting protein structure from amino acid sequences. 2. Accelerating high-throughput screening for drug discovery. 3. Mapping out the human cell atlas. 4. Precision medicine.
Let's dive in!
1. Predicting protein structure from amino acid sequences.
Proteins are the workhorses of biology. In our body, myriads of processes are controlled by proteins. They enable life. Yet compared to their importance, we know so little about them!
In the last 24 hours, more than 400 of you decided to follow me. Thank you, I am honored!
As you probably know, I love explaining complex machine learning concepts simply. I have collected some of my past threads for you to make sure you don't miss out on them.
Convolution is not the easiest operation to understand: it involves functions, sums, and two moving parts.
However, there is an illuminating explanation โ with probability theory!
There is a whole new aspect of convolution that you (probably) haven't seen before.
๐งต ๐๐ฝ
In machine learning, convolutions are most often applied for images, but to make our job easier, we shall take a step back and go to one dimension.
There, convolution is defined as below.
Now, let's forget about these formulas for a while, and talk about a simple probability distribution: we toss two 6-sided dices and study the resulting values.
To formalize the problem, let ๐ and ๐ be two random variables, describing the outcome of the first and second toss.