Tag: Woodcock

Ian Woodcock

Synthetic Generation of Genomic Datasets using Synthetic Data Vault

Many wonder what the mysterious world of coding can allow you to do. The first things that come to mind are software UI (User Interface) or UX (User Experience), maybe game development, and many other things out there. But there is one field that may seem to be hidden from the world. All are found in some virtual underground dungeon. No, I am not taking you to the dark web. I am talking about data analysis and machine learning. Python is the best programming language that allows you to manipulate Excel datasets. From containing personal information of customers to numbers of statistics of a store and their items. We can use those kinds of datasets and use one to program it to run through an algorithm to give us simply a score. In this, we will be dealing with DNA genomic datatsets and we will put it through an algorithm that creates synthetic genomic data. The score will specifically focus on the broadness to unuiqueness of the type of genomic data in the original dataset and the new dataset.

SFTE 499, Senior Capstone

Ernest Bonat

Richardson 100

10 – 10:30 AM

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Ian Woodcock

Using Machine Learning Autoencoders Neural Networks for Dimensional Reduction of Genomic Datasets

“Machine learning is a well-known field in today’s society when it comes to technology. The first thing that probably comes to your mind is AI, which is a fair assumption, and I do have to admit, yes, machine learning and AI are related in some ways. But this is not involved with robotics similar to what you see in movies or science fairs, or robotics classes. It’s done more so in programming and is ever more present in the world of coding and software engineering. If you had a noisy image, a machine could make it less noisy on the image! If you had a file that was quite big in data size and you wanted to compress it down, a machine could do that! But what if I told you, you could condense down large dataset files, and the files took up so much of your storage on a small thumb drive, and it took forever for a machine to learn and output results? Well, now you can! This is how we use a neural network called an autoencoder and use the dimensionality reduction method to condense our data in Genomics!”

SFTE 445 – Introduction to Machine Learning and AI

Dr. Ernest Bonat

4pm – L204