http://multimedialab.elis.ugent.be Ghent University – iMinds, ELIS Department/Multimedia Lab Ghent University Global Campus – Center for Biotech Data Science Mijung Kim, Wesley De Neve, Peter Lambert FEA Research Symposium 2015 {mijung.kim, wesley.deneve, peter.lambert}@ugent.be EXPLORING DEEP LEARNING FOR AUTOMATIC RIGHT WHALE RECOGNITION AND NOVEL DRUG DESIGN 9th December | Ghent | Belgium Automatic Right Whale Recognition Identifying right whales! Hmm… How about using deep learning? If you have many photos, then I can train a deep convolutional neural network for identifying the individual right whales. However, the low number of photos and the many classes make this challenging! Still, let us give it a try… Novel Drug Design Hello Mijung! How is your research going? Good! I am currently exploring DEEP LEARNING, a novel technique in the field of machine learning. I recently had a talk with Jasper, a marine biologist. He is doing research on North Atlantic Right Whales, an endangered species with fewer than 500 animals left. So, to monitor the health and the status of the remaining population, Jasper and his colleagues take photographs of right whales during aerial surveys, and then manually identify the animals photographed. However, manually identifying right whales is a time- consuming process, requiring special training. Mijung, would it somehow be possible for you to automate the identification process? Look at this! By giving photos as an input to a convolutional neural network, it will automatically extract those visual features that make it possible to identify each individual whale. Yesterday, I met Jozef. He tried to develop a new rejuvenation drug, but his chemical experiment ended up in failure because he could not predict the side effect of the chemical synthesis. If he would have known about my research, he would have been able to predict the explosion at least. In particular, I am currently investigating the use of multi-task neural networks to design novel drugs. These networks do not only consider the main task but also related tasks. So, by making use of these in- silico networks during drug design, I can predict side effects like the toxicity of combinations of chemical molecules, without having to make use of in-vivo or in-vitro experiments. Task 1 Task 2 Hidden Layer Hello Wesley! Interesting! Mijung, can you apply deep learning to other biotech problems as well? Yes! I can give you a real-world example. Whale 1 Whale 2 Whale 3 Whale 4 convolution + nonlinearity max pooling convolution + pooling layers vector fully connected layers multi-class classification Whale 1 Whale 2 Whale 3 Whale 4 multi-task neural network
Exploring Deep Machine Learning for Automatic Right Whale Recognition and Novel Drug Design
Feb 16, 2017
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
http://multimedialab.elis.ugent.beGhent University – iMinds, ELIS Department/Multimedia Lab
Ghent University Global Campus – Center for Biotech Data Science
Mijung Kim, Wesley De Neve, Peter LambertFEA Research Symposium 2015
{mijung.kim, wesley.deneve, peter.lambert}@ugent.be
EXPLORING DEEP LEARNING FOR AUTOMATICRIGHT WHALE RECOGNITION AND NOVEL DRUG DESIGN
9th December | Ghent | Belgium
Automatic Right Whale Recognition
Identifying right whales! Hmm…
How about using deep learning?
If you have many photos, then I can train a deep convolutional neural network for identifying the individual right whales.
However, the low number of photos and the many classes make this challenging!
Still, let us give it a try…
Novel Drug Design
HelloMijung!
How is your research going?
Good! I am currently exploring DEEP LEARNING, a novel technique in
the field of machine learning.
I recently had a talk with Jasper, a marine biologist. He is doing research on North Atlantic Right Whales, an endangered species with fewer than 500 animals left. So, to monitor the health and the status of the remaining population, Jasper and his colleagues take photographs of right whales during aerial surveys, and then manually identify the animals photographed. However, manually identifying right whales is a time-consuming process, requiring special training. Mijung, would it somehow be possible for you to automate the identification process?
Look at this! By giving photos as an input to a convolutional neural network, it will automatically extractthose visual features that make it possible to identify each individual whale.
Yesterday, I met Jozef. He tried to develop a new rejuvenation drug, but his chemical experiment ended up in failure because he could not predict the side effect of the chemical synthesis. If he would have known about my research, he would have been able to predict the explosion at least.
In particular, I am currently investigating the use of multi-task neural networks to design novel drugs. These networks do not only consider the main task but also related tasks. So, by making use of these in-silico networks during drug design, I can predict side effects like the toxicity of combinations of chemical molecules, without having to make use of in-vivo or in-vitro experiments.
Task 1
Task 2
HiddenLayer
Hello Wesley!
Interesting! Mijung, can you
apply deep learning to other biotech problems
as well?
Yes! I can give you a real-world
example.
Whale 1
Whale 2
Whale 3
Whale 4
convolution + nonlinearity
max pooling
convolution + pooling layers
vector
Fully connected layersfully connected layers multi-class classification
Whale 1
Whale 2
Whale 3
Whale 4
multi-task neural network
Related Documents
