Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Target Interaction and...
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http://datasciencelab.ugent.be/ Ghent University – iMinds, ELIS Department/Data Science Lab Ghent University Global Campus – Center for Biotech Data Science Mijung Kim, Jasper Zuallaert, Wesley De Neve, and Rik Van de Walle BIG N2N Annual Symposium {mijung.kim, jasper.zuallaert, wesley.deneve, rik.vandewalle}@ugent.be BIOTECH DATA SCIENCE @ GUGC IN KOREA: DEEP LEARNING FOR PREDICTION OF DRUG-TARGET INTERACTION AND DNA ANALYSIS May 19, 2016 | Ghent | Belgium Overview of Deep Machine Learning Prediction of Drug-Target Interaction Input layer Hidden layers Output layer Automatic end-to-end learning of hierarchical features through multi-layered neural networks Training Data: CheMBL Dataset by EMBL-EBI Model using Deep Learning (TensorFlow) New Drug Potential Target DNA Analysis using Natural Language Processing Techniques Word2Vec on DNA sequences → Represents every -gram by a vector e.g., ACG = [O.359, …, -O.129] Vectors are calculated based on surrounding -grams e.g., ... TTA CGA ACG TGG CAT ... Convolutional Neural Networks ACG GCT CTA TAA AAG AGA GAC ACC CCT CTA … … c i-4 c i-3 c i-2 c i-1 c i c i+1 c i+2 … Long Short-Term Memory Networks ACG GCT CTA TAA AAG AGA GAC ACC CCT CTA … … One-hot representation A C C A T A … 1 O O 1 O 1 O 1 1 O O O O O O O O O O O O O 1 O ACC CCA CAT ATA O.241 O.124 -O.549 O.421 -O.853 O.513 O.185 -O.129 … … … … -O.252 -O.884 O.112 O.466 Training Prediction Prediction
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Transcript of Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Target Interaction and...
http://datasciencelab.ugent.be/Ghent University – iMinds, ELIS Department/Data Science Lab
Ghent University Global Campus – Center for Biotech Data Science
Mijung Kim, Jasper Zuallaert, Wesley De Neve, and Rik Van de WalleBIG N2N Annual Symposium
{mijung.kim, jasper.zuallaert, wesley.deneve, rik.vandewalle}@ugent.be
BIOTECH DATA SCIENCE @ GUGC IN KOREA: DEEP LEARNING FORPREDICTION OF DRUG-TARGET INTERACTION AND DNA ANALYSIS
May 19, 2016 | Ghent | Belgium
Overview of Deep Machine Learning
Prediction of Drug-Target Interaction
Input layer Hidden layers Output layer
Automatic end-to-end learning of hierarchical features through multi-layered neural networks
Training Data: CheMBL Dataset by EMBL-EBI
Model usingDeep Learning
(TensorFlow)
NewDrug
Potential Target
DNA Analysis using Natural Language Processing Techniques
Word2Vec on DNA sequences
→ Represents every 𝑛-gram by a vector
e.g., ACG = [O.359, …, -O.129]
Vectors are calculated based on
surrounding 𝑛-grams
e.g., ... TTA CGA ACG TGG CAT ...
Convolutional Neural Networks
ACG GCT CTA TAA AAG AGA GAC ACC CCT CTA …
… ci-4 ci-3 ci-2 ci-1 ci ci+1 ci+2 …
Long Short-Term Memory Networks
ACG GCT CTA TAA AAG AGA GAC ACC CCT CTA …
…
One-hot representation
A C C A T A …
1 O O 1 O 1
O 1 1 O O O
O O O O O O
O O O O 1 O
ACC CCA CAT ATA
O.241 O.124 -O.549 O.421
-O.853 O.513 O.185 -O.129
… … … …
-O.252 -O.884 O.112 O.466
Training
Prediction
Prediction
