Could it be as easy as spotting spam emails?
▪ Is it possible to predict novel therapeutic targets using available
gene – disease association data?
▪ Is Open Targets just a catalogue of gene – disease associations
or can we learn from it what makes a good target?
A positive – unlabelled (PU) semi-
supervised learning approach
▪ Obtain all gene – disease associations and supporting evidence from Open
Targets platform. For all genes, create numeric features by taking the
mean score across all diseases:
▪ Genetic associations (germline)
▪ Somatic mutations
▪ Significant gene expression changes
▪ Disease-relevant phenotype in animal model
▪ Pathway-level evidence
▪ Gather positive labels from Pharmaprojects: only consider targets with
drugs currently on the market, in clinical trials or preclinical studies. A
semi-supervised framework with only positive labels is used: targets
according to PharmaProjects constitute the positive class (P), while the
rest of the proteome is used as the unlabelled class (U), containing both
negatives and yet-to-be-discovered positive.
▪ All positive cases (1421) and an equal number of randomly selected
unlabelled cases (2842 in total) are set apart for training (80%) and
testing (20%). The remainder is kept as a prediction set where predictions
from the final model will be made.
Finding structure in the data
Hierarchical clustering PCA t-SNE
Identifying most important features
Chi-squared test and information gain Decision tree classification criteria
Nested cross-validation and bagging for
tuning and model selection
Bischl et al., 2012
Four classifiers are independently tuned, trained and tested on the training
set using a nested cross-validation strategy (4 inner rounds for parameter
tuning and 4 outer rounds to assess performance):
▪ Random forest
▪ Feed-forward neural network with single hidden layer
▪ Support vector machine with radial kernel
▪ Gradient boosting machine with AdaBoost exponential loss
In PU learning, U contains both positive and negative cases, which results in classifier
instability. Bagging (bootstrap aggregating) can improve the performance of instable
classifiers by randomly resampling P and U with replacement (bootstrap) and then
aggregating the results by majority voting:
▪ Bagging with 100 iterations was applied to the neural network, the support vector
machine and the gradient boosting machine.
▪ Random forests are already a special case of bagging.
Assessing classifier performance
Neural network classifier
achieves 71% accuracy
(0.76 AUC) on test set
Investigating results across the pipeline
Successful and more advanced targets have higher
disease association evidence
Validation of predictions with literature mining
Significant overlap between neural
network predictions and text mining
results (p = 5.05e-172)
Automating drug target discovery
with machine learning
▪ The gene – disease association data from Open Targets contains enough
information to predict whether a protein can make a therapeutic target or
not with decent accuracy.
▪ According to our model, the most informative evidence types are animal
models showing disease-relevant phenotypes, dysregulated gene
expression in disease tissue and genetic associations between gene and
▪ The ability to predict late stage targets with greater accuracy confirms that
clear linkage between target and disease is essential to maximise chances
of success in the clinic.
▪ Lack of prediction on indication;
▪ No tractability considerations.
▪ Philippe Sanseau
▪ Ian Dunham
▪ Gautier Koscielny
▪ Giovanni Dall’Olio
▪ Pankaj Agarwal
▪ Mark Hurle
▪ Steven Barrett
▪ Nicola Richmond
▪ Jin Yao