Predicting clinically promising therapeutic hypotheses using tensor factorization

Abstract

BackgroundDetermining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17.ResultsAs a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods.ConclusionIn this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2664-1) contains supplementary material, which is available to authorized users.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs

“…Rosalind out-performs five comparable approaches in identifying those drug targets most likely to be therapeutically linked to a disease. Rosalind is able to make prospective predictions using time-sliced data 15 as well as predict those genes that have a high probability of efficacy in a clinical trial 16 .…”

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

“…To access how trained models can be generalized into groups of drugs that the models have never trained on before, we further make a leave-one-drug-class-out cross-validation ( Yao et al, 2019 ). We first mapped the UMLS concept to the ATC code, then divided the drugs into 15 classes by the ATC code (See Supplementary Table S1 ).…”

Bayesian tensor factorization for predicting clinical outcomes using integrated human genetics evidence

“…Notably, drug targets with human genetics evidence are twice more likely to be approved [ (Nelson et al, 2015), (King et al, 2019)] and the most recent drug approvals from FDA corroborate this strong trend (Ochoa et al, 2022). However, a recent tensor factorization-based approach from (Yao et al, 2019) found that additional information on targets and indications might not necessarily improve the predictive accuracy underscoring the importance of feature selection and evidence data quality. Here we revisit this approach by integrating different lines of human genetics evidence collated from publicly available sources and assess the relative predictive performance of models incorporating different types of human genetics evidence.…”