# Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities

arXiv: https://arxiv.org/abs/2105.01545 · HTML: https://mlsanity.com/paper/2105.01545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 9 of 9)

- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [Hashing as Tie-Aware Learning to Rank](https://mlsanity.com/paper/1705.08562/) (2017)
- [Multi-Label Sampling based on Local Label Imbalance](https://mlsanity.com/paper/2005.03240/) (2020)
- [MBA: Mini-Batch AUC Optimization](https://mlsanity.com/paper/1805.11221/) (2018)
- [Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery](https://mlsanity.com/paper/2012.12325/) (2020)
- [Fast Kronecker product kernel methods via generalized vec trick](https://mlsanity.com/paper/1601.01507/) (2016)
- [Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions](https://mlsanity.com/paper/2007.07029/) (2020)
- [AUC Optimisation and Collaborative Filtering](https://mlsanity.com/paper/1508.06091/) (2015)

## Built on by (most cited first, 2 of 2)

- [Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery](https://mlsanity.com/paper/2201.04437/) (2022)
- [Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization](https://mlsanity.com/paper/2201.09508/) (2022)

## Abstract (arXiv)

In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process. Although it has been shown that fusing heterogeneous drug and target similarities can improve the prediction ability, the existing similarity combination methods ignore the interaction consistency for neighbour entities which is more crucial for the DTI prediction model. Furthermore, area under the precision-recall curve (AUPR) that emphasizes the accuracy of top-ranked pairs and area under the receiver operating characteristic curve (AUC) that heavily punishes the existence of low ranked interacting pairs are two widely used evaluation metrics in DTI prediction. However, the two metrics are seldom considered as losses within existing DTI prediction methods. This paper first proposes two matrix factorization (MF) methods that optimize AUPR and AUC using convex surrogate losses respectively, and then develops an ensemble MF approach takes advantage of the two area under the curve metrics by combining the two single metric based MF models. Both three proposed approaches incorporate a novel local interaction consistency aware similarity interaction method to generate fused drug and target similarities that preserve vital information from the more reliable view. Experimental results over five datasets under different prediction settings show that the proposed methods outperform various competitors in terms of the metric(s) they optimize. In addition, the validation on the top ranked novel predictions confirms the ability of our methods to discover potential new DTIs.