ArticlePloS one2020
Modified linear regression predicts drug-target interactions accurately.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network.Journal of cellular and molecular medicine · 2024Article
- Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design).Methods in molecular biology (Clifton, N.J.) · 2024Article
- DGDTA: dynamic graph attention network for predicting drug-target binding affinity.BMC bioinformatics · 2023Article
- A review on compound-protein interaction prediction methods: Data, format, representation and model.Computational and structural biotechnology journal · 2021Review
- An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.BioMed research international · 2021Article
- Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion.Contrast media & molecular imaging · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
State-of-the-art approaches for the prediction of drug-target interactions (DTI) are based on various techniques, such as matrix factorisation, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we propose the framework of Asymmetric Loss Models (ALM) which is more consistent with the underlying chemical reality compared with conventional regression techniques. Furthermore, we propose to use an asymmetric loss model with BLM to predict drug-target interactions accurately. We evaluate our approach on publicly available real-world drug-target interaction datasets. The results show that our approach outperforms state-of-the-art DTI techniques, including recent versions of BLM.
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