ArticleJournal of cheminformatics2018
Effect of missing data on multitask prediction methods.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Modeling and Interpretability Study of the Structure-Activity Relationship for Multigeneration EGFR Inhibitors.ACS omega · 2025Article
- QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.Journal of cheminformatics · 2024Article
- MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.Journal of chemical information and modeling · 2024Article
- Large-Scale Modeling of Sparse Protein Kinase Activity Data.Journal of chemical information and modeling · 2023Article
- Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach.Acta pharmaceutica Sinica. B · 2023Article
- Review
- Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction.Journal of cheminformatics · 2022Article
- Don't Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models.Molecules (Basel, Switzerland) · 2021Article
- Recent Advances in In Silico Target Fishing.Molecules (Basel, Switzerland) · 2021Review
- Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions.Journal of computer-aided molecular design · 2021Article
- Memory-assisted reinforcement learning for diverse molecular de novo design.Journal of cheminformatics · 2020Article
- An up-to-date overview of computational polypharmacology in modern drug discovery.Expert opinion on drug discovery · 2020Review
- Recommender Systems in Antiviral Drug Discovery.ACS omega · 2020Article
- Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT.Journal of cheminformatics · 2020Article
- A Survey of Multi-task Learning Methods in Chemoinformatics.Molecular informatics · 2019Review
- Article
- Novel applications of Machine Learning in cheminformatics.Journal of cheminformatics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.