ArticleJournal of cheminformatics2012
Fast rule-based bioactivity prediction using associative classification mining.
Article in Journal of cheminformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed.
- Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs.Journal of cheminformatics · 2022Article
- Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care.Frontiers in medicine · 2021Article
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium.Metallomics : integrated biometal science · 2020Article
- Predicted Biological Activity of Purchasable Chemical Space.Journal of chemical information and modeling · 2018Article
- Article
- Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity.Frontiers in molecular biosciences · 2015Article
- An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data.Analytica chimica acta · 2014Article
- Discovering associations in biomedical datasets by link-based associative classifier (LAC).PloS one · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM), which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR), classification based on multiple association rules (CMAR) and classification based on association rules (CBA) are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB), mutagenicity and hERG (the human Ether-a-go-go-Related Gene) blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM) methods, and produce highly interpretable models.
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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.