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Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/wcms.100openalex-snapshot
Computational toxicology: a tool for all industries
inferredcites →https://doi.org/10.1007/pl00011662openalex-snapshot
Making Use of the Most Expressive Jumping Emerging Patterns for Classification
inferredcites →https://doi.org/10.1007/s10115-004-0178-1openalex-snapshot
Mining border descriptions of emerging patterns from dataset pairs
inferredcites →https://doi.org/10.1016/b978-1-55860-307-3.50011-3openalex-snapshot
GALOIS : An order-theoretic approach to conceptual clustering
inferredcites →https://doi.org/10.1016/j.tox.2006.04.032openalex-snapshot
Use of computer-assisted prediction of toxic effects of chemical substances
inferredcites →https://doi.org/10.1016/s0027-5107(00)00064-6openalex-snapshot
The Ames Salmonella/microsome mutagenicity assay
inferredcites →https://doi.org/10.1016/s0169-409x(02)00012-1openalex-snapshot
Computer systems for the prediction of toxicity: an update
inferredcites →https://doi.org/10.1021/ci010244iopenalex-snapshot
Analysis of Large Screening Data Sets via Adaptively Grown Phylogenetic-Like Trees
inferredcites →https://doi.org/10.1021/ci049860fopenalex-snapshot
The Reduced Graph Descriptor in Virtual Screening and Data-Driven Clustering of High-Throughput Screening Data
inferredcites →https://doi.org/10.1021/ci0503715openalex-snapshot
Substructure Mining Using Elaborate Chemical Representation
inferredcites →https://doi.org/10.1021/ci100092xopenalex-snapshot
Introduction of Jumping Fragments in Combination with QSARs for the Assessment of Classification in Ecotoxicology
inferredcites →https://doi.org/10.1021/ci600301topenalex-snapshot
Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection
inferredcites →https://doi.org/10.1021/ci600411vopenalex-snapshot
Integration of Structure−Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity
inferredcites →https://doi.org/10.1021/ci800094aopenalex-snapshot
Accurate and Interpretable Computational Modeling of Chemical Mutagenicity
inferredcites →https://doi.org/10.1021/mp100103eopenalex-snapshot
Predicting Phospholipidosis Using Machine Learning
inferredcites →https://doi.org/10.1023/a:1025361621494openalex-snapshot
In silico prediction of drug toxicity
inferredcites →https://doi.org/10.1038/nature04710openalex-snapshot
hERG potassium channels and cardiac arrhythmia
inferredcites →https://doi.org/10.1145/312129.312191openalex-snapshot
Efficient mining of emerging patterns
inferredcites →https://doi.org/10.2174/157340908783769238openalex-snapshot
Novel Rule-Based Method for Multi-Parametric Multi-Objective Decision Support in Lead Optimization Using KEM
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/minf.201500004openalex-snapshot
Chemoinformatics at the University of Sheffield 2002–2014
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2017.12.002openalex-snapshot
In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115419openalex-snapshot
A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2016.02.022openalex-snapshot
Prediction and characterization of P-glycoprotein substrates potentially bound to different sites by emerging chemical pattern and hierarchical cluster analysis
inferred← citeshttps://doi.org/10.1016/j.tourman.2014.06.015openalex-snapshot
Identifying emerging hotel preferences using Emerging Pattern Mining technique
inferred← citeshttps://doi.org/10.1016/j.ymeth.2020.05.013openalex-snapshot
TOP: A deep mixture representation learning method for boosting molecular toxicity prediction
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.0c00006openalex-snapshot
Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.7b00083openalex-snapshot
Evaluation of Different Methods for Identification of Structural Alerts Using Chemical Ames Mutagenicity Data Set as a Benchmark
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00198openalex-snapshot
Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b01890openalex-snapshot
The Pharmacophore Network: A Computational Method for Exploring Structure–Activity Relationships from a Large Chemical Data Set
inferred← citeshttps://doi.org/10.1021/ci400186nopenalex-snapshot
Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci400597dopenalex-snapshot
Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci500147bopenalex-snapshot
Prediction of Compounds in Different Local Structure–Activity Relationship Environments Using Emerging Chemical Patterns
inferred← citeshttps://doi.org/10.1021/ci5001828openalex-snapshot
Emerging Pattern Mining To Aid Toxicological Knowledge Discovery
inferred← citeshttps://doi.org/10.1021/ci500611vopenalex-snapshot
Discovering Structural Alerts for Mutagenicity Using Stable Emerging Molecular Patterns
inferred← citeshttps://doi.org/10.1039/c4tx00071dopenalex-snapshot
New structural alerts for Ames mutagenicity discovered using emerging pattern mining techniques
inferred← citeshttps://doi.org/10.1039/c5mb00312aopenalex-snapshot
Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs
inferred← citeshttps://doi.org/10.1039/d2dd00136eopenalex-snapshot
Generating structural alerts from toxicology datasets using the local interpretable model-agnostic explanations method
inferred← citeshttps://doi.org/10.1093/bib/bbu042openalex-snapshot
Discriminative pattern mining and its applications in bioinformatics
inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983340openalex-snapshot
TOP: Towards Better Toxicity Prediction by Deep Molecular Representation Learning
inferred← citeshttps://doi.org/10.1186/1758-2946-6-8openalex-snapshot
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
inferred← citeshttps://doi.org/10.2147/dddt.s108118openalex-snapshot
Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants
inferred← citeshttps://doi.org/10.2200/s00897ed1v01y201901dmk016openalex-snapshot
Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
inferred← citeshttps://doi.org/10.5936/csbj.201302013openalex-snapshot
AUTOMATED DETECTION OF STRUCTURAL ALERTS (CHEMICAL FRAGMENTS) IN (ECO)TOXICOLOGY
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