A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.
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Computational methods for prediction of in vitro effects of new chemical structuresRecent Advances and Emerging Applications in Text and Data Mining for Biomedical DiscoveryUsing nuclear receptor activity to stratify hepatocarcinogensCharacterization of chemically induced liver injuries using gene co-expression modulesMachine learning algorithms for mode-of-action classification in toxicity assessmentParadigm shift in toxicity testing and modeling.A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression.Improving the human hazard characterization of chemicals: a Tox21 updateProfiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef DatabaseIdentification of protein functions using a machine-learning approach based on sequence-derived properties.Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment.In vitro and modelling approaches to risk assessment from the U.S. Environmental Protection Agency ToxCast programme.Relative impact of incorporating pharmacokinetics on predicting in vivo hazard and mode of action from high-throughput in vitro toxicity assays.In-silico predictive mutagenicity model generation using supervised learning approachesDiscovery of antibiotics-derived polymers for gene delivery using combinatorial synthesis and cheminformatics modeling.Can human experts predict solubility better than computers?Predicting epiglottic collapse in patients with obstructive sleep apnoea.Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBasesIdentifying Biological Pathway Interrupting Toxins Using Multi-Tree Ensembles
P2860
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P2860
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.
description
2008 nî lūn-bûn
@nan
2008 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
name
A comparison of machine learni ...... ulated multi-scale data model.
@ast
A comparison of machine learni ...... ulated multi-scale data model.
@en
type
label
A comparison of machine learni ...... ulated multi-scale data model.
@ast
A comparison of machine learni ...... ulated multi-scale data model.
@en
prefLabel
A comparison of machine learni ...... ulated multi-scale data model.
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A comparison of machine learni ...... ulated multi-scale data model.
@en
P2093
P2860
P356
P1433
P1476
A comparison of machine learni ...... ulated multi-scale data model.
@en
P2093
Fathi Elloumi
R Woodrow Setzer
P2860
P2888
P356
10.1186/1471-2105-9-241
P577
2008-05-19T00:00:00Z
P5875
P6179
1022202697