Integrated in silico approaches for the prediction of Ames test mutagenicity.

about

IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data

P2860

Q27902312-32F31D89-EF8A-4FB2-98AD-69823477C8F8

P2860

Integrated in silico approaches for the prediction of Ames test mutagenicity.

Item

http://www.wikidata.org/entity/Q51330280

description

2012 nî lūn-bûn

@nan

2012年の論文

@ja

2012年学术文章

@wuu

2012年学术文章

@zh

2012年学术文章

@zh-cn

2012年学术文章

@zh-hans

2012年学术文章

@zh-my

2012年学术文章

@zh-sg

2012年學術文章

@yue

2012年學術文章

@zh-hant

name

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@en

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@nl

type

Item

label

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@en

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@nl

prefLabel

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@en

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@nl

P1476

Integrated in silico approaches for the prediction of Ames test mutagenicity.

@en

P2093

Andrew Scott

http://www.w3.org/2001/XMLSchema#string

Andrew White

http://www.w3.org/2001/XMLSchema#string

Claire Moore

http://www.w3.org/2001/XMLSchema#string

Jin Li

http://www.w3.org/2001/XMLSchema#string

Paul Carmichael

http://www.w3.org/2001/XMLSchema#string

Sandeep Modi

http://www.w3.org/2001/XMLSchema#string

Sophie Malcomber

http://www.w3.org/2001/XMLSchema#string

P2860

An improved bacterial test system for the detection and classification of mutagens and carcinogens

Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit

Comparison of the predicted and observed secondary structure of T4 phage lysozyme

Extended-connectivity fingerprints

Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers.

Cheminformatics analysis and learning in a data pipelining environment.

Derivation and validation of toxicophores for mutagenicity prediction.

Benchmark data set for in silico prediction of Ames mutagenicity.

Interpretation of nonlinear QSAR models applied to Ames mutagenicity data.

P2888

s10822-012-9595-5

P304

1017-1033

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1007/S10822-012-9595-5

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P577

2012-08-24T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

22918548

http://www.w3.org/2001/XMLSchema#string

Integrated in silico approaches for the prediction of Ames test mutagenicity.

about

P2860

P2860

Integrated in silico approaches for the prediction of Ames test mutagenicity.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P577

P698

P356

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P577

P698