Dose–response prediction for in-vitro drug combination datasets: a probabilistic approach

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 1 online resource.

Language: Englisch

Bibliographic citation: Dose–response prediction for in-vitro drug combination datasets: a probabilistic approach ; volume:24 ; number:1 ; day:21 ; month:4 ; year:2023 ; pages:1-31 ; date:12.2023
BMC bioinformatics ; 24, Heft 1 (21.4.2023), 1-31, 12.2023

Creator: Rønneberg, Leiv
Kirk, Paul D. W.
Zucknick, Manuela

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12859-023-05256-6

URN: urn:nbn:de:101:1-2023090121072091313303

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:56 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Rønneberg, Leiv
Kirk, Paul D. W.
Zucknick, Manuela
SpringerLink (Online service)

Other Objects (12)

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

crestr: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Artikel

Probabilistic unawareness

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Artikel

Probabilistic unawareness

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Artikel

Probabilistic unawareness

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read and agree to the privacy policy for the collection of personal data.*

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Dose–response prediction for in-vitro drug combination datasets: a probabilistic approach

Show object

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Probabilistic unawareness

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Probabilistic unawareness

Estimating parameters for probabilistic linkage of privacy-preserved datasets

Quantitative modeling of dose–response and drug combination based on pathway network

Curve interpolation and shape modeling via probabilistic nodes combination

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

<italic>crestr</italic>: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

The in-vitro effect of gonadotropins’ type and combination on Granulosa cells gene expressions

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Probabilistic unawareness

Related objects

Reset password