Author

S. E. Lazic

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Pharmacology & Toxicology· 4 Cell Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 5 Approach:Bayesian inference· 4 Research Focus:benchmarking· 1 Research Focus:host-pathogen & immune response· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

E. Semenova· 3 shared A. M. Afzal· 2 shared D. Williams· 2 shared A. Hock· 1 shared B. Zhang· 1 shared C. E. Pollard· 1 shared D. P. Williams· 1 shared G. Kadamur· 1 shared

Activity by Year

2017-2020 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersPharmacology & Toxicology

E. Semenova

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Author profile →

5 papers

Recent·Most cited

Flexible fitting of PROTAC concentration-response curves with changepoint Gaussian Processes

E. Semenova, M. L. Guerriero, B. Zhang, A. Hock, P. Hopcroft, G. Kadamur, A. M. Afzal, S. E. Lazic

This paper introduces a Bayesian changepoint Gaussian Processes model to automatically fit PROTAC concentration-response curves, enabling accurate parameter estimation and compound ranking without manual intervention.

bioRxiv · Nov 13, 2020Cell Biology

Improving drug safety predictions by reducing poor analytical practices

S. E. Lazic, D. Williams

The paper identifies poor analytical practices in drug safety prediction models and proposes solutions to improve their reliability.

bioRxiv · Sep 25, 2020Pharmacology & Toxicology

A Bayesian neural network for toxicity prediction

E. Semenova, D. P. Williams, A. M. Afzal, S. E. Lazic

This paper introduces a Bayesian neural network for toxicity prediction, showing it outperforms traditional models in capturing ordinal data uncertainty.

bioRxiv · Apr 28, 2020Pharmacology & Toxicology

Determining organ weight toxicity with Bayesian causal models: Improving on the analysis of relative organ weights

S. E. Lazic, E. Semenova, D. Williams

The paper proposes using Bayesian causal models to improve the analysis of organ weight toxicity by addressing limitations in traditional methods like relative organ weight and ANCOVA.

bioRxiv · Mar 31, 2020Pharmacology & Toxicology

Predicting drug safety and communicating risk: benefits of a Bayesian approach

S. E. Lazic, N. Edmunds, C. E. Pollard

This paper advocates for Bayesian methods in drug safety prediction, using hERG-mediated QT prolongation as a case study, and provides code for implementation.

bioRxiv · Nov 1, 2017Pharmacology & Toxicology