Author

L. Parts

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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Microbiology· 2 Cancer Biology· 1 Cell Biology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 4 Assay:CRISPR screen· 2 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Organism:E. coli· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:machine learning (classical)· 2

Frequent Co-Authors

A. Farewell· 2 shared D. Moradigaravand· 2 shared J. Warringer· 2 shared M. Palm· 2 shared V. Mustonen· 2 shared A. Rinken· 1 shared A. Tsherniak· 1 shared B. Newman· 1 shared

Activity by Year

2019-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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2 shared papersMicrobiology

A. Farewell

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

Optimised whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells

S. Usluer, P. Hallast, L. Crepaldi, Y. Zhou, K. Urgo, C. Dincer, J. Su, G. Noell, K. Alasoo, O. El Garwany, S. Gerety, B. Newman, O. M. Dovey, L. Parts

The paper optimizes CRISPR interference screens in human iPSCs using dCas9-KRAB-MeCP2, identifies ARID1A-dependent growth regulators, and finds proteasome-related genes as synthetic lethal targets.

bioRxiv · Oct 3, 2022 · PreprintCell Biology

Live-cell microscopy or fluorescence anisotropy with budded baculoviruses - which way to go with measuring ligand binding to M4 muscarinic receptors?

M.-J. Tahk, J. Torp, M. A. S. Ali, D. Fishman, L. Parts, L. Grätz, C. Müller, M. Keller, S. Veiksina, T. Laasfeld, A. Rinken

The paper compares fluorescence anisotropy and live-cell microscopy for measuring ligand binding kinetics to M4 muscarinic receptors, finding both methods suitable for affinity and kinetics analysis.

bioRxiv · Dec 22, 2021Pharmacology & Toxicology

Machine-learning prediction of resistance to sub-inhibitory antimicrobial concentrations from Escherichia coli genomes

S. Benkwitz-Bedford, M. Palm, T. Y. Demirtas, V. Mustonen, A. Farewell, J. Warringer, D. Moradigaravand, L. Parts

This paper uses machine learning to predict E. coli growth under sub-inhibitory antimicrobial concentrations from genomic data, finding moderate to strong correlations but highlighting remaining missing heritability.

bioRxiv · Mar 26, 2021 · PreprintMicrobiology

Genomic epidemiology and evolution of Escherichia coli in wild animals

R. Murphy, M. Palm, V. Mustonen, J. Warringer, A. Farewell, D. Moradigaravand, L. Parts

This study analyzes whole genome sequencing data of commensal E. coli from wild animals to explore population structure, host associations, and the presence of virulence and antibiotic resistance genes.

mSphere · Feb 24, 2021Peer-reviewedMicrobiology

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency datasets

J. M. Dempster, C. Pacini, S. Pantel, F. M. Behan, T. Green, J. Krill-Burger, C. M. Beaver, S. T. Younger, V. Zhivich, H. Najgebauer, F. Allen, E. Goncalves, R. Shepherd, J. G. Doench, K. Yusa, , A. Tsherniak

This study compares two large pan-cancer CRISPR-Cas9 screens, finding high concordance in gene dependencies despite differences in protocols and reagents.

bioRxiv · Jun 4, 2019Cancer Biology