Characterization of drug effects on cell cultures from phase-contrast microscopy images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15110%2F22%3A73615819" target="_blank" >RIV/61989592:15110/22:73615819 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/22:00360157

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0010482522008794?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0010482522008794?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2022.106171" target="_blank" >10.1016/j.compbiomed.2022.106171</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Characterization of drug effects on cell cultures from phase-contrast microscopy images

Popis výsledku v původním jazyce

In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs.

Název v anglickém jazyce

Characterization of drug effects on cell cultures from phase-contrast microscopy images

Popis výsledku anglicky

In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTERS IN BIOLOGY AND MEDICINE

ISSN

0010-4825

e-ISSN

1879-0534

Svazek periodika

151

Číslo periodika v rámci svazku

December 2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

106171

Kód UT WoS článku

000885971100003

EID výsledku v databázi Scopus

2-s2.0-85140305228