Automatic Classification of Z-ring Formation Stages at the Single Cell Level in Escherichia Coli by Machine Learning
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol 2: Bioimaging |
| Toimittajat | M Silveira, A Fred, H Gamboa, M Vaz |
| Kustantaja | SCITEPRESS |
| Sivut | 72-76 |
| Sivumäärä | 5 |
| ISBN (elektroninen) | 978-989-758-215-8 |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - Kesto: 1 tammikuuta 1900 → … |
Conference
| Conference | INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
In E. coli, Z-ring formation precedes the assembly of the membrane that partitions a cell into two daughter cells. Initially, as FtsZ proteins are expressed, they preferentially locate at the poles. After, they form a ring at midcell, in between the nucleoids, 'marking' where a constriction will form. Finally, the ring becomes a circle, where the septum separating the daughter cells forms. Being the temporal-spatial organization of FtsZ noisy, differing between cells in timing and location, its study requires observing many cells by time-lapse microscopy. To assist, image and signal processing methods are needed to extract information unbiasedly from many cells. Also, one needs automatic identification of the ring formation stage in individual cells. Here we used three classification methods to identify the stage of ring formation from microscopy images: Decision Tree (DT), Support Vector Machine (SVM), and Regularized Multinomial Logistic regression (RMLR). We find that RMLR performs better (higher 10-fold cross-validated accuracy, ACC). Our study will assist future studies at the single cell level of the spatio-temporal dynamics of cell division in E. coli.