Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Khushi, Matloob; Dean, Imraan M.; Teber, Erdahl; Chircop, Megan; Arthur, Jonathan W.; Flores‐Rodriguez, Neftali

doi:10.1186/s12859-017-1966-4

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…For example, RNAi-based screens have used automated imaging to assess genes required in spindle assembly (Goshima et al, 2007;Kwon et al, 2008). Khushi et al (2017) recently developed an automated image analysis software for phenotypic descriptions of mitotic spindles. Finally, utilization of machine learning approaches may offer additional benefits to users with such expertise (Sommer and Gerlich, 2013).…”

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confidence: 99%

A Cell Adhesion-Based Reconstitution Method for Studying Cell Polarity

Johnston

2020

Front. Cell Dev. Biol.

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Cell polarity is an evolutionarily conserved process of asymmetric spatial organization within cells and is essential to tissue structure, signal transduction, cell migration, and cell division. The establishment and maintenance of polarity typically involves extensive protein-protein interactions that can be made further intricate by cell cycle-dependent regulation. These aspects can make interpreting phenotypes within traditional in vivo genetic systems challenging due to pleiotropic effects in loss-of-function experiments. Minimal reconstitution methods offer investigators the advantage of stricter control of otherwise complex systems and allow for more direct assessment of the role of individual components to the process of interest. Here I provide a detailed protocol for a cell adhesion-based method of inducing cell polarity within non-polarized Drosophila S2 cells. This technique is simple, cost effective, moderate throughput, and amenable to RNAi-based loss-of-function studies. The ability to "plug-and-play" genes of interest allows investigators to easily assess the contribution of individual protein domains and post-translational modifications to their function. The system is ideally suited to test not only the requirement of individual components but also their sufficiency, and can provide important insight into the epistatic relationship among multiple components in a protein complex. Although designed for use within Drosophila cells, the general premise and protocol should be easily adapted to mammalian cell culture or other systems that may better suit the interests of potential users.

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confidence: 99%

A Cell Adhesion-Based Reconstitution Method for Studying Cell Polarity

Johnston

2020

Front. Cell Dev. Biol.

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“…Automatic curation of medical images has been in use in various domains of medical research [29,30]. In this research we show that automatic measurement of corneal nerve tortuosity is possible.…”

Section: Discussionmentioning

confidence: 72%

U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures

Mehrgardt

Zandavi

Poon

et al. 2020

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Measurement of corneal nerve tortuosity is associated with dry eye disease, diabetic retinopathy, and a range of other conditions. However, clinicians measure tortuosity on very different grading scales that are inherently subjective. Using in vivo confocal microscopy, 253 images of corneal nerves were captured and manually labelled by two researchers with tortuosity measurements ranging on a scale from 0.1 to 1.0. Tortuosity was estimated computationally by extracting a binarised nerve structure utilising a previously published method. A novel U-Net segmented adjacent angle detection (USAAD) method was developed by training a U-Net with a series of back feeding processed images and nerve structure vectorizations. Angles between all vectors and segments were measured and used for training and predicting tortuosity measured by human labelling. Despite the disagreement among clinicians on tortuosity labelling measures, the optimised grading measurement was significantly correlated with our USAAD angle measurements. We identified the nerve interval lengths that optimised the correlation of tortuosity estimates with human grading. We also show the merit of our proposed method with respect to other baseline methods that provide a single estimate of tortuosity. The real benefit of USAAD in future will be to provide comprehensive structural information about variations in nerve orientation for potential use as a clinical measure of the presence of disease and its progression.

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“…Advanced machine learning methods coupled with feature engineering and addition of more features improve prediction models [28,29]. A clear one, as mentioned earlier, is using the ratio of free:total PSA [3], but another option is measurement of androgen and estrogen steroids, since the role of these hormones are acknowledged in other cancer-types [30].…”

Section: Discussionmentioning

confidence: 99%

Predicting High-Risk Prostate Cancer Using Machine Learning Methods

Barlow

Mao

Khushi

2019

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Prostate cancer can be low- or high-risk to the patient’s health. Current screening on the basis of prostate-specific antigen (PSA) levels has a tendency towards both false positives and false negatives, both of which have negative consequences. We obtained a dataset of 35,875 patients from the screening arm of the National Cancer Institute’s Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We segmented the data into instances without prostate cancer, instances with low-risk prostate cancer, and instances with high-risk prostate cancer. We developed a pipeline to deal with imbalanced data and proposed algorithms to perform preprocessing on such datasets. We evaluated the accuracy of various machine learning algorithms in predicting high-risk prostate cancer. An accuracy of 91.5% can be achieved by the proposed pipeline, using standard scaling, SVMSMOTE sampling method, and AdaBoost for machine learning. We then evaluated the contribution of rate of change of PSA, age, BMI, and filtration by race to this model’s accuracy. We identified that including the rate of change of PSA and age in our model increased the area under the curve (AUC) of the model by 6.8%, whereas BMI and race had a minimal effect.

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Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Cited by 11 publications

References 18 publications

A Cell Adhesion-Based Reconstitution Method for Studying Cell Polarity

A Cell Adhesion-Based Reconstitution Method for Studying Cell Polarity

U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures

Predicting High-Risk Prostate Cancer Using Machine Learning Methods

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