Dataset Information

Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

ABSTRACT: Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a process increasing cell migration and metastasis. Marker-based approaches are limited by inconsistences among patients, types of cancer, and partial EMT states. Alternatively, we analyzed cancer cell migration behavior using computer vision. Using a microfluidic single-cell migration chip and high-content imaging, we extracted morphological features and recorded migratory direction and speed of breast cancer cells. By applying a Random Decision Forest (RDF) and an Artificial Neural Network (ANN), we achieved over 99% accuracy for cell movement direction prediction and 91% for speed prediction. Unprecedentedly, we identified highly motile cells and non-motile cells based on microscope images and a machine learning model, and pinpointed and validated morphological features determining cell migration, including not only known features related to cell polarization but also novel ones that can drive future mechanistic studies. Predicting cell movement by computer vision and machine learning establishes a ground-breaking approach to analyze cell migration and metastasis.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC6329292 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Zhang Zhixiong Z Chen Lili L Humphries Brock B Brien Riley R Wicha Max S MS Luker Kathryn E KE Luker Gary D GD Chen Yu-Chih YC Yoon Euisik E

Integrative biology : quantitative biosciences from nano to macro 20181201 12

Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a process increasing cell migration and metastasis. Marker-based approaches are limited by inconsistences among patients, types of cancer, and partial EMT states. Alternatively, we analyzed cancer cell migration behavior using computer vision. Using a microf ...[more]

PMID: 30420987

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Project description:Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples. Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined. Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model. Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Dataset Information

Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Publications

Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

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