scholarly journals PIKE-R2P: Protein–protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction

2021 ◽  
Vol 22 (S6) ◽  
Author(s):  
Xinnan Dai ◽  
Fan Xu ◽  
Shike Wang ◽  
Piyushkumar A. Mundra ◽  
Jie Zheng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Single Cell ◽  
Prior Knowledge ◽  
Protein Interactions ◽  
Interaction Network ◽  
Superior Performance ◽  
Single Cell Level ◽  
Cell Level ◽  
Protein Prediction

Abstract Background Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning models. However, existing machine learning methods have not considered relationship among the proteins sufficiently. Results We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein–protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements. Conclusion The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.

scholarly journals Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

2020 ◽  
Vol 21 (9) ◽  
pp. 3166
Author(s):  
Kiminori Yanagisawa ◽  
Masayasu Toratani ◽  
Ayumu Asai ◽  
Masamitsu Konno ◽  
Hirohiko Niioka ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Single Cell ◽  
Tumor Cells ◽  
Antitumor Drugs ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cancer ◽  
Deep Learning Model

It is known that single or isolated tumor cells enter cancer patients’ circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.

scholarly journals Gene expression-based identification of antigen-responsive CD8+ T cells on a single-cell level

2019 ◽  
Author(s):  
Yannick F. Fuchs ◽  
Virag Sharma ◽  
Anne Eugster ◽  
Gloria Kraus ◽  
Robert Morgenstern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
T Cells ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Large Gene ◽  
Gene Sets ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractCD8+ T cells are important effectors of adaptive immunity against pathogens, tumors and self antigens. Here, we asked how human cognate antigen-responsive CD8+ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labelled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8+ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8+ T cells from unselected populations. Gene response profiles of CD8+ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.One-sentence summaryIdentification of genes, gene sets, and development of a machine learning-based classifier that distinguishes antigen-responsive CD8+ T cells on a single-cell level.

scholarly journals Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

2017 ◽  
Author(s):  
Timea Toth ◽  
Tamas Balassa ◽  
Norbert Bara ◽  
Ferenc Kovacs ◽  
Andras Kriston ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Cell Cultures ◽  
Cell Biology ◽  
Single Cell Level ◽  
Cell Level ◽  
Phenotypic Analysis ◽  
Additional Information ◽  
A Cell ◽  
Local Properties

AbstractTo answer major questions of cell biology, it is essential to understand cellular complexity. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the microenvironment of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's microenvironment significantly improves the accuracy of machine learning-based phenotyping.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level

Talanta ◽  
2021 ◽  
Vol 226 ◽  
pp. 122195
Author(s):  
Shuaishuai Yan ◽  
Shuying Wang ◽  
Jingxuan Qiu ◽  
Menghua Li ◽  
Dezhi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Single Cell ◽  
Rapid Detection ◽  
Single Cell Level ◽  
Cell Level ◽  
Food Borne Pathogens ◽  
Food Borne
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