scholarly journals Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Xiangning Chen ◽  
Daniel G. Chen ◽  
Zhongming Zhao ◽  
Justin M. Balko ◽  
Jingchun Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rna Sequencing ◽  
Transcriptome Sequencing ◽  
Weighted Average ◽  
Sequencing Data ◽  
Pixel Intensity ◽  
Image Objects ◽  
Artificial Image ◽  
Highly Correlated

Abstract Background Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients.

scholarly journals Artificial Image Objects for Classification of Breast Cancer Biomarkers with Transcriptome Sequencing Data and Convolutional Neural Network Algorithms

2021 ◽  
Author(s):  
Xiangning Chen ◽  
Daniel G CHEN ◽  
Zhongming Zhao ◽  
Justin M Balko ◽  
Jingchun CHEN
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Convolutional Neural Network ◽  
Rna Sequencing ◽  
High Dimension ◽  
Transcriptome Sequencing ◽  
Weighted Average ◽  
Sequencing Data ◽  
Image Objects ◽  
Artificial Image

Abstract Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively to due to the high dimension of the data and the high correlation of gene expression. Methods: We propose a novel method that transforms RNA sequencing data into artificial image objects (AIOs) and apply convolutional neural network (CNN) algorithm to classify these AIOs. The AIO technique considers each gene as a pixel in digital image, standardizes and rescales gene expression levels into a range suitable for image display. Using the GSE81538 (n = 405) and GSE96058 (n = 3,373) datasets, we create AIOs for the subjects and design CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results: With 5-fold cross validation, we accomplish a classification accuracy and AUC of 0.797 ± 0.034 and 0.820 ± 0.064 for Ki67 status. For NHG, the weighted average of categorical accuracy is 0.726 ± 0.018, and the weighted average of AUC is 0.848 ± 0.019. With GSE81538 as training data and GSE96058 as testing data, the accuracy and AUC for Ki67 are 0.772 ± 0.014 and 0.820 ± 0.006, and that for NHG are 0.682 ± 0.013 and 0.808 ± 0.003 respectively. These results are comparable to or better than the results reported in the original study. For both Ki67 and NHG, the calls from our models have similar predictive power for survival as the calls from trained pathologists in survival analyses. Comparing the calls from our models and the pathologists, we find that the discordant subjects for Ki67 are a group of patients for whom estrogen receptor, progesterone receptor, PAM50 and NHG could not predict their survival rate, and their responses to chemotherapy and endocrine therapy are also different from the concordant subjects. Conclusions: RNA sequencing data can be transformed into AIOs and be used to classify the status of Ki67 and NHG by CNN algorithm. The AIO method can handle high dimension data with highly correlated variables with no requirement for variable selection, leading to a data-driven, consistent and automation-ready approach to model RNA sequencing data.

scholarly journals Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network

Patterns ◽  
2021 ◽  
pp. 100303
Author(s):  
Xiangning Chen ◽  
Daniel G. Chen ◽  
Zhongming Zhao ◽  
Justin Zhan ◽  
Changrong Ji ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Objects ◽  
Artificial Image

scholarly journals RNAseq by Total RNA Library Identifies Additional RNAs Compared to Poly(A) RNA Library

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Quanhu Sheng ◽  
Mingsheng Guo ◽  
Brian Lehmann ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rnas ◽  
Breast Cancer Cell Lines ◽  
Rna Expression ◽  
Library Preparation ◽  
Sequencing Data ◽  
Protein Coding ◽  
Total Rna ◽  
Capture Method ◽  
Highly Correlated

The most popular RNA library used for RNA sequencing is the poly(A) captured RNA library. This library captures RNA based on the presence of poly(A) tails at the 3′ end. Another type of RNA library for RNA sequencing is the total RNA library which differs from the poly(A) library by capture method and price. The total RNA library costs more and its capture of RNA is not dependent on the presence of poly(A) tails. In practice, only ribosomal RNAs and small RNAs are washed out in the total RNA library preparation. To evaluate the ability of detecting RNA for both RNA libraries we designed a study using RNA sequencing data of the same two breast cancer cell lines from both RNA libraries. We found that the RNA expression values captured by both RNA libraries were highly correlated. However, the number of RNAs captured was significantly higher for the total RNA library. Furthermore, we identify several subsets of protein coding RNAs that were not captured efficiently by the poly(A) library. One of the most noticeable is the histone-encode genes, which lack the poly(A) tail.

scholarly journals Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1795 ◽  
Author(s):  
Xiao Lin ◽  
Dalila Sánchez-Escobedo ◽  
Josep R. Casas ◽  
Montse Pardàs
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Input Image ◽  
Estimation Accuracy ◽  
Single Task ◽  
Qualitative And Quantitative ◽  
Highly Correlated

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

scholarly journals Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

2018 ◽  
Vol 8 (2) ◽  
pp. 179 ◽  
Author(s):  
Wijang Widhiarso ◽  
Yohannes Yohannes ◽  
Cendy Prakarsah
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Gray Level ◽  
Classification Result ◽  
Image Objects ◽  
Accuracy Level ◽  
Occurrence Matrix ◽  
Contrast Feature ◽  
Extract Information

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.

scholarly journals Face Detection and Recognition Method Based on Improved Convolutional Neural Network

2021 ◽  
Vol 15 ◽  
pp. 774-781
Author(s):  
Zhengqiu Lu ◽  
Chunliang Zhou ◽  
Xuyang Xuyang ◽  
Weipeng Zhang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Weighted Average ◽  
Recognition Algorithm ◽  
Equal Treatment ◽  
Learning Technology ◽  
Main Research ◽  
Common Face

with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.

Application of Convolutional Neural Networks for Automatic Number Plate Recognition on Complex Background Images

2015 ◽  
Vol 756 ◽  
pp. 695-703 ◽  
Author(s):  
A.A. Druki ◽  
J.A. Bolotova ◽  
V.G. Spitsyn
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
High Speed ◽  
Character Segmentation ◽  
Pixel Intensity ◽  
Intensity Histogram ◽  
Complex Background

The relevance of this study is stipulated by the necessity of designing techniques, algorithms, and programs improving the efficiency of automatic number plate recognition (ANPR) on images with complex backgrounds.Purpose: The aim of this work is to improve the efficiency of automatic number plate recognition on images with complex backgrounds using methods, algorithms, and programs invariant to affine and projective transformations.Design/methodology: Such techniques as artificial intelligence, pattern identification and recognition, the theory of artificial neural networks (ANN), convolutional neural networks (CNN), evolutionary algorithms, mathematical modeling, the probability theory and mathematical statistics were applied via Visual Studio and MatLab software.Findings: The software is developed allowing the automatic number plate recognition on complex background images. The convolutional neural network comprising seven layers is suggested to identify the plate localization, i.e. finding and isolating the plate on the picture. The pixel intensity histogram-based algorithm was used for character segmentation or finding individual characters on the plates. The convolutional neural network comprising six layers is designed to recognize characters. The suggested software system allows automatic number plate recognition at large angles of inclinations and rather a high speed.

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