A stacking ensemble deep learning approach to cancer type classification based on TCGA data

Abstract

Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p = < 0.001, and p = < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p = < 0.001 and p = < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p = < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

“…At the same time, the selection of the distribution will be carried out against the goodness of fit, taking as reference the lowest NlogL. In this same stage, two generalised neural models with deep-learning structures, which had been widely used for pattern identification and classification, were used to validate the model: a stochastic neural model with a stacked deep learning structure (SSDL) [14] and a convolutional deep-learning neural network (CDLM) with stochastic computing [13]. In the validation stage, LG-HDLM was expected to yield probability distributions with extended S-GAPs, and performance indices higher than 75% on average (ELM-750 un.)…”

“…This evaluation identifies the reflectance band (Spectral image) or vegetation index (VI) that allows better identification of MCOPs by LW-affectation. In the first phase within a second stage, the LG-HDLM was evaluated against the reconstruction of the loss structure defined by Scenario 1 and was validated against two generalised deep-learning models commonly used for pattern classification and labelling: a deep-learning model with Stochastic stacked structure (SSDL) [13], and a deep-learning model with convolutional structure (CDLM) [14]. In a second phase within the same stage, the LG-HDLM (Substructure 2) was evaluated against three temporal risk scenarios showing the evolution of LW in a study zone for a period of 6 months before reference Scenario (Scenario 2-month 0, Scenario 2.1-month 1, Scenario 2.2-month 2).…”

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

“…Yuan et al 5 used several machine learning algorithms to study lung adenocarcinoma and lung squamous cell cancer and identified 13 top genes. Mohammed et al 6 used least absolute shrinkage and selection operator (LASSO) as feature selection method to learn cancer type classification based on TCGA data. Chen and Dhahbi 7 applied overlapping feature selection methods for cancer classification and biomarker identification.…”

Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements

“…This may be credited to the availability of knowledgeable individuals with sufficient skills to perform optimal data pre-processing, model selection, tuning, validation, and software deployment. However, many novel AI platforms that are being developed by African institutions affiliates may be employing open-source databases comprised of patients from other populations [ 33 - 37 ]. This alludes to the unavailability of clinical, imaging, pathologic, and molecular data from cancer patients, especially in electronic formats, within many parts of the regions.…”