A Cascade Flexible Neural Forest Model for Cancer Subtypes Classification on Gene Expression Data

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

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A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

BMC Bioinformatics ◽

10.1186/s12859-021-04391-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lianxin Zhong ◽

Qingfang Meng ◽

Yuehui Chen ◽

Lei Du ◽

Peng Wu

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Small Sample Size ◽

Small Sample ◽

Expression Data ◽

Cancer Subtypes ◽

Cancer Pathogenesis ◽

Depth Study ◽

And Function

Abstract Background Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. Results In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. Conclusion The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

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COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006892 ◽

2008 ◽

Vol 22 (08) ◽

pp. 1587-1598 ◽

Cited By ~ 3

Author(s):

WEIXIANG LIU ◽

KEHONG YUAN ◽

JIAN WU ◽

DATIAN YE ◽

ZHEN JI ◽

...

Keyword(s):

Gene Expression ◽

Mixture Model ◽

Gene Expression Data ◽

Small Sample Size ◽

Data Classification ◽

Small Sample ◽

Training Data ◽

Microarray Data Analysis ◽

Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

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A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

BMC Bioinformatics ◽

10.1186/s12859-019-3116-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 6

Author(s):

Jing Xu ◽

Peng Wu ◽

Yuehui Chen ◽

Qingfang Meng ◽

Hussain Dawood ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

High Throughput Sequencing ◽

Omics Data ◽

Expression Data ◽

Cancer Subtypes ◽

Stacked Autoencoder ◽

Subtype Classification ◽

Sequencing Technologies ◽

Cancer Subtype

Abstract Background Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification. Results A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. Conclusion The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

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An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220124756 ◽

2019 ◽

Vol 21 (9) ◽

pp. 631-645 ◽

Cited By ~ 5

Author(s):

Saeed Ahmed ◽

Muhammad Kabir ◽

Zakir Ali ◽

Muhammad Arif ◽

Farman Ali ◽

...

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Expression Data ◽

Classification Accuracy ◽

Early Stage ◽

Small Sample Size ◽

Feature Selection Method ◽

Small Sample ◽

Expression Data ◽

Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

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