scholarly journals Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

2018 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Keivan Maghooli ◽  
NiloofarYousefi Moteghaed ◽  
Masoud Garshasbi
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Expression Profiles ◽  
Optimization Algorithms ◽  
Gene Expression Profiles ◽  
Hybrid Optimization ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Hybrid Optimization Algorithms

CLASSIFICATION OF MULTIPLE CANCER TYPES USING FUZZY SUPPORT VECTOR MACHINES AND OUTLIER DETECTION METHODS

2005 ◽  
Vol 17 (06) ◽  
pp. 300-308 ◽  
Author(s):  
LI-YEH CHUANG ◽  
CHENG-HONG YANG ◽  
LI-CHENG JIN
Keyword(s):  
Support Vector Machine ◽  
Outlier Detection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Detection Methods ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Multiple Cancer ◽  
Flexible Architecture ◽  
Cancer Types

The support vector machine (SVM) is a new learning method and has shown comparable or better results than the neural networks on some applications. In this paper, we applied SVM to classify multiple cancer types by gene expression profiles and exploit some strategies of the SVM method, including fuzzy logic and statistical theories. Using the proposed strategies and outlier detection methods, the FSVM (fuzzy support vector machine) can achieve a comparable or better performance than other methods, and provide a more flexible architecture to discriminate against SRBCT and non-SRBCT samples.

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

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