scholarly journals Multiclass cancer classification based on gene expression comparison

2014 ◽  
Vol 0 (0) ◽  
Author(s):  
Sitan Yang ◽  
Daniel Q. Naiman
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Classification Problems ◽  
New Approach ◽  
Class Discrimination ◽  
Disease States ◽  
Genomic Analyses ◽  
Multiclass Classification Problems

AbstractAs the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an

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.

Hidden Markov models for cancer classification using gene expression profiles

2015 ◽  
Vol 316 ◽  
pp. 293-307 ◽  
Author(s):  
Thanh Nguyen ◽  
Abbas Khosravi ◽  
Douglas Creighton ◽  
Saeid Nahavandi
Keyword(s):  
Gene Expression ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification

scholarly journals A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT

BMC Genomics ◽  
2010 ◽  
Vol 11 (Suppl 3) ◽  
pp. S4 ◽  
Author(s):  
Sudhir Chowbina ◽  
Youping Deng ◽  
Junmei Ai ◽  
Xiaogang Wu ◽  
Xin Guan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rat Liver ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
New Approach ◽  
Responsive Gene
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