Kernel-Based Self-Organized Maps Trained with Supervised Bias bor Gene Expression Data Mining

2005 ◽  
pp. 1777-1793
Author(s):  
Stergios Papadimitriou
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Gene Expression Data ◽  
Expression Data ◽  
Self Organized

scholarly journals Classification of Kidney Cancer Data Using Cost-Sensitive Hybrid Deep Learning Approach

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 154 ◽  
Author(s):  
Ho Sun Shon ◽  
Erdenebileg Batbaatar ◽  
Kyoung Ok Kim ◽  
Eun Jong Cha ◽  
Kyung-Ah Kim
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Gene Expression Data ◽  
Genomic Data ◽  
Classification Model ◽  
Expression Data ◽  
Cancer Data ◽  
Prognosis Prediction

Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis.

On Extraction of Cancer Informative Genes and Gene Expression Data Mining

2016 ◽  
Vol 10 (4) ◽  
pp. 293-299 ◽  
Author(s):  
Bin Yu ◽  
Qiran Wang ◽  
Xuemin Wang ◽  
Shan Li ◽  
Lifeng Lou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Gene Expression Data ◽  
Expression Data

Identification of novel human glioblastoma-specific transcripts by serial analysis of gene expression data mining

2013 ◽  
Vol 13 (5) ◽  
pp. 367-375 ◽  
Author(s):  
Yanlin Su ◽  
Jie Xiong ◽  
Zhitong Bing ◽  
Xiaomin Zeng ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Gene Expression Data ◽  
Expression Data ◽  
Human Glioblastoma

The Clustering Algorithm Study of Gene Expression Data

2011 ◽  
Vol 183-185 ◽  
pp. 93-98
Author(s):  
Rui He ◽  
Chun Mei Lin
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Undirected Graph ◽  
Similarity Measurement ◽  
Expression Data ◽  
Clustering Method ◽  
Self Organized

This paper proposes an evolutionary self-organized clustering method of genes based on undirected graph expression. In this method, we use the vertices of the graph to represent genes, and regard the weight between two vertices as similarity measurement of two genes. Thus, the similarities among genes can be extracted according to the space feature of graph with immune evolutionary method. To demonstrate the effectiveness of the proposed method, the method is tested on yeast cell cycle expression dataset; the results suggest that this method is capable of clustering genes.

scholarly journals Pattern Discovery in Gene Expression Data

2009 ◽  
pp. 45-64
Author(s):  
Gráinne Kerr ◽  
Heather Ruskin ◽  
Martin Crane
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Cluster Analysis ◽  
Gene Regulation ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Mrna Levels ◽  
Expression Data ◽  
Single Experiment ◽  
Gene Regulation Networks

Microarray technology1 provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The enormous amount of data produced by this high throughput approach presents a challenge for data analysis: to extract meaningful patterns, to evaluate its quality, and to interpret the results. The most commonly used method of identifying such patterns is cluster analysis. Common and sufficient approaches to many data-mining problems, for example, Hierarchical, K-means, do not address well the properties of “typical” gene expression data and fail, in significant ways, to account for its profile. This chapter clarifies some of the issues and provides a framework to evaluate clustering in gene expression analysis. Methods are categorised explicitly in the context of application to data of this type, providing a basis for reverse engineering of gene regulation networks. Finally, areas for possible future development are highlighted.

scholarly journals Text Mining Perspectives in Microarray Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Jeyakumar Natarajan
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Text Mining ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Machine Learning Algorithms ◽  
Microarray Data Analysis ◽  
Expression Data ◽  
Related Data ◽  
Mining Methods

Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.

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