Employing Machine Learning Techniques for Data Enrichment: Increasing the Number of Samples for Effective Gene Expression Data Analysis

2011 ◽  
Author(s):  
Utku Erdogdu ◽  
Mehmet Tan ◽  
Reda Alhajj ◽  
Faruk Polat ◽  
Douglas Demetrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Machine Learning Techniques ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Data Enrichment ◽  
Learning Techniques

scholarly journals Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Yining Xu ◽  
Xinran Cui ◽  
Yadong Wang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Real World ◽  
Tumor Metastasis ◽  
Machine Learning Techniques ◽  
Expression Data ◽  
Real World Data ◽  
Convolutional Network ◽  
Learning Techniques

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop a patient’s therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model’s performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).

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