Ensemble of Deep Learning Approach for the Feature Selection from High-Dimensional Microarray Data

2022 ◽  
pp. 591-600
Author(s):  
Nabendu Bhui
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Microarray Data ◽  
High Dimensional ◽  
Learning Approach

Feature Selection Algorithms for Mining High Dimensional DNA Microarray Data

2011 ◽  
pp. 685-710 ◽  
Author(s):  
David J. Dittman ◽  
Taghi M. Khoshgoftaar ◽  
Randall Wald ◽  
Jason Van Hulse
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Microarray Data ◽  
High Dimensional ◽  
Dna Microarray Data ◽  
Selection Algorithms

scholarly journals Deep learning for predicting disease status using genomic data

2018 ◽  
Author(s):  
Qianfan Wu ◽  
Adel Boueiz ◽  
Alican Bozkurt ◽  
Arya Masoomi ◽  
Allan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Genomic Data ◽  
Disease Status ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Learning Approach ◽  
Low Dimensional

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature review. All four articles used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. This deep learning approach outperformed existing prediction approaches, such as prediction based on probe-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

scholarly journals Feature Selection for high Dimensional DNA Microarray data using hybrid approaches

2013 ◽  
Vol 9 (16) ◽  
pp. 824-828 ◽  
Author(s):  
Ammu Prasanna Kumar ◽  
◽  
Preeja Valsala
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Microarray Data ◽  
High Dimensional ◽  
Dna Microarray Data ◽  
Hybrid Approaches ◽  
Selection For

Novel machine learning approach for classification of high-dimensional microarray data

2019 ◽  
Vol 23 (24) ◽  
pp. 13409-13421 ◽  
Author(s):  
Rabia Aziz Musheer ◽  
C. K. Verma ◽  
Namita Srivastava
Keyword(s):  
Machine Learning ◽  
Microarray Data ◽  
High Dimensional ◽  
Learning Approach ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document