Corpus ID: 56517070

A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network

@article{Chen2018AMT,
  title={A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network},
  author={Xi Chen and J. Xie and Qingcong Yuan},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.08674}
}
With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensionality and complexity of the data. Here we present models of deep learning (DL) and apply them to gene expression data for the diagnosis and categorization of cancer. In this… Expand

References

SHOWING 1-10 OF 30 REFERENCES
Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data
This research explores machine learning methods for the development of computer models that use gene expression data to distinguish between tumor and non-tumor, between metastatic and non-metastatic,Expand
Multi-class cancer classification via partial least squares with gene expression profiles
TLDR
This paper presents the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes and evaluated the classification algorithms and the variability of the error rates using simulations based on randomization of the real data sets. Expand
Using deep learning to enhance cancer diagnosis and classication
Using automated computer tools and in particular machine learning to facilitate and enhance medical analysis and diagnosis is a promising and important area. In this paper, we show that howExpand
Gene selection from microarray data for cancer classification - a machine learning approach
TLDR
It is shown that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets and it is demonstrated that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. Expand
Multiclass cancer diagnosis using tumor gene expression signatures
  • S. Ramaswamy, P. Tamayo, +12 authors T. Golub
  • Medicine, Biology
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2001
TLDR
The results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics. Expand
Combining multiple approaches for gene microarray classification
TLDR
A method for gene microarray classification that combines different feature reduction approaches for improving classification performance is proposed and results show the goodness of the proposed approach with respect to the state of the art. Expand
Pan-Cancer Epigenetic Biomarker Selection from Blood Samples Using SAS
TLDR
It is reported that 55 methylation CpG sites measurable in blood samples can be used as biomarkers for early cancer detection and classification. Expand
A Top-r Feature Selection Algorithm for Microarray Gene Expression Data
TLDR
The proposed algorithm first divides genes into subsets, the sizes of which are relatively small, then selects informative smaller subsets of genes from a subset and merges the chosen genes with another gene subset to update the gene subset. Expand
Using deep learning to enhance head and neck cancer diagnosis and classification
TLDR
This paper mainly focuses on classifier Deep learning framework in h2o that gives better accuracy in head and neck cancer detection and obtains the satisfactory results with 98.8% accuracy. Expand
Distinctive gene expression patterns in human mammary epithelial cells and breast cancers.
  • C. Perou, S. Jeffrey, +11 authors D. Botstein
  • Biology, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 1999
TLDR
The results support the feasibility and usefulness of this systematic approach to studying variation in gene expression patterns in human cancers as a means to dissect and classify solid tumors. Expand
...
1
2
3
...