A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION

By Padideh Danaee, Reza Ghaeini, David A. Hendrix. Pacific Symposium on Biocomputing, 2017

295 citations recorded by the source corpus.

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7fddac954ad0a6d9c85b468f87b789abf7ee5f6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mykola Pechenizkiy
2. Shiwei Liu
3. T. Mcginnity
4. Yulong Pei
5. Weiqi Luo
6. Amir Hussain
7. Mufti Mahmud
8. Jian Weng

In Molecular Biology

1. T. Mcginnity
2. Amir Hussain
3. Mufti Mahmud
4. M. S. Kaiser

Citations in the corpus

• Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “Probabilistic DL methods capable of quantifying prediction uncertainty, such as Bayesian DL [161], are great candidates to address these issues and have recently started to be applied in cancer diagnosis tasks [162 – 164].”
• Deep Learning in Mining Biological Data (2020, background): “to extract features for cancer diagnosis and classification along with the identification of the related genes from GE data [53].”
• Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware (2019, methodology): “Additionally, a Stacked Denoising Autoencoder (SDAE) was employed to represent high-dimensional features with lower dimensional and more meaningful features (Danaee et al., 2017).”
• DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (2019, background): “, speech recognition [1], image recognition [2], drug discovery [3] and gene analysis for cancer research [4], [5].”
• Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[40] SDAE Various medical images Cancer detection from gene data; the study was successful as this method managed to extract genes that are helpful when it comes to cancer prediction.”