Learning structure in gene expression data using deep architectures, with an application to gene clustering

By Aman Gupta, Haohan Wang, Madhavi K. Ganapathiraju. 2015

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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. Eric P. Xing
2. Haohan Wang
3. Brett K. Beaulieu-Jones
4. Yifan Peng
5. Anshul B Kundaje
6. Avanti Shrikumar
7. Casey S. Greene
8. Amr M. Alexandari

In Molecular Biology

1. Casey S. Greene
2. Yifan Peng
3. Brett K. Beaulieu-Jones
4. G. Way
5. Amr M. Alexandari
6. Zhiyong Lu
7. Avanti Shrikumar
8. S. Joshua Swamidass

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Denoising autoencoders have been used to cluster yeast expression microarrays into known modules representing cell cycle processes [142] and to stratify yeast strains based on chemical and mutational perturbations [143].”
• On the Origin of Deep Learning (2017, background): “and tasks in additional to traditional tasks like object detection, face recognition, or language models, for example, Osako et al. (2015) uses the recurrent neural network to denoise speech signals, Gupta et al. (2015) uses stacked autoencoders to discover clustering patterns of gene expressions. G”
• A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION (2017, unknown): “24 In contrast, other approaches such as kernel PCA (KPCA) may be capable of uncovering these nonlinear relationships.”
• Using neural networks for reducing the dimensions of single-cell RNA-Seq data (2017, background): “(19), use DAEs to analyze time series bulk gene expression data.”